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Billah, Mustain, Adnan Anwar, Ziaur Rahman, and Syed Md Galib. "Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes." Energies 14, no. 13 (June 28, 2021): 3887. http://dx.doi.org/10.3390/en14133887.
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Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques.
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Chen, Jian, Xuxin Zhang, Rui Zhang, Chen Wang, and Ling Liu. "De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks." IEEE Transactions on Information Forensics and Security 16 (2021): 3412–25. http://dx.doi.org/10.1109/tifs.2021.3080522.
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Saha, Aniruddha, Akshayvarun Subramanya, and Hamed Pirsiavash. "Hidden Trigger Backdoor Attacks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11957–65. http://dx.doi.org/10.1609/aaai.v34i07.6871.
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With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topic. Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time. Most state-of-the-art backdoor attacks either provide mislabeled poisoning data that is possible to identify by visual inspection, reveal the trigger in the poisoned data, or use noise to hide the trigger. We propose a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time. We perform an extensive study on various image classification settings and show that our attack can fool the model by pasting the trigger at random locations on unseen images although the model performs well on clean data. We also show that our proposed attack cannot be easily defended using a state-of-the-art defense algorithm for backdoor attacks.
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Dunn, Corey, Nour Moustafa, and Benjamin Turnbull. "Robustness Evaluations of Sustainable Machine Learning Models against Data Poisoning Attacks in the Internet of Things." Sustainability 12, no. 16 (August 10, 2020): 6434. http://dx.doi.org/10.3390/su12166434.
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With the increasing popularity of the Internet of Things (IoT) platforms, the cyber security of these platforms is a highly active area of research. One key technology underpinning smart IoT systems is machine learning, which classifies and predicts events from large-scale data in IoT networks. Machine learning is susceptible to cyber attacks, particularly data poisoning attacks that inject false data when training machine learning models. Data poisoning attacks degrade the performances of machine learning models. It is an ongoing research challenge to develop trustworthy machine learning models resilient and sustainable against data poisoning attacks in IoT networks. We studied the effects of data poisoning attacks on machine learning models, including the gradient boosting machine, random forest, naive Bayes, and feed-forward deep learning, to determine the levels to which the models should be trusted and said to be reliable in real-world IoT settings. In the training phase, a label modification function is developed to manipulate legitimate input classes. The function is employed at data poisoning rates of 5%, 10%, 20%, and 30% that allow the comparison of the poisoned models and display their performance degradations. The machine learning models have been evaluated using the ToN_IoT and UNSW NB-15 datasets, as they include a wide variety of recent legitimate and attack vectors. The experimental results revealed that the models’ performances will be degraded, in terms of accuracy and detection rates, if the number of the trained normal observations is not significantly larger than the poisoned data. At the rate of data poisoning of 30% or greater on input data, machine learning performances are significantly degraded.
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Weerasinghe, Sandamal, Tansu Alpcan, Sarah M. Erfani, and Christopher Leckie. "Defending Support Vector Machines Against Data Poisoning Attacks." IEEE Transactions on Information Forensics and Security 16 (2021): 2566–78. http://dx.doi.org/10.1109/tifs.2021.3058771.
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Kalajdzic, Kenan, Ahmed Patel, and Mona Taghavi. "Two Methods for Active Detection and Prevention of Sophisticated ARP-Poisoning Man-in-the-Middle Attacks on Switched Ethernet LANs." International Journal of Digital Crime and Forensics 3, no. 3 (July 2011): 50–60. http://dx.doi.org/10.4018/jdcf.2011070104.
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This paper describes two novel methods for active detection and prevention of ARP-poisoning-based Man-in-the-Middle (MitM) attacks on switched Ethernet LANs. As a stateless and inherently insecure protocol, ARP has been used as a relatively simple means to launch Denial-of-Service (DoS) and MitM attacks on local networks and multiple solutions have been proposed to detect and prevent these types of attacks. MitM attacks are particularly dangerous, because they allow an attacker to monitor network traffic and break the integrity of data being sent over the network. The authors introduce backwards compatible techniques to prevent ARP poisoning and deal with sophisticated stealth MitM programs.
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Alsuwat, Emad, Hatim Alsuwat, Marco Valtorta, and Csilla Farkas. "Adversarial data poisoning attacks against the PC learning algorithm." International Journal of General Systems 49, no. 1 (June 17, 2019): 3–31. http://dx.doi.org/10.1080/03081079.2019.1630401.
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Prabadevi, B., and N. Jeyanthi. "TSCBA-A Mitigation System for ARP Cache Poisoning Attacks." Cybernetics and Information Technologies 18, no. 4 (November 1, 2018): 75–93. http://dx.doi.org/10.2478/cait-2018-0049.
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Abstract Address Resolution Protocol (ARP) cache poisoning results in numerous attacks. A novel mitigation system for ARP cache poisoning presented here avoids ARP cache poisoning attacks by introducing timestamps and counters in the ARP messages and ARP data tables. The system is evaluated based on criteria specified by the researchers and abnormal packets.
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Zhou, Xingchen, Ming Xu, Yiming Wu, and Ning Zheng. "Deep Model Poisoning Attack on Federated Learning." Future Internet 13, no. 3 (March 14, 2021): 73. http://dx.doi.org/10.3390/fi13030073.
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Federated learning is a novel distributed learning framework, which enables thousands of participants to collaboratively construct a deep learning model. In order to protect confidentiality of the training data, the shared information between server and participants are only limited to model parameters. However, this setting is vulnerable to model poisoning attack, since the participants have permission to modify the model parameters. In this paper, we perform systematic investigation for such threats in federated learning and propose a novel optimization-based model poisoning attack. Different from existing methods, we primarily focus on the effectiveness, persistence and stealth of attacks. Numerical experiments demonstrate that the proposed method can not only achieve high attack success rate, but it is also stealthy enough to bypass two existing defense methods.
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Aydin, Burc. "Global Characteristics of Chemical, Biological, and Radiological Poison Use in Terrorist Attacks." Prehospital and Disaster Medicine 35, no. 3 (April 2, 2020): 260–66. http://dx.doi.org/10.1017/s1049023x20000394.
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AbstractBackground:Chemical, biological, and radiological (CBR) terrorism continues to be a global threat. Studies examining global and historical toxicological characteristics of CBR terrorism are lacking.Methods:Global Terrorism Database (GTD) and RAND Database of Worldwide Terrorism Incidents (RDWTI) were searched for CBR terrorist attacks from 1970 through 2017. Events fulfilling terrorism and poisoning definitions were included. Variables of event date and location, event realization, poisonous agent type, poisoning agent, exposure route, targets, connected events, additional means of harm, disguise methods, poisonings, and casualties were analyzed along with time trends and data gaps.Results:A total of 446 events of CBR terrorism were included from all world regions. A trend for increased number of events over time was observed (R2 = 0.727; coefficient = 0.511). In these attacks, 4,093 people lost their lives and 31,903 were injured. Chemicals were the most commonly used type of poison (63.5%). The most commonly used poisonous agents were acids (12.3%), chlorine or chlorine compounds (11.2%), riot control agents (10.8%), cyanides (5.8%), and Bacillus anthracis (4.9%). Occurrence of poisoning was confirmed in 208 events (46.6%). Most common exposure routes were skin, mucosa, or eye (57.2%) and inhalation (47.5%). Poison was delivered with additional means of harm in 151 events (33.9%) and in a disguised way in 214 events (48.0%), respectively.Conclusions:This study showed that CBR terrorism is an on-going and increasingly recorded global threat involving diverse groups of poisons with additional harmful mechanisms and disguise. Industrial chemicals were used in chemical attacks. Vigilance and preparedness are needed for future CBR threats.
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Taheri, Rahim, Reza Javidan, Mohammad Shojafar, Zahra Pooranian, Ali Miri, and Mauro Conti. "On defending against label flipping attacks on malware detection systems." Neural Computing and Applications 32, no. 18 (July 28, 2020): 14781–800. http://dx.doi.org/10.1007/s00521-020-04831-9.
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Abstract Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature.
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Prabadevi, B., and N. Jeyanthi. "Security Solution for ARP Cache Poisoning Attacks in Large Data Centre Networks." Cybernetics and Information Technologies 17, no. 4 (November 27, 2017): 69–86. http://dx.doi.org/10.1515/cait-2017-0042.
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AbstractThe bridge protocol (Address Resolution Protocol) ARP, integrating Ethernet (Layer 2) and IP protocol (Layer 3) plays a vital role in TCP/IP communication since ARP packet is the first packet generated during any TCP/IP communications and they are the first traffic from the host. In the large data center, as the size of the broadcast domain (i.e., number of hosts on the network) increases consequently the broadcast traffic from the communication protocols like ARP also increases. This paper addresses the problem faced by Layer 2 protocols like insecured communication, scalability issues and VM migration issues. The proposed system addresses these issues by introducing two new types of messaging with traditional ARP and also combat the ARP Cache poisoning attacks like host impersonation, MITM, Distributed DoS by making ARP stateful. The components of the proposed methodology first start the process by decoding the packets, updates the invalid entry made by the user with Timestamp feature and messages being introduced. The system has been implemented and compared with various existing solutions.
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Chiba, Tomoki, Yuichi Sei, Yasuyuki Tahara, and Akihiko Ohsuga. "A Countermeasure Method Using Poisonous Data Against Poisoning Attacks on IoT Machine Learning." International Journal of Semantic Computing 15, no. 02 (June 2021): 215–40. http://dx.doi.org/10.1142/s1793351x21400043.
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In the modern world, several areas of our lives can be improved, in the form of diverse additional dimensions, in terms of quality, by machine learning. When building machine learning models, open data are often used. Although this trend is on the rise, the monetary losses since the attacks on machine learning models are also rising. Preparation is, thus, believed to be indispensable in terms of embarking upon machine learning. In this field of endeavor, machine learning models may be compromised in various ways, including poisoning attacks. Assaults of this nature involve the incorporation of injurious data into the training data rendering the models to be substantively less accurate. The circumstances of every individual case will determine the degree to which the impairment due to such intrusions can lead to extensive disruption. A modus operandi is proffered in this research as a safeguard for machine learning models in the face of the poisoning menace, envisaging a milieu in which machine learning models make use of data that emanate from numerous sources. The information in question will be presented as training data, and the diversity of sources will constitute a barrier to poisoning attacks in such circumstances. Every source is evaluated separately, with the weight of each data component assessed in terms of its ability to affect the precision of the machine learning model. An appraisal is also conducted on the basis of the theoretical effect of the use of corrupt data as from each source. The extent to which the subgroup of data in question can undermine overall accuracy depends on the estimated data removal rate associated with each of the sources described above. The exclusion of such isolated data based on this figure ensures that the standard data will not be tainted. To evaluate the efficacy of our suggested preventive measure, we evaluated it in comparison with the well-known standard techniques to assess the degree to which the model was providing accurate conclusions in the wake of the change. It was demonstrated during this test that when the innovative mode of appraisal was applied, in circumstances in which 17% of the training data are corrupt, the degree of precision offered by the model is 89%, in contrast to the figure of 83% acquired through the traditional technique. The corrective technique suggested by us thus boosted the resilience of the model against harmful intrusion.
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Sabir, Bushra, Faheem Ullah, M. Ali Babar, and Raj Gaire. "Machine Learning for Detecting Data Exfiltration." ACM Computing Surveys 54, no. 3 (June 2021): 1–47. http://dx.doi.org/10.1145/3442181.
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Context : Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks. It is important to systematically review and synthesize the ML-based data exfiltration countermeasures for building a body of knowledge on this important topic. Objective : This article aims at systematically reviewing ML-based data exfiltration countermeasures to identify and classify ML approaches, feature engineering techniques, evaluation datasets, and performance metrics used for these countermeasures. This review also aims at identifying gaps in research on ML-based data exfiltration countermeasures. Method : We used Systematic Literature Review (SLR) method to select and review 92 papers. Results : The review has enabled us to: (a) classify the ML approaches used in the countermeasures into data-driven, and behavior-driven approaches; (b) categorize features into six types: behavioral, content-based, statistical, syntactical, spatial, and temporal; (c) classify the evaluation datasets into simulated, synthesized, and real datasets; and (d) identify 11 performance measures used by these studies. Conclusion : We conclude that: (i) The integration of data-driven and behavior-driven approaches should be explored; (ii) There is a need of developing high quality and large size evaluation datasets; (iii) Incremental ML model training should be incorporated in countermeasures; (iv) Resilience to adversarial learning should be considered and explored during the development of countermeasures to avoid poisoning attacks; and (v) The use of automated feature engineering should be encouraged for efficiently detecting data exfiltration attacks.
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Ulva, Fadillah, Nurul Prihastita Rizyana, and Afzahul Rahmi. "Hubungan Tingkat Pengetahuan Dengan Gejala Keracunan Pestisida pada Petani Penyemprot Pestisida Tanaman Holtikultura di Kecamatan Lembah Gumanti Kabupaten Solok Tahun 2019." Jurnal Ilmiah Universitas Batanghari Jambi 19, no. 3 (October 15, 2019): 501. http://dx.doi.org/10.33087/jiubj.v19i3.696.
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The World Health Organization (WHO) and The United Nations Environment Program (UNEP) predict there are 1.5 million cases of pesticide poisoning that occur in the agricultural sector, most of which occur in developing countries. Pesticides are hazardous and toxic materials (B3) that must be managed properly. Farmers get many benefits from using pesticides. Pesticides are used by farmers to control pest attacks. Improper using of pesticides can endanger farmers. Horticulture plants need pesticides to control pest attacks. There are various factors that can affect the level of pesticide poisoning in farmers, one of them is the level of knowledge. Lack of knowledge about pesticides will increase the risk of pesticide poisoning. The purpose of this study was to determine the relationship between the level of knowledge with symptoms of pesticide poisoning in horticultural farmers in Lembah Gumanti, Solok Regency in 2019. The type of this research was observational analytic research with cross sectional design. The population in this study were all horticultural farmers in the Gumanti Lembah Subdistrict of Solok Regency in 2018 totaling 128 people with a sample of 56 people. The sample is done by proportional random sampling technique. Data analysis was carried out by univariate and bivariate. The results showed that 41.1% of respondents had symptoms of risky poisoning, 46.4% of respondents knowledge was still low. Based on statistical tests it is known that there is a significant relationship between the level of knowledge with symptoms of pesticide poisoning. It can be concluded that the level of knowledge will affect the incidence of pesticide poisoning in horticultural farmers. It can be suggested to farmers to increase knowledge regularly.
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Singh, Vishwa Pratap, and R. L. Ujjwal. "Threat identification and risk assessments for named data networking architecture using SecRam." International Journal of Knowledge-based and Intelligent Engineering Systems 25, no. 1 (April 9, 2021): 33–47. http://dx.doi.org/10.3233/kes-210051.
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Named Data networking is an instance of information centric networking, aims to improve the performance of the Internet by using in-network caching at storage-enabled routers and provide name based content access. However, name based content access and in-network caching make Name Data network vulnerable to new security attacks like cache pollutions, cache poisoning, false locality, cache snooping and interest flooding, etc. In this paper, we have evaluated NDN security principles, the impact of threats, ratified various security enablers, and built-in mitigation actions to combat security attacks. We have systematically applied SecRam in NDN for statistical security risk assessment, identification of run time threats, and assessment of available methods to mitigate these threats, as SecRam considers operational focus areas and proved useful for identification and severity assessment of run time threats. We have modified SecRam and used it in an entirely different domain, i.e., to a computer network, as SESAR proposed SecRam specifically for ATM systems that cannot be directly applied to another context. According to the best of our knowledge, it is the first attempt for a complete risk assessment of NDN. We have concluded this paper by defining a set of open security challenges that should be considered by future researchers.
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Duan, Jinhuan, Xianxian Li, Shiqi Gao, Zili Zhong, and Jinyan Wang. "SSGD: A Safe and Efficient Method of Gradient Descent." Security and Communication Networks 2021 (August 5, 2021): 1–11. http://dx.doi.org/10.1155/2021/5404061.
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With the vigorous development of artificial intelligence technology, various engineering technology applications have been implemented one after another. The gradient descent method plays an important role in solving various optimization problems, due to its simple structure, good stability, and easy implementation. However, in multinode machine learning system, the gradients usually need to be shared, which will cause privacy leakage, because attackers can infer training data with the gradient information. In this paper, to prevent gradient leakage while keeping the accuracy of the model, we propose the super stochastic gradient descent approach to update parameters by concealing the modulus length of gradient vectors and converting it or them into a unit vector. Furthermore, we analyze the security of super stochastic gradient descent approach and demonstrate that our algorithm can defend against the attacks on the gradient. Experiment results show that our approach is obviously superior to prevalent gradient descent approaches in terms of accuracy, robustness, and adaptability to large-scale batches. Interestingly, our algorithm can also resist model poisoning attacks to a certain extent.
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Xuan, Shichang, Ming Jin, Xin Li, Zhaoyuan Yao, Wu Yang, and Dapeng Man. "DAM-SE: A Blockchain-Based Optimized Solution for the Counterattacks in the Internet of Federated Learning Systems." Security and Communication Networks 2021 (July 1, 2021): 1–14. http://dx.doi.org/10.1155/2021/9965157.
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The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the “free-rider attacks” and “model poisoning attacks” in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.
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Mahloujifar, Saeed, Dimitrios I. Diochnos, and Mohammad Mahmoody. "The Curse of Concentration in Robust Learning: Evasion and Poisoning Attacks from Concentration of Measure." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4536–43. http://dx.doi.org/10.1609/aaai.v33i01.33014536.
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Many modern machine learning classifiers are shown to be vulnerable to adversarial perturbations of the instances. Despite a massive amount of work focusing on making classifiers robust, the task seems quite challenging. In this work, through a theoretical study, we investigate the adversarial risk and robustness of classifiers and draw a connection to the well-known phenomenon of “concentration of measure” in metric measure spaces. We show that if the metric probability space of the test instance is concentrated, any classifier with some initial constant error is inherently vulnerable to adversarial perturbations.One class of concentrated metric probability spaces are the so-called Lévy families that include many natural distributions. In this special case, our attacks only need to perturb the test instance by at most O(√n) to make it misclassified, where n is the data dimension. Using our general result about Lévy instance spaces, we first recover as special case some of the previously proved results about the existence of adversarial examples. However, many more Lévy families are known (e.g., product distribution under the Hamming distance) for which we immediately obtain new attacks that find adversarial examples of distance O(√n).Finally, we show that concentration of measure for product spaces implies the existence of forms of “poisoning” attacks in which the adversary tampers with the training data with the goal of degrading the classifier. In particular, we show that for any learning algorithm that uses m training examples, there is an adversary who can increase the probability of any “bad property” (e.g., failing on a particular test instance) that initially happens with non-negligible probability to ≈ 1 by substituting only Õe(√m) of the examples with other (still correctly labeled) examples.
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Xiang, Zhen, David J. Miller, Hang Wang, and George Kesidis. "Detecting Scene-Plausible Perceptible Backdoors in Trained DNNs Without Access to the Training Set." Neural Computation 33, no. 5 (April 13, 2021): 1329–71. http://dx.doi.org/10.1162/neco_a_01376.
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Abstract Backdoor data poisoning attacks add mislabeled examples to the training set, with an embedded backdoor pattern, so that the classifier learns to classify to a target class whenever the backdoor pattern is present in a test sample. Here, we address posttraining detection of scene-plausible perceptible backdoors, a type of backdoor attack that can be relatively easily fashioned, particularly against DNN image classifiers. A post-training defender does not have access to the potentially poisoned training set, only to the trained classifier, as well as some unpoisoned examples that need not be training samples. Without the poisoned training set, the only information about a backdoor pattern is encoded in the DNN's trained weights. This detection scenario is of great import considering legacy and proprietary systems, cell phone apps, as well as training outsourcing, where the user of the classifier will not have access to the entire training set. We identify two important properties of scene-plausible perceptible backdoor patterns, spatial invariance and robustness, based on which we propose a novel detector using the maximum achievable misclassification fraction (MAMF) statistic. We detect whether the trained DNN has been backdoor-attacked and infer the source and target classes. Our detector outperforms existing detectors and, coupled with an imperceptible backdoor detector, helps achieve posttraining detection of most evasive backdoors of interest.
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Rathore, Shailendra, Yi Pan, and Jong Hyuk Park. "BlockDeepNet: A Blockchain-Based Secure Deep Learning for IoT Network." Sustainability 11, no. 14 (July 22, 2019): 3974. http://dx.doi.org/10.3390/su11143974.
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The recent development in IoT and 5G translates into a significant growth of Big data in 5G—envisioned industrial automation. To support big data analysis, Deep Learning (DL) has been considered the most promising approach in recent years. Note, however, that designing an effective DL paradigm for IoT has certain challenges such as single point of failure, privacy leak of IoT devices, lack of valuable data for DL, and data poisoning attacks. To this end, we present BlockDeepNet, a Blockchain-based secure DL that combines DL and blockchain to support secure collaborative DL in IoT. In BlockDeepNet, collaborative DL is performed at the device level to overcome privacy leak and obtain enough data for DL, whereas blockchain is employed to ensure the confidentiality and integrity of collaborative DL in IoT. The experimental evaluation shows that BlockDeepNet can achieve higher accuracy for DL with acceptable latency and computational overhead of blockchain operation.
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Kasyap, Harsh, and Somanath Tripathy. "Privacy-preserving Decentralized Learning Framework for Healthcare System." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 2s (June 10, 2021): 1–24. http://dx.doi.org/10.1145/3426474.
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Clinical trials and drug discovery would not be effective without the collaboration of institutions. Earlier, it has been at the cost of individual’s privacy. Several pacts and compliances have been enforced to avoid data breaches. The existing schemes collect the participant’s data to a central repository for learning predictions as the collaboration is indispensable for research advances. The current COVID pandemic has put a question mark on our existing setup where the existing data repository has proved to be obsolete. There is a need for contemporary data collection, processing, and learning. The smartphones and devices held by the last person of the society have also made them a potential contributor. It demands to design a distributed and decentralized Collaborative Learning system that would make the knowledge inference from every data point. Federated Learning [21], proposed by Google, brings the concept of in-place model training by keeping the data intact to the device. Though it is privacy-preserving in nature, however, it is susceptible to inference, poisoning, and Sybil attacks. Blockchain is a decentralized programming paradigm that provides a broader control of the system, making it attack resistant. It poses challenges of high computing power, storage, and latency. These emerging technologies can contribute to the desired learning system and motivate them to address their security and efficiency issues. This article systematizes the security issues in Federated Learning, its corresponding mitigation strategies, and Blockchain’s challenges. Further, a Blockchain-based Federated Learning architecture with two layers of participation is presented, which improves the global model accuracy and guarantees participant’s privacy. It leverages the channel mechanism of Blockchain for parallel model training and distribution. It facilitates establishing decentralized trust between the participants and the gateways using the Blockchain, which helps to have only honest participants.
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Khosravi, Alireza, Abdolreza Ghoreishi, and Seyyedeh-Masoumeh Bagheri. "Epidemiologic study of causes of seizure attacks in patients admitted to emergency of Zahedan city hospital, 2015-2016." International Journal Of Community Medicine And Public Health 5, no. 1 (December 23, 2017): 72. http://dx.doi.org/10.18203/2394-6040.ijcmph20175765.
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Background: Seizure is one of the most important cause of admission to the emergency department (ED). The admission rate can be decreased by identifying the etiology of seizure which leads to appropriate treatment and elimination of the underlying cause. The purpose of this study is to survey the etiology of seizure in cases admitted to ED. Methods: This cross-sectional study was conducted on 150 patients with seizure admitted to Zahedan city hospital in 2015-16. Data were collected by a checklist including demographic, familial history, past medical history of seizure, cause and type of seizure, time of occurrence, status seizure and cause of recurrence which was completed for each patient. The data was analyzed by statistical methods in SPSS.16. Results: Among all of 150 patients 82 (54.6%) were male and 68 (45.3%) were female. The most common age group was 18-45 years with 114 (76%) patients. 74 (49.3%) patients had PMH of seizure and 15(10%) patients had positive FH of seizure. The most common cause of seizure was idiopathic epilepsy (47.3%), cerebral vascular lesions (14%), withdrawal and poisoning (6.7%). The other causes were paroxysmal non epileptic seizure, primary and secondary brain tumors, metabolic diseases, trauma each with prevalence of (5.3%). Congenital diseases (3.3%), infections (2.7%), demyelinating diseases (2%) and others (2.7%). The most common type of seizures was generalized tonic-colonic seizure (69%). (55.3%) seizures occurred in 6AM- 6PM. (4.6%) patients had status seizure. The most prevalent causes of recurrent seizure was related to inadequate drug use. Conclusions: The most common cause of seizure was idiopathic epilepsy and the next common causes were cerebral vascular lesion and withdrawal. Regular follow up of epileptic patients and eliminating the underlying cause and social abnormality will be effective in decreasing the occurrence of seizure.
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Chinonso Mark, Kingsley. "Russia-United Kingdom Diplomatic Crisis over Salisbury Nerve Agent Attack: An Analysis." International Journal of Law and Public Administration 1, no. 1 (June 11, 2018): 58. http://dx.doi.org/10.11114/ijlpa.v1i1.3357.
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Russia-United Kingdom Diplomatic ties have been banter of ally-crises-ally relations and have not fared well since its first diplomatic contact in 1553 during the era of Tsardom of Russia, and currently at its most tensed point occasioned by the alleged poison attack on Sergei Skripal in Salisbury. While it remains unclear which exactly of the Novichok variants were used to poison Skripal and his daughter? The medical effect of the poison is well understood and Britain with their allies points at Russia as the culprit. This single act has turn sour the diplomatic relations of these great powers. However, it raised multiplicity of arguments among scholars and analysts, who try to highlight and analyse views on who seeks to gain from the cold war between the power megalomaniacs. It is on this ground that the paper looks at the diplomatic crisis between Russia and United Kingdom over the attempted poisoning of Skripal and his daughter. The paper employed a combination of Psycho-Cultural Theory of Conflict and Conspiracy theory as it framework, using a qualitative analysis which relies on secondary data as its source of information to explore the problematic. It further examined if there were any similarities between the Salisbury attack and other attacks carried out in United Kingdom. The paper concludes with the implications of the attack, notes that despite Russia-United Kingdom crisis prone diplomatic relation over Salisbury nerve agent attack, they have an obligation to cooperate in the United Nation should an international conflict arise, as they encompass two of the five permanent members in the UN Security Council giving them the power to veto the approval of UN Security Council resolutions should any quick decision be required. Hence, the pronged disagreement is an interruption to the adoption of quick resolutions, particularly with the emergent danger of conflict and crises on the Korean headland and the on-going civil turbulence in Syria. The paper recommends among others; for Putin to take the opportunity of a new term to start afresh and rebuild ties with the United Kingdom, following the full dictates of diplomatic principles.
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Dar, Bilquees, and Sheraz Ahsmad Lone. "Pre and post Vulnerability of Floods to Mental Health Among the Residents of Srinagar City, J&K-India." Sustainability, Agri, Food and Environmental Research 11, no. 1 (June 6, 2021): 1–12. http://dx.doi.org/10.7770/safer-v11n1-art2393.
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Health effects occur directly through contact with flood waters or indirectly from damage to infrastructure, ecosystems, food and water supplies or social support systems. They can be immediate or can appear days, weeks or months after the floods have receded. Two thirds of flood related deaths worldwide are from drowning and one third from physical trauma, heart attacks, electrocution, carbon monoxide poisoning or fire. The main of this paper was to assess the Pre and post vulnerability of floods to mental health among the residents of Srinagar city. For the collection of primary data, sample of 200 respondents were randomly selected from various areas of the Srinagar city. A well structured questionnaire was employed for collecting primary data. The study reveals that Maximum number patients were found during post floods (974). During pre floods out of the total 418 patients, maximum were found in the month of July (116 patients) followed by August (107 patients). However in post floods out of 974, maximum cases were found in January (281 patients) followed by march (273 patients). Females were found more vulnerable in both cases but in pre flood 62 percent females were found exposed to different mental health problems which increased to 77 percent after floods of September, 2014.
Key words: Flood, Mental Health, Srinagar, Vulnerability
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Baud, F. J. "Cyanide: critical issues in diagnosis and treatment." Human & Experimental Toxicology 26, no. 3 (March 2007): 191–201. http://dx.doi.org/10.1177/0960327107070566.
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The concern of a terrorist attack using cyanide, as well as the gradual awareness of cyanide poisoning in fire victims, has resulted in a renewed interest in the diagnosis and treatment of cyanide poisoning. The formerly academic presentation of cyanide poisoning must be replaced by more useful knowledge, which will allow emergency physicians and rescue workers to strongly suspect cyanide poisoning at the scene. Human cyanide poisonings may result from exposure to cyanide, its salts, or cyanogenic compounds, while residential fires are the most common condition of exposure. In fire victims, recognition of the cyanide toxidrome has been hampered by the short half-life in blood and poor stability of cyanide. In contrast, carboxyhemoglobin, as a marker of carbon monoxide poisoning, is easily measured and long-lasting. No evidence supports the assumption of the arbitrary fixed lethal thresholds of 50% for carboxyhemoglobin, and 3 mg/L for cyanide, in fire victims. Preliminary data, drawn when comparing pure carbon monoxide and pure cyanide poisonings, suggest that a cyanide toxidrome can be defined considering signs and symptoms induced by cyanide and carbon monoxide, respectively. Prospective studies in fire victims may provide value in clarifying signs and symptoms related to both toxicants. Cyanide can induce a lifethreatening poisoning from which a full recovery is possible. A number of experimentally efficient antidotes to cyanide exist, whose clinical use has been hampered due to serious side effects. The availability of potentially safer antidotes unveils the possibility of their value as first-line treatment, even in a complex clinical situation, where diagnosis is rapid and presumptive.
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Kwon, Hyun, Hyunsoo Yoon, and Ki-Woong Park. "Selective Poisoning Attack on Deep Neural Networks †." Symmetry 11, no. 7 (July 8, 2019): 892. http://dx.doi.org/10.3390/sym11070892.
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Studies related to pattern recognition and visualization using computer technology have been introduced. In particular, deep neural networks (DNNs) provide good performance for image, speech, and pattern recognition. However, a poisoning attack is a serious threat to a DNN’s security. A poisoning attack reduces the accuracy of a DNN by adding malicious training data during the training process. In some situations, it may be necessary to drop a specifically chosen class of accuracy from the model. For example, if an attacker specifically disallows nuclear facilities to be selectively recognized, it may be necessary to intentionally prevent unmanned aerial vehicles from correctly recognizing nuclear-related facilities. In this paper, we propose a selective poisoning attack that reduces the accuracy of only the chosen class in the model. The proposed method achieves this by training malicious data corresponding to only the chosen class while maintaining the accuracy of the remaining classes. For the experiment, we used tensorflow as the machine-learning library as well as MNIST, Fashion-MNIST, and CIFAR10 as the datasets. Experimental results show that the proposed method can reduce the accuracy of the chosen class by 43.2%, 41.7%, and 55.3% in MNIST, Fashion-MNIST, and CIFAR10, respectively, while maintaining the accuracy of the remaining classes.
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Li, Mohan, Yanbin Sun, Hui Lu, Sabita Maharjan, and Zhihong Tian. "Deep Reinforcement Learning for Partially Observable Data Poisoning Attack in Crowdsensing Systems." IEEE Internet of Things Journal 7, no. 7 (July 2020): 6266–78. http://dx.doi.org/10.1109/jiot.2019.2962914.
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Qureshi, Adnan Mahmood, Nadeem Anjum, Rao Naveed Bin Rais, Masood Ur-Rehman, and Amir Qayyum. "Detection of malicious consumer interest packet with dynamic threshold values." PeerJ Computer Science 7 (March 17, 2021): e435. http://dx.doi.org/10.7717/peerj-cs.435.
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As a promising next-generation network architecture, named data networking (NDN) supports name-based routing and in-network caching to retrieve content in an efficient, fast, and reliable manner. Most of the studies on NDN have proposed innovative and efficient caching mechanisms and retrieval of content via efficient routing. However, very few studies have targeted addressing the vulnerabilities in NDN architecture, which a malicious node can exploit to perform a content poisoning attack (CPA). This potentially results in polluting the in-network caches, the routing of content, and consequently isolates the legitimate content in the network. In the past, several efforts have been made to propose the mitigation strategies for the content poisoning attack, but to the best of our knowledge, no specific work has been done to address an emerging attack-surface in NDN, which we call an interest flooding attack. Handling this attack-surface can potentially make content poisoning attack mitigation schemes more effective, secure, and robust. Hence, in this article, we propose the addition of a security mechanism in the CPA mitigation scheme that is, Name-Key Based Forwarding and Multipath Forwarding Based Inband Probe, in which we block the malicious face of compromised consumers by monitoring the Cache-Miss Ratio values and the Queue Capacity at the Edge Routers. The malicious face is blocked when the cache-miss ratio hits the threshold value, which is adjusted dynamically through monitoring the cache-miss ratio and queue capacity values. The experimental results show that we are successful in mitigating the vulnerability of the CPA mitigation scheme by detecting and blocking the flooding interface, at the cost of very little verification overhead at the NDN Routers.
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Ahmad, Nasir, Adi Isworo, and Citra Indriani. "KEJADIAN LUAR BIASA KERACUNAN “CUMI-CUMIAN” DI SEKOLAH DASAR NEGERI 1 TRASAN BANDONGAN KABUPATEN MAGELANG." MEDIA ILMU KESEHATAN 7, no. 2 (November 17, 2019): 131–36. http://dx.doi.org/10.30989/mik.v7i2.232.
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Background: On May 4th, 2016, at 12:30 district surveillance officer of Magelang Health Department received reports from Public Health Center of Bandongan about 21 students of SDN 1 Trasan who suffered from the same food-poisoning symptoms.
Objective: Investigation was carried out to identify the source, how it spread and how to control it.
Methods: This study used descriptive analytic and mapping the cases distribution location. The case was people experiencing symptoms of dizziness or abdominal pain or nausea or vomiting. Data analysis was done by using bivariate analysis. Data collection were done through interviews, observations and laboratory tests on the food samples.
Results: The case was 50 students (from 1-6 grade students). The perceived symptoms were dizziness (77%), nausea (42%), abdominal pain (40%) and vomiting (8%). Attack rate found ranged from 14.3% to 60% with the highest Attack rate found on class three (60%). The incubation period of 15-240 minutes (mean 72.3 minutes). Calamari like positive Bacillus cereus and Rhodamine-B 10 mg/kg.
Conclusion: The outbreak of food poisoning because calamari like contaminated Bacillus cereus. We suggested the school committee to provide the socialization of harmful food for the students. The teachers should restrict the permission for the food vendor to sell at school.
Keywords: Bacillus cereus, , Food Poisoning, Outbreak, Rhodamine B, School Food
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Ahmad, Nasir. "KEJADIAN LUAR BIASA KERACUNAN “CUMI-CUMIAN” DI SEKOLAH DASAR NEGERI 1 TRASAN BANDONGAN KABUPATEN MAGELANG." Media Ilmu Kesehatan 7, no. 2 (August 30, 2018): 131–36. http://dx.doi.org/10.30989/mik.v7i2.280.
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Background: On May 4th, 2016, at 12:30 district surveillance officer of Magelang Health Department received reports from Public Health Center of Bandongan about 21 students of SDN 1 Trasan who suffered from the same food-poisoning symptoms.
Objective: Investigation was carried out to identify the source, how it spread and how to control it.
Methods: This study used descriptive analytic and mapping the cases distribution location. The case was people experiencing symptoms of dizziness or abdominal pain or nausea or vomiting. Data analysis was done by using bivariate analysis. Data collection were done through interviews, observations and laboratory tests on the food samples.
Results: The case was 50 students (from 1-6 grade students). The perceived symptoms were dizziness (77%), nausea (42%), abdominal pain (40%) and vomiting (8%). Attack rate found ranged from 14.3% to 60% with the highest Attack rate found on class three (60%). The incubation period of 15-240 minutes (mean 72.3 minutes). Calamari like positive Bacillus cereus and Rhodamine-B 10 mg/kg.
Conclusion: The outbreak of food poisoning because calamari like contaminated Bacillus cereus. We suggested the school committee to provide the socialization of harmful food for the students. The teachers should restrict the permission for the food vendor to sell at school.
Keywords: Bacillus cereus, , Food Poisoning, Outbreak, Rhodamine B, School Food
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Glynn, J. R., and D. J. Bradley. "The relationship between infecting dose and severity of disease in reported outbreaks of salmonella infections." Epidemiology and Infection 109, no. 3 (December 1992): 371–88. http://dx.doi.org/10.1017/s0950268800050366.
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SUMMARYThe relationship between size of the infecting dose and severity of the resulting disease has been investigated for salmonella infections by reanalysis of data within epidemics for 32 outbreaks, and comparing data between outbreaks for 68 typhoid epidemics and 49 food-poisoning outbreaks due to salmonellas. Attack rate, incubation period, amount of infected food consumed and type of vehicle are used as proxy measures of infecting dose, while case fatality rates for typhoid and case hospitalization rates for food poisoning salmonellas were used to assess severity. Limitations of the data are discussed. Both unweighted and logit analysis models are used.There is no evidence for a dose-severity relationship forSalmonella typhi, but evidence of a correlation between dose and severity is available from within-epidemic or between-epidemic analysis, or both, forSalmonella typhimurium, S. enteritidis, S. infantis, S. newport, andS. thompson. The presence of such a relationship affects the way in which control interventions should be assessed.
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Tracqui, A., C. Mutter-Schmidt, P. Kintz, C. Berton, and P. Mangin. "Amisulpride poisoning: a report on two cases." Human & Experimental Toxicology 14, no. 3 (March 1995): 294–98. http://dx.doi.org/10.1177/096032719501400310.
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The first two observations of human poisoning involving the recently developed neuroleptic amisulpride are described. In both cases drug determination was per formed using reversed-phase HPLC coupled with diode- array detection. Case 1 was a nonfatal overdosage in which the ingestion of 3.0 g amisulpride induced an attack of seizures, then light coma with agitation, hyperthermia, mydriasis, minimal extrapyramidal features, tachycardia and slight prolongation of the QT interval; the blood con centration of amisulpride was 9.63 μg ml-1. Case 2 was a fatality attributed to amisulpride in which the measured blood concentration was 41.70 μg ml-1. Our results are discussed in the light of data previously reported on the toxicity of substituted benzamides.
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Hidano, Seira, Takao Murakami, Shuichi Katsumata, Shinsaku Kiyomoto, and Goichiro Hanaoka. "Exposing Private User Behaviors of Collaborative Filtering via Model Inversion Techniques." Proceedings on Privacy Enhancing Technologies 2020, no. 3 (July 1, 2020): 264–83. http://dx.doi.org/10.2478/popets-2020-0052.
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AbstractPrivacy risks of collaborative filtering (CF) have been widely studied. The current state-of-theart inference attack on user behaviors (e.g., ratings/purchases on sensitive items) for CF is by Calandrino et al. (S&P, 2011). They showed that if an adversary obtained a moderate amount of user’s public behavior before some time T, she can infer user’s private behavior after time T. However, the existence of an attack that infers user’s private behavior before T remains open. In this paper, we propose the first inference attack that reveals past private user behaviors. Our attack departs from previous techniques and is based on model inversion (MI). In particular, we propose the first MI attack on factorization-based CF systems by leveraging data poisoning by Li et al. (NIPS, 2016) in a novel way. We inject malicious users into the CF system so that adversarialy chosen “decoy” items are linked with user’s private behaviors. We also show how to weaken the assumption made by Li et al. on the information available to the adversary from the whole rating matrix to only the item profile and how to create malicious ratings effectively. We validate the effectiveness of our inference algorithm using two real-world datasets.
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Ullah, Syed Sajid, Insaf Ullah, Hizbullah Khattak, Muhammad Asghar Khan, Muhammad Adnan, Saddam Hussain, Noor Ul Amin, and Muazzam A. Khan Khattak. "A Lightweight Identity-Based Signature Scheme for Mitigation of Content Poisoning Attack in Named Data Networking With Internet of Things." IEEE Access 8 (2020): 98910–28. http://dx.doi.org/10.1109/access.2020.2995080.
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Hussain, Saddam, Syed Sajid Ullah, Abdu Gumaei, Mabrook Al-Rakhami, Ijaz Ahmad, and Syed Muhammad Arif. "A Novel Efficient Certificateless Signature Scheme for the Prevention of Content Poisoning Attack in Named Data Networking-Based Internet of Things." IEEE Access 9 (2021): 40198–215. http://dx.doi.org/10.1109/access.2021.3063490.
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Horikoshi, Chifuyu, Phil F. Battley, and Edward O. Minot. "Annual survival estimates and risk of fluoroacetate (1080) secondary poisoning for New Zealand falcons (Falco novaeseelandiae) in a managed exotic forest." Wildlife Research 45, no. 2 (2018): 155. http://dx.doi.org/10.1071/wr17144.
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Context The risk of secondary poisoning to native fauna during pest control operations is an issue of global concern. In New Zealand, non-target impacts during sodium fluoroacetate (1080) operations are particularly contentious. 1080 is used extensively for pest control for conservation, bovine tuberculosis control, and in plantation forestry for seedling protection from herbivores. The endemic New Zealand falcon (Falco novaeseelandiae) breeds in Kaingaroa forest, an intensively managed pine plantation where regular 1080 poison operations are conducted; however, causes of mortality and risks of secondary poisoning by 1080 are not well documented. Aims We aimed to investigate mortality and survival of adult falcons with an emphasis on assessing the possible role of 1080 poisoning in annual mortality. Methods Using radio-telemetry and visual observations, we monitored 37 marked adult falcons before and after 1080 operations in 2013–14 (16 through carrot-bait and 21 through cereal-bait operations) and assessed mortality causes through post-mortem examinations. Using Program MARK, the annual survival rates for adults and independent juveniles were estimated from long-term banding data (2003–2014). Key results Survival of falcons was high through both cereal-bait (21/21) and carrot-bait (15/16) 1080 operations (overall 95% CI for survival = 84–100%). The exception was a radio-tagged male that died of unknown causes within a fortnight of an operation and tested negative for 1080 residues. Three falcons were depredated by introduced mammals. One falcon was found dead in an emaciated condition but evidently died from head injury through Australasian magpie (Cracticus tibicen) attack. The annual survival rate of falcons estimated from long-term banding was 80 ± 6.0% (mean ± s.e.) for adults and 29 ± 0.1% for juveniles. Conclusions No adult falcon death was attributable to 1080 poisoning in this study. Identifiable mortalities were attributable to depredation by introduced mammals and an injury from an Australasian magpie. The annual survival rate of Kaingaroa falcons was comparable to those of other raptor species worldwide. Implications The risk to adult falcons from 1080 secondary poisoning is likely low. Whether this is also true for juveniles requires further study.
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Prajapati, Paresh, Prabhjot Kaur, Tanvir K. Sidhu, Gurkirat Singh, Shyam Mehra, Sourabh Paul, Varun M. Malhotra, and Rupali . "Investigation of the food poisoning outbreak in girls hostel of medical college in Punjab." International Journal Of Community Medicine And Public Health 7, no. 8 (July 24, 2020): 3166. http://dx.doi.org/10.18203/2394-6040.ijcmph20203395.
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Background: Food safety is intricately woven with food security and nutrition. The “in‑living personnel” constitute a high‑risk group for food poisoning because of community kitchen practices. Quick response and action are vital to limit the morbidity and spread of the disease.Methods: A retrospective cohort study was conducted to investigate the food poisoning outbreak in private medical college. After the initial information of the sudden cases of vomiting and diarrhoea from girls’ hostel, epidemiological case sheet was developed for collecting the information from students. A line listing of all the probable cases was done. Common food items were identified. An environmental data recording was done. Attack rate, attributable risk and relative risk with 95% confidence interval were calculated for each food item to establish an association with the illness.Results: Out of the 97 students, who consumed the dinner, 8 girls presented with severe symptoms of the gastrointestinal upset along with other bodily symptoms, 60 students showed mild symptoms and 29 students did not show any of the symptoms of the disease. After analysis, palak paneer was found to be the food responsible for outbreak with most probable cause of contamination with Staph auerus.Conclusions: It has been found out in this investigation that whenever the sanitation conditions of the cooking area were compromised and negligence was played at the part of food handlers - it has always increased the probability of such outbreaks and sufferings of the people.
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Cherney, L. S., H. V. Fesenko, А. V. Prokhorov, О. Yu Moroz, and V. M. Liaskivskiy. "The Beetles (Coleoptera) dangerous for Japanese quail Coturnix japonica Temminck et Schlegel, 1849 (Phasanidae) at farms." Ukrainian Entomological Journal 16, no. 1 (October 2, 2019): 36–43. http://dx.doi.org/10.15421/281906.
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Reproduction of the Japanese quail, Coturnix japonica Temminck et Schlegel, 1849, at private farms has led to the formation of a complex of insects harming this species of birds. Darkling beetles Alphitobius diaperinus (Panzer, 1796) and A. laevigatus (Fabricius, 1781) (Coleoptera, Tenebrionidae) are the main pests of Japanese quail. Alphitobius diaperinus had already been recorded causing damage to the poultry industry in Crimea. Its larvae and adults attack chicks in the mass. Significant cannibalism is recorded for A. laevigatus in laboratory conditions. We suppose that complex of harmful insects will be added by species of the genus Ulomoides Blackburn, 1888, namely U. dermestoides (Chevrolat, 1878) imported into Ukraine. Properties of the adopted wreckers, providing their invulnerability in poultry houses, are first shown, namely: mass breeding of A. diaperinus due to feeding on other birds, ability of females of U. dermestoides to oviposit eggs during 1,5 month after the singular copulation, duration of the larval stage up to 96–110 days (usually one month long) due to a cannibalism only. The features of development and behavior of U. dermestoides are shown resembling these of A. diaperinus. The results of studies on the lifecycle’ peculiarities carried out at 2012–2019 under the laboratory conditions are given. The practical role of A. diaperinus, A. laevigatus and U. dermestoides is discussed. The forecast regarding the negative impact of U. dermestoides on the aviculture development in the Southern Ukraine is presented. The data on the poisoning of birds (C. japonica) with beetles of bean weevil Acanthoscelides obtectus (Say, 1831) (Chrysomelidae, Bruchinae) are shown. Present contribution is beneficial not only for specialists in fundamental research, but also for practitioners, in particular for personnel of State Veterinary and Plant Health as well as the State Sanitary and Epidemiological Service. First worked out and recommended a production trap for a fishing-out in the poultry houses of harmfuls beetles and their larvae at the presence of birds.
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Hadzic, Devleta, Nada Mladina, Mirsada Praso, Selmira Brkic, and Belkisa Colic. "CHARACTERISTICS OF CHILDHOOD DIFFICULTIES IN BREATHING SYNDROME." Acta Medica Saliniana 37, no. 2 (December 28, 2008): 151–56. http://dx.doi.org/10.5457/ams.v37i2.14.
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Introduction: Syndrome of difficulties in breathing has an important position in pathology of childhood. It is manifested as in diseases of respiratory tract so in series of diseases and pathological conditions linked to other organs and systems. Patients and Methods: Patient with difficulties in breathing develops clinical presentation of respiratory distress, which is characterized with many different clinical symptoms and signs. Acute respiratory failure with discrepancy between utility of oxygen and produces of carbon dioxide is the last point of respiratory distress, so the primary care of clinician is quickly recognition of abnormal blood gasses values. Early identification and appropriate treatment of incoming respiratory failure is essential for good prognosis and decreasing long term complications. The aim of this paper was to analyze retrospectively histories of diseases of children treated at the Department of Intensive care Pediatrics clinic in Tuzla and to establish type and frequency of diseases characterized with syndrome of difficult breathing, frequency of non-respiratory diseases in etiology of this syndrome, and to estimate correlation of clinical findings in admission with pulse oximetry and blood gases findings. Analysis was based on population of patients treated at the Department of Intensive care unit Pediatrics clinic in Tuzla with recorded, clinically manifested syndrome of difficult breathing. Patient selection was performed consecutively from January 1st till 31st December 2006. All selected patients were from Tuzla Canton. Source of data for this investigation was Admission protocol for Pediatric Clinics and Intensive care unit protocol and personal histories of children treated at the Intensive care unit of Pediatric Clinics January 1st till 31st December 2006. Method of work was retrospective study which analyzed anamnestic data, clinical and laboratory findings, therapeutical procedures and length of hospitalization at the Intensive care unit and outcome of the treatment. Results: The results of investigation demonstrated that in anlized period (from January 1st till 31st December 2006) in Pediatric Clinic, Tuzla a total number of 3932 children were treated, out of them 767 (19.5%) children were treated at the Department of Intensive care unit. Syndrome of difficulties in breathing was recorded in 608 patients (79.3%). The biggest number of children in this group were treated for syndrome of broncho-obstruction, total of 332 children (54.6%). Other large group was neurological disorders: convulsions and epilepsy, total number of 125 patients (20.6%). Out of total number of patients 11 (1.8%) suffered from complete failure of breathing and required mechanical ventilation support. Out of this number 10 of them were chronic ill patients. The most common causal factor for respiratory insufficiency in strict meaning of this word and endangering respiratory arrest was epileptic attack and recidivated pneumonia. Discussion: Clinical findings, pulse oximetry and blood gases analysis were in correlation and in favor of hypoxemic type of respiratory insufficiency. Results of gas analysis for group of neurological disorders and poisoning spoke in favor of acute hypercapnic respiratory insufficiency. Clinical parameters for dyspnea were absent and finding of pulse oximetry monitored isolated for these disorders demonstrated partly unreliable.
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Samosir, Kholilah, Onny Setiani, and Nurjazuli Nurjazuli. "Hubungan Pajanan Pestisida dengan Gangguan Keseimbangan Tubuh Petani Hortikultura di Kecamatan Ngablak Kabupaten Magelang." JURNAL KESEHATAN LINGKUNGAN INDONESIA 16, no. 2 (November 7, 2017): 63. http://dx.doi.org/10.14710/jkli.16.2.63-69.
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Latar belakang, Upaya untuk meningkatkan produksi pertanian agar tanaman tidak rusak oleh hama dan penyakit petani menggunakan pestisida dengan harapan mampu meningkatkan hasil pertanian dan serta dapat membuat biaya pengelolaan pertanian menjadi lebih efisien dan ekonomis. Pestisida dapat bersifat akut, kronis maupun sistemik, yang dapat menyerang sistem syaraf ,salah satunya gangguan keseimbangan,hati atau liver,dan keseimbangan hormonal dengan cara mempengaruhi kerja enzim. Penelitian bertujuan mengetahui hubungan pajanan pestisida dengan gangguan keseimbangan tubuh pada petani hortikultura di Kecamatan Ngablak, Kabupaten Magelang.Metode, penelitian ini merupakan penelitian observasional dengan desain Cross Sectional. Populasi penelitan adalah petani yang termasuk dalam kelompok tani desa Sumberejo. Sampel adalah petani desa Sumberejo yang memenuhi kriteria sebanyak 70 responden. Pengumpulan data menggunakan kuisioner, pemeriksaan kolinesterase dalam darah menggunakan Spectrophotometer, dan gangguan keseimbangan tubuh dengan menggunakan romberg test.Hasil, Sebanyak 14,3 % petani dari hasil pemeriksaan kadar kolinesterase pada petani desa Sumberejo di Kecamatan Ngablak mengalami keracunan pestisida dan 34,3% petani dari hasil pemeriksaan romberg test mengalami gangguan keseimbangan, dari hasil uji chi square menunjukkan ada hubungan antara masa kerja nilai (p = 0,036),lama kerja per hari (p = 0,015), penggunaan alat pelindung diri (p = 0,035 dan kadar kolinesterase (p = 0,000 dengan gangguan keseimbangan dan tidak ada hubungan antara frekuensi, jumlah, jenis, dosis, cara penyemprotan, dan pengelolaan pestisida dengan gangguan keseimbangan tubuhKesimpulan, faktor risiko masa kerja petani, lama kerja per hari,cara penyemprotan, penggunaan alat pelindung diri mempengaruhi adanya kadar kolinesterase dalam darah yang dapat menyebabkan gangguan keseimbangan tubuh. ABSTRACTBackground: Efforts to increase agricultural production to prevent damage or plant from past and deseae are using pesticides. It is expected to increase the agricultural yields and also can make the cost of management cheaper and economical. The pesticide give rice to cause acute, chronic or systemic poisoning. Pesticides can attack nervous system, and cause body balance disorder, The liver disorder, stomach, the immune system and the hormonal balance affect the action of enzyme. The purpose of this research was to the assocation between the pesticide exposure and body balance disorders on horticultura farmers in Ngablak sub district, Magelang District.The method: This research used the observational analiytic method with cross-sectional approach. The population in this research were horticultura farmers of Sumberejo village. The sample in this research were farmers that meet the inclusion criteria. Data collection used the questionnaire, cholinesterase in blood by spectrophotometer, examination the body were disorder was measured by the romberg test. The number of samples in this research ballance 70 farmer in Ngablak sub district, Magelang District.The results: The average level of cholinesterase of the farmer at Sumberejo village in Ngablak sub-district showered that 14,3% were poisoned by pesticide. The study result showed that 34,3% horticultura farmer at Ngablak sub-district suffered ao body balance disorder. Based on chi-square test it showed a assocation between the working period (=0,036), the duration day of work (p=0,015), the personal protective aquipment (p=0,035), the average level of cholinesterase (p=0,000) balance disorders and there is no relationship between the number, type, management, frequency and dose of pesticide spraying with body.The conclusion: Of this research, risk factor of the farmer’s working, the length of work, the spraying method, personal protective equipment effect the avarage level of cholinesterae in blood can cause distrurbance of body balance horticultura farmers.
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Nahorna, A. M., and N. V. Savenkova. "Natural death of the employees at workplace in Ukraine in the dynamics of 2015-2020." Environment & Health 100 (3) (September 2021): 13–21. http://dx.doi.org/10.32402/dovkil2021.03.013.
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Introduction: In recent years, there has been a gradual decline in occupational traumatism in Ukraine, but an in-depth analysis of the data shows that statistics does not fully reveal the complexity of the current situation on occupational traumatism and the quality of its registration and record-keeping, especially in case of sudden death at a workplace. According to the Resolution of the Cabinet of Ministers of April 17, 2019 № 337 on «Procedure for investigation and record-keeping of the incidents, occupational diseases, and accidents at the production» (with changes made in accordance with the Resolution of the Cabinet of Ministers № 1, 05.01.2021) the amendments were made where the circumstances of an accident and / or acute occupational disease (poisoning), cases of sudden death were recognized as related to production (according to the Article 52, paragraphs 6-8). Objective: We identified the regularities of the formation of the indicators of natural death at workplace of the employees over 2015-2020 in Ukraine. Materials and methods: According to the data of the State Statistics Committee of Ukraine and the State Labour Service of Ukraine, we performed the analysis of natural deaths of the employees at workplace in the dynamics of monitoring by the types of economic activity, occupations and causes over 2015-2020 and established the ways for the improvement of their record-keeping. Statistical data were evaluated with the help of rankings, methods of generally accepted statistical analysis. Results and discussion: From 2015 to 2019, the number of the accidents at production, registered and recognized as insured events, decreased from 4,260 to 3,876 (by 9.0%), and the number of the fatal traumatized increased from 375 to 422 (by 11.1%). In 2020, the number of the accidents (A) increased up to 6121 (by 30.4%), and the accidents with fatal consequences increased up to 653 (by 42.5%), mainly due to the diseases of circulatory system and COVID-19. In recent years, there has been an increase in sudden deaths (SD) and «rejuvenation» of the contingent of working people.The problem of natural death is actual worldwide and according to the WHO, makes up 5-7%. Among those who died of natural causes, young people aged 20-39 years make up a significant proportion, mainly due to circulatory diseases and COVID-19. It was established that in the dynamics of 2015-2020, there were 4861 cases of natural death (ND) at workplace in Ukraine, which are 8.0-16.2 per 100 thousand working population and 27.2-55.0 per 100 thousand population working under harmful working conditions against 5.3 and 14.1 in 2012. The analysis of cases of ND in terms of gender, age and length of service shows that more than 77.0% are men aged 20-60 years old. The distribution of cases of ND at the workplace in Ukraine by the branches of industry shows that the socio-cultural sphere (30.0-55.2%), transport (16%), engineering and metallurgy (10.5-6.6%), coal and mining industry (4.2-2.4%) are the main ones. The cases of ND at workplaces from diseases of the circulatory system in the employees of social sphere and transport, miners of coal mines have been analyzed in details. The methods of prevention of ND at workplace are proposed. Conclusions: 1. The problem of natural death at the workplace requires an additional legislation to determine the circumstances of death connected with the working conditions or the feautures of the production process. 2. The most frequent cases of ND at the workplace in Ukraine are registered among employees of the socio-cultural sphere (30.0-55.2%), transport (16%), mechanical engineering and metallurgical industry (10.5-6.6%), construction and road construction, agro-industrial complex , coal and mining industry (4.2-2.4%). 3. Diseases of the circulatory system (acute cardiovascular failure (heart attack, stroke), coronary heart disease, heart and lung failure on the background of nervous and emotional stress, lack of sleep, the presence of a complex work schedule (daily, 12 hours, night shifts, which requires a separate study of causation) and impact of the adverse factors of the working environment were the main causes of ND in the employees. 4. Prevention of ND is in the field of the restoration of the system of providing medical care to employees, improvement of the quality of preventive medical examinations, pre-shift control, occupational selection.
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"DNS Security - Prevent DNS Cache Poisoning Attack using Blockchain." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (February 10, 2020): 2151–62. http://dx.doi.org/10.35940/ijitee.d1549.029420.
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The block chain is attaining popularity day to day as it is acting as distributed ledger for Cryptocurrency such as Bitcoin and ripple. This research paper has focused on the Blockchain and its working pattern with technical implementation of block creation. This technical paper is considering the prevention of DNS Cache poisoning attack in Blockchain which is known as larger class of name-based attacks. DNS Packet interceptions may be made using various attacks like Cache poisoning attack, man-in-the-middle attacks etc. As there are numerous security mechanisms to secure the Blockchain but in order to make Blockchain immune from cache poisoning attack, there is need to update the block creation module. Therefore, this research work is proposed to make reduction in probability of data corruption that can be created from different attacks. It resolves the issue of cache poisoning attacks using user defined port instead of predefined port. However, the initialization of transmission is performed here using predefined port number. In second step, the encrypted port number is decrypted to initiate communication using user defined port number. The use of port with IP address would restrict attacks during data transmission. The paper has presented the comparative analysis of existing DNS attacking prevention mechanism to proposed work.
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Chen, Liang, Yangjun Xu, Fenfang Xie, Min Huang, and Zibin Zheng. "Data poisoning attacks on neighborhood‐based recommender systems." Transactions on Emerging Telecommunications Technologies, January 14, 2020. http://dx.doi.org/10.1002/ett.3872.
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Takiddin, Abdulrahman, Muhammad Ismail, Usman Zafar, and Erchin Serpedin. "Robust Electricity Theft Detection Against Data Poisoning Attacks in Smart Grids." IEEE Transactions on Smart Grid, 2020, 1. http://dx.doi.org/10.1109/tsg.2020.3047864.
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Ganesan, Kavitha. "Machine Learning Data Detection Poisoning Attacks Using Resource Schemes Multi-Linear Regression." Neural, Parallel, & Scientific Computations 28, no. 2 (June 1, 2020). http://dx.doi.org/10.46719/npsc20202821.
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Al-Drees, Mohammed, Marwah M. Almasri, Mousa Al-Akhras, and Mohammed Alawairdhi. "Building a DNS Tunneling Dataset." International Journal of Sensors, Wireless Communications and Control 10 (November 24, 2020). http://dx.doi.org/10.2174/2210327910999201124205758.
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Background:: Domain Name System (DNS) is considered the phone book of the Internet. Its main goal is to translate a domain name to an IP address that the computer can understand. However, DNS can be vulnerable to various kinds of attacks, such as DNS poisoning attacks and DNS tunneling attacks. Objective:: The main objective of this paper is to allow researchers to identify DNS tunnel traffic using machine-learning algorithms. Training machine-learning algorithms to detect DNS tunnel traffic and determine which protocol was used will help the community to speed up the process of detecting such attacks. Method:: In this paper, we consider the DNS tunneling attack. In addition, we discuss how attackers can exploit this protocol to infiltrate data breaches from the network. The attack starts by encoding data inside the DNS queries to the outside of the network. The malicious DNS server will receive the small chunk of data decoding the payload and put it together at the server. The main concern is that the DNS is a fundamental service that is not usually blocked by a firewall and receives less attention from systems administrators due to a vast amount of traffic. Results:: This paper investigates how this type of attack happens using the DNS tunneling tool by setting up an environment consisting of compromised DNS servers and compromised hosts with the Iodine tool installed in both machines. The generated dataset contains the traffic of HTTP, HTTPS, SSH, SFTP, and POP3 protocols over the DNS. No features were removed from the dataset so that researchers could utilize all features in the dataset. Conclusion:: DNS tunneling remains a critical attack that needs more attention to address. DNS tunneled environment allows us to understand how such an attack happens. We built the appropriate dataset by simulating various attack scenarios using different protocols. The created dataset contains PCAP, JSON, and CSV files to allow researchers to use different methods to detect tunnel traffic.
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Zhao, Ping, Hongbo Jiang, Jie Li, Zhu Xiao, Daibo Liu, Ju Ren, and Deke Guo. "Garbage in, Garbage out: Poisoning Attacks Disguised with Plausible Mobility in Data Aggregation." IEEE Transactions on Network Science and Engineering, 2021, 1. http://dx.doi.org/10.1109/tnse.2021.3103919.
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"DNS Aegis, Authentication with Digital Signature using Hash Functions and Various Attacks: KARKOFF." International Journal of Innovative Technology and Exploring Engineering 9, no. 1 (November 10, 2019): 5066–69. http://dx.doi.org/10.35940/ijitee.a8901.119119.
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DNS have a crucial role in adequate functioning/performance of the Internet. Even though every single internet applications rely/trust on ‘Domain-Name-System’ for the ‘Name-Resolutions’ yet in this particular infrastructure have numerous ‘Security-Vulnerabilities’ with specific severity level and influenced by many attacks such as: ‘BIT SQUATTING REDIRECTION’, ‘CACHE POISONING’, ‘DNS REBINDING’, ‘TYPO SQUATTING REDIRECTION’ etc. Suppose what will happen if ‘DNS-Server’ or ‘DNS-Services’ gonna compromised? Answer will be all the resources which belong to Internet/Intranet/Extranet influenced, has a result adverse effect not only for the resources used but to confidential data too. One important point is ‘DNS’ aren’t just gonna used for having ‘Domain-Names’ w.r.t. logical addresses, but it is also utilize to Restrict Unauthorized/Un-Authenticated traffic too. So in cyber security arena manageable, trust worthier infrastructure of DNS is must.
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Crook, Alexandra, Aline De Lima Leite, Thomas Payne, Fatema Bhinderwala, Jade Woods, Vijay K. Singh, and Robert Powers. "Radiation exposure induces cross-species temporal metabolic changes that are mitigated in mice by amifostine." Scientific Reports 11, no. 1 (July 7, 2021). http://dx.doi.org/10.1038/s41598-021-93401-7.
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AbstractExposure to acute, damaging radiation may occur through a variety of events from cancer therapy and industrial accidents to terrorist attacks and military actions. Our understanding of how to protect individuals and mitigate the effects of radiation injury or Acute Radiation Syndrome (ARS) is still limited. There are only a few Food and Drug Administration-approved therapies for ARS; whereas, amifostine is limited to treating low dose (0.7–6 Gy) radiation poisoning arising from cancer radiotherapy. An early intervention is critical to treat ARS, which necessitates identifying diagnostic biomarkers to quickly characterize radiation exposure. Towards this end, a multiplatform metabolomics study was performed to comprehensively characterize the temporal changes in metabolite levels from mice and non-human primate serum samples following γ-irradiation. The metabolomic signature of amifostine was also evaluated in mice as a model for radioprotection. The NMR and mass spectrometry metabolomics analysis identified 23 dysregulated pathways resulting from the radiation exposure. These metabolomic alterations exhibited distinct trajectories within glucose metabolism, phospholipid biosynthesis, and nucleotide metabolism. A return to baseline levels with amifostine treatment occurred for these pathways within a week of radiation exposure. Together, our data suggests a unique physiological change that is independent of radiation dose or species. Furthermore, a metabolic signature of radioprotection was observed through the use of amifostine prophylaxis of ARS.