Relevant bibliographies by topics / Prediction of attacks in wild

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Prediction of attacks in wild'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Prediction of attacks in wild"

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Bilen, Abdulkadir, and Ahmet Bedri Özer. "Cyber-attack method and perpetrator prediction using machine learning algorithms." PeerJ Computer Science 7 (April 9, 2021): e475. http://dx.doi.org/10.7717/peerj-cs.475.

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Cyber-attacks have become one of the biggest problems of the world. They cause serious financial damages to countries and people every day. The increase in cyber-attacks also brings along cyber-crime. The key factors in the fight against crime and criminals are identifying the perpetrators of cyber-crime and understanding the methods of attack. Detecting and avoiding cyber-attacks are difficult tasks. However, researchers have recently been solving these problems by developing security models and making predictions through artificial intelligence methods. A high number of methods of crime pred
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Siswanto, Joko, Irwan Sembiring, Adi Setiawan, and Iwan Setyawan. "Number of Cyber Attacks Predicted With Deep Learning Based LSTM Model." JUITA : Jurnal Informatika 12, no. 1 (2024): 39. http://dx.doi.org/10.30595/juita.v12i1.20210.

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The increasing number of cyber attacks will result in various damages to the functioning of technological infrastructure. A prediction model for the number of cyber attacks based on the type of attack, handling actions and severity using time-series data has never been done. A deep learning-based LSTM prediction model is proposed to predict the number of cyberattacks in a time series on 3 evaluated data sets MSLE, MSE, MAE, RMSE, and MAPE, and displays the predicted relationships between prediction variables. Cyber attack dataset obtained from kaggle.com. The best prediction model is epoch 20,
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Zuzcák, Matej, and Petr Bujok. "Using honeynet data and a time series to predict the number of cyber attacks." Computer Science and Information Systems, no. 00 (2021): 40. http://dx.doi.org/10.2298/csis200715040z.

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A large number of cyber attacks are commonly conducted against home computers, mobile devices, as well as servers providing various services. One such prominently attacked service, or a protocol in this case, is the Secure Shell (SSH) used to gain remote access to manage systems. Besides hu man attackers, botnets are a major source of attacks on SSH servers. Tools such as honeypots allow an effective means of recording and analysing such attacks. However, is it also possible to use them to effectively predict these attacks? The prediction of SSH attacks, specifically the prediction of activity
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Kadiri, Padmaja, and Seshadri Ravala. "Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks." International Journal of e-Collaboration 17, no. 4 (2021): 75–88. http://dx.doi.org/10.4018/ijec.2021100106.

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Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during secur
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Li, Zhong, Xianke Wu, and Changjun Jiang. "Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems." Security and Safety 1 (2022): 2022003. http://dx.doi.org/10.1051/sands/2022003.

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Nowadays, large numbers of smart sensors (e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services (DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in t
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Ruda, Aleš, Jaromír Kolejka, and Thakur Silwal. "GIS-Assisted Prediction and Risk Zonation of Wildlife Attacks in the Chitwan National Park in Nepal." ISPRS International Journal of Geo-Information 7, no. 9 (2018): 369. http://dx.doi.org/10.3390/ijgi7090369.

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Population growth forces the human community to expand into the natural habitats of wild animals. Their efforts to use natural sources often collide with wildlife attacks. These animals do not only protect their natural environment, but in the face of losing the potential food sources, they also penetrate in human settlements. The research was situated in the Chitwan National Park (CNP) in Nepal, and the aim of this study was to investigate possible geospatial connections between attacks of all kinds of animals on humans in the CNP and its surroundings between 2003 and 2013. The patterns of at
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Yuan, Guotao, Hong Huang, and Xin Li. "Self-supervised learning backdoor defense mixed with self-attention mechanism." Journal of Computing and Electronic Information Management 12, no. 2 (2024): 81–88. http://dx.doi.org/10.54097/7hx9afkw.

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Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors into the DNN models by poisoning a small number of training samples. The attacked models perform normally on benign samples, but when the backdoor is activated, their prediction results will be maliciously altered. To address the issues of suboptimal backdoor defense effectiveness and limited generality, a hybrid self-attention mechanism-based self-supervised learning method for backdoor defense is proposed. This method defends against backdoor attacks by levera
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Hairab, Belal Ibrahim, Heba K. Aslan, Mahmoud Said Elsayed, Anca D. Jurcut, and Marianne A. Azer. "Anomaly Detection of Zero-Day Attacks Based on CNN and Regularization Techniques." Electronics 12, no. 3 (2023): 573. http://dx.doi.org/10.3390/electronics12030573.

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The rapid development of cyberattacks in the field of the Internet of things (IoT) introduces new security challenges regarding zero-day attacks. Intrusion-detection systems (IDS) are usually trained on specific attacks to protect the IoT application, but the attacks that are yet unknown for IDS (i.e., zero-day attacks) still represent challenges and concerns regarding users’ data privacy and security in those applications. Anomaly-detection methods usually depend on machine learning (ML)-based methods. Under the ML umbrella are classical ML-based methods, which are known to have low predictio
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Tibble, Holly, Athanasios Tsanas, Elsie Horne, et al. "Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model." BMJ Open 9, no. 7 (2019): e028375. http://dx.doi.org/10.1136/bmjopen-2018-028375.

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IntroductionAsthma is a long-term condition with rapid onset worsening of symptoms (‘attacks’) which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificity to avoid unnecessary prescribing of preventative medications that carry an associated risk of adverse events. We aim to create a risk score to predict asthma attacks in primary care using a statistical learning approach trained on routinely collected electronic health record data.Methods and analysisWe will employ machine-learning classifiers (naïv
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Gazzan, Mazen, and Frederick T. Sheldon. "Opportunities for Early Detection and Prediction of Ransomware Attacks against Industrial Control Systems." Future Internet 15, no. 4 (2023): 144. http://dx.doi.org/10.3390/fi15040144.

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Industrial control systems (ICS) and supervisory control and data acquisition (SCADA) systems, which control critical infrastructure such as power plants and water treatment facilities, have unique characteristics that make them vulnerable to ransomware attacks. These systems are often outdated and run on proprietary software, making them difficult to protect with traditional cybersecurity measures. The limited visibility into these systems and the lack of effective threat intelligence pose significant challenges to the early detection and prediction of ransomware attacks. Ransomware attacks o
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Dissertations / Theses on the topic "Prediction of attacks in wild"

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Yermalovich, Pavel. "Risk estimation and prediction of cyber attacks." Doctoral thesis, Université Laval, 2021. http://hdl.handle.net/20.500.11794/67982.

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L’utilisation de l’information est étroitement liée à sa sécurité. Le fait d’exploiter des vulnérabilités permet à une tierce personne de compromettre la sécurité d’un système d’information. La modélisation des menaces aide à prévoir les attaques les plus probables visant une infrastructure donnée afin de mieux les contrer. Le projet de recherche proposé « Estimation des risques et prédiction des cyber-attaques » vise la combinaison de différentes techniques de prédiction de cyber-attaques pour mieux protéger un système informatique. Il est nécessaire de trouver les paramètres les plus informa
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Arthur, Jacob D. "Enhanced Prediction of Network Attacks Using Incomplete Data." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1020.

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For years, intrusion detection has been considered a key component of many organizations’ network defense capabilities. Although a number of approaches to intrusion detection have been tried, few have been capable of providing security personnel responsible for the protection of a network with sufficient information to make adjustments and respond to attacks in real-time. Because intrusion detection systems rarely have complete information, false negatives and false positives are extremely common, and thus valuable resources are wasted responding to irrelevant events. In order to provide bette
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Harshe, Omkar Anand. "Preemptive Detection of Cyber Attacks on Industrial Control Systems." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/54005.

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Industrial Control Systems (ICSes), networked through conventional IT infrastructures, are vulnerable to attacks originating from network channels. Perimeter security techniques such as access control and firewalls have had limited success in mitigating such attacks due to the frequent updates required by standard computing platforms, third-party hardware and embedded process controllers. The high level of human-machine interaction also aids in circumventing perimeter defenses, making an ICS susceptible to attacks such as reprogramming of embedded controllers. The Stuxnet and Aurora attacks ha
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Nuñez, del Prado Cortez Miguel. "Inference attacks on geolocated data." Thesis, Toulouse, INSA, 2013. http://www.theses.fr/2013ISAT0028/document.

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Au cours des dernières années, nous avons observé le développement de dispositifs connectéset nomades tels que les téléphones mobiles, tablettes ou même les ordinateurs portablespermettant aux gens d’utiliser dans leur quotidien des services géolocalisés qui sont personnalisésd’après leur position. Néanmoins, les services géolocalisés présentent des risques enterme de vie privée qui ne sont pas forcément perçus par les utilisateurs. Dans cette thèse,nous nous intéressons à comprendre les risques en terme de vie privée liés à la disséminationet collection de données de localisation. Dans ce but
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Fedderwitz, Frauke. "Protecting dogs against attacks by wolves (Canis lupus), with comparison to African wild dogs (Lycaon pictus) and dholes (Cuon alpinus)." Thesis, Linköpings universitet, Institutionen för fysik, kemi och biologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-56817.

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In this thesis five different protection harnesses for hunting dogs against canidae attacks were assessed. Hunting dogs can be attacked and severely injured or killed by wolves (Canis lupus) when released during hunting. So far there is no effective protection method. Similar problems are reported with African wild dogs (Lycaon pictus) and dholes (Cuon alpinus) with other domestic animals. In this study the experimental harnesses were presented on a dummy to lure the animals to attack them. The harnesses with physical (screws or spikes on the back) and ultrasound (immediate bite controlled and
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Luke, Mark Elden. "Predicting Drag Polars For Micro Air Vehicles." Diss., CLICK HERE for online access, 2003. http://contentdm.lib.byu.edu/ETD/image/etd297.pdf.

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Bourgeois, Luc. "ACCase inhibitor resistant wild oat in Manitoba : prediction, identification, and characterization." 1997. http://hdl.handle.net/1993/7393.

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High use of aryloxyphenoxypropionate (APP) and cyclohexanedione (CHD), collectively referred to as acetyl coenzyme A carboxylase (ACCase) inhibiting or Group 1 herbicides, has led to the selection of resistant wild oat (Avena fatua) populations in some regions of Manitoba. The first objective of this thesis was to determine areas of Manitoba at low, medium, and high risk of developing Group 1 resistance. This objective was achieved by using data included in the Manitoba Crop Insurance Corporation (MCIC) database. Low, medium, and high risk areas were those in which Group 1 herbicides were spra
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Chao, Jenny C. "Attachment style, anxiety sensitivity and personality in the prediction of panic attacks : response to a symptom-provocation test." 2004. http://link.library.utoronto.ca/eir/EIRdetail.cfm?Resources__ID=94843&T=F.

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Jiang, Jun. "Detection of network anomalies and novel attacks in the internet via statistical network traffic separation and normality prediction." Thesis, 2005. http://library1.njit.edu/etd/fromwebvoyage.cfm?id=njit-etd2005-027.

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Gonçalves, Hélia Marisa do Vale. "Analysis and prediction of the occurrence of leptospirosis in wild boars (Sus scrofa Linnaeus, 1758) of the Trás-os-Montes Region." Master's thesis, 2016. http://hdl.handle.net/10348/5395.

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Dissertação de Mestrado em Biologia Clínica Laboratorial<br>A leptospirose é uma zoonose (re) emergente com importância mundial, causada por espiroquetas patogénicas do género Leptospira, mantidas no meio ambiente por vários reservatórios selvagens e domésticos, os quais representam a principal fonte de infeção para os humanos e outros hospedeiros suscetíveis. O javali (Sus scrofa) é o ungulado de caça maior com maior importância em termos cinegéticos em Portugal. Apresenta uma das distribuições geográficas mais amplas e na Europa as suas densidades populacionais têm aumentado, havendo uma cr
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Books on the topic "Prediction of attacks in wild"

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C, Jorgensen Charles, Ross James C, and Ames Research Center, eds. Neural network prediction of new aircraft design coefficients. National Aeronautics and Space Administration, Ames Research Center, 1997.

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Campbell, Doug. The Campbell prediction system: A wild land fire prediction system and language. s.n., 1993.

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Campbell, Doug. The Campbell prediction system: A wild land fire prediction and communication system. D. Campbell, 1991.

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United States. National Aeronautics and Space Administration., ed. Prediction of wind tunnel effects on the installed F/A-18A inlet flow field at high angles-of-attack. National Aeronautics and Space Administration, 1995.

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John, Christopher. Wild Jack. 2nd ed. Collier Books, 1991.

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United States. National Aeronautics and Space Administration., ed. Aerothermodynamic measurement and prediction for modified orbiter at Mach 6 and 10. American Institute of Aeronautics and Astronautics, 1995.

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Copyright Paperback Collection (Library of Congress), ed. Trailsman 329: Bayou Trackdown. Signet, 2009.

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Vaillant, John. The tiger: A true story of vengeance and survival. Vintage Books, 2011.

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Vaillant, John. Le tigre: Une histoire de survie dans la taïga : récit. Libretto, 2013.

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Vaillant, John. The tiger: A true story of vengeance and survival. Alfred A. Knopf, 2010.

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Book chapters on the topic "Prediction of attacks in wild"

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Rebeiro, Chester, Debdeep Mukhopadhyay, and Sarani Bhattacharya. "Branch Prediction Attacks." In Timing Channels in Cryptography. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12370-7_9.

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Ryckelynck, David, Fabien Casenave, and Nissrine Akkari. "Error Estimation." In Manifold Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52764-7_3.

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AbstractConsider first data-based machine learning techniques. They rely on large sets of examples provided during the training stage and do not learn with equations. Dealing with a situation that do not belong to the training set variability, namely an out-of-distribution sample, can be very challenging for these techniques. Trusting them could imply being able to guarantee that the training set covers the operational domain of the system to be trained. Besides, data-based AI can lack in robustness: examples have been given of adversarial attacks in which a classifier was tricked to infer a w
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Vovk, Vladimir. "Competing with Wild Prediction Rules." In Learning Theory. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11776420_41.

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Cao, Yulong, Chaowei Xiao, Anima Anandkumar, Danfei Xu, and Marco Pavone. "AdvDO: Realistic Adversarial Attacks for Trajectory Prediction." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20065-6_3.

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Aakaash, N., K. Akshaya Bala, Veerabrahmam Pranathi, and Meenakshi S. Arya. "Prediction of Network Attacks Using Connection Behavior." In Machine Learning for Predictive Analysis. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7106-0_30.

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Chen, Jinyin, Ximin Zhang, and Haibin Zheng. "Backdoor Attack on Dynamic Link Prediction." In Attacks, Defenses and Testing for Deep Learning. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0425-5_7.

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Abusnaina, Ahmed, Mohammed Abuhamad, DaeHun Nyang, Songqing Chen, An Wang, and David Mohaisen. "Insights into Attacks’ Progression: Prediction of Spatio-Temporal Behavior of DDoS Attacks." In Information Security Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65299-9_27.

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Arumugam, Chamundeswari, Varsha Bhargavi Dwarakanathan, S. Gnanamary, et al. "Prediction of SQL Injection Attacks in Web Applications." In Computational Science and Its Applications – ICCSA 2019. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24305-0_37.

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Liu, Mingqian, and Hongyi Zhang. "Poisoning Attacks on Deep Learning Based Spectrum Prediction." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7770-9_31.

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Subramani, Karthika, Roberto Perdisci, and Maria Konte. "Detecting and Measuring In-The-Wild DRDoS Attacks at IXPs." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80825-9_3.

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Conference papers on the topic "Prediction of attacks in wild"

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Yang, Tianming, Runhai Jiao, Junqiang Wei, and Xuejie Wu. "A Markov Matrix Based Method for Prediction Direction in Wind Power Forecasting Attacks." In 2024 6th International Conference on Energy, Power and Grid (ICEPG). IEEE, 2024. https://doi.org/10.1109/icepg63230.2024.10775482.

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Nguyen, Hoang Dai, Karthika Subramani, Bhupendra Acharya, Roberto Perdisci, and Phani Vadrevu. "C-Frame: Characterizing and measuring in-the-wild CAPTCHA attacks." In 2024 IEEE Symposium on Security and Privacy (SP). IEEE, 2024. http://dx.doi.org/10.1109/sp54263.2024.00200.

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Tan, Yicong, Xinyue Shen, Yun Shen, Michael Backes, and Yang Zhang. "On the Effectiveness of Prompt Stealing Attacks on In-the-Wild Prompts." In 2025 IEEE Symposium on Security and Privacy (SP). IEEE, 2025. https://doi.org/10.1109/sp61157.2025.00120.

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Saiganesh, Marati, Kiran L. N. Eranki, Mahankali Koushik, and Peddi Shashi Preetham. "Prediction of Intrusion Attacks Using Machine Learning Approach." In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717035.

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Zhang, Zifan, Minghong Fang, Jiayuan Huang, and Yuchen Liu. "Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction." In 2024 IFIP Networking Conference (IFIP Networking). IEEE, 2024. http://dx.doi.org/10.23919/ifipnetworking62109.2024.10619763.

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Alemerien, Khalid, Sadeq Al-Suhemat, Fadi Alsuhimat, and Enshirah Altarawneh. "Enhancing Side-Channel Attacks Prediction using Convolutional Neural Networks." In 2024 14th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2024. http://dx.doi.org/10.1109/acit62333.2024.10712474.

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Al-Turk, Aynoor, Kasim M. Al-Aubidy, and Mustafa A. Al-Khawaldeh. "Prediction of Asthma Attacks Using ANFIS and Mobile Technologies." In 2024 17th International Conference on Development in eSystem Engineering (DeSE). IEEE, 2024. https://doi.org/10.1109/dese63988.2024.10912035.

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Al-Turk, Aynoor, Kasim M. Al-Aubidy, and Mustafa A. Al-Khawaldeh. "Prediction of Asthma Attacks Using ANFIS and Mobile Technologies." In 2024 17th International Conference on Development in eSystem Engineering (DeSE). IEEE, 2024. https://doi.org/10.1109/dese63988.2024.10911899.

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Subrmanian, Kayalvizhi, Gunasekar Thangarasu, Zhao Yanyan, and K. Nattar Kannan. "Enhancing Detection and Prediction of DDoS Attacks Through Regression Modeling." In 2024 IEEE 6th Symposium on Computers & Informatics (ISCI). IEEE, 2024. http://dx.doi.org/10.1109/isci62787.2024.10668039.

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Faradilla, Arnes, and Taufik Djatna. "The Prevention Stroke for High-Risk Patients Using Prediction and Treatment Based on Adaptive Neuro-Fuzzy Inference System (ANFIS)." In 14th International Seminar on Industrial Engineering and Management. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-qk2n23.

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Stroke is the second factor of mortality in the world. According to the World Health Organization (WHO), stroke is an acute brain dysfunction. The effects of stroke are disability and mortality. Therefore, this is a concern for world health. In early 2019, the Pandemic Covid-19 attacked the world and caused many mortalities. Especially, people who have complications with diseases such as heart attack, stroke, and asthma. The purpose of this research is to predict stroke diseases with input parameters (age, glucose level, heart rate, and BMI) and to test the accuracy of the system. Moreover, an
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Reports on the topic "Prediction of attacks in wild"

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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,
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Hatada, Mitsuhiro, and Matthew Scholl. An Empirical Study on Flow-based Botnet Attacks Prediction. National Institute of Standards and Technology, 2020. http://dx.doi.org/10.6028/nist.tn.2111.

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Granot, David, Richard Amasino, and Avner Silber. Mutual effects of hexose phosphorylation enzymes and phosphorous on plant development. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7587223.bard.

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Research objectives 1) Analyze the combined effects of hexose phosphorylation and P level in tomato and Arabidopsis plants 2) Analyze the combined effects of hexose phosphorylation and P level in pho1 and pho2 Arabidopsis mutants 3) Clone and analyze the PHO2 gene 4) Select Arabidopsis mutants resistant to high and low P 5) Analyze the Arabidopsis mutants and clone the corresponding genes 6) Survey wild tomato species for growth characteristics at various P levels Background to the topic Hexose phosphorylating enzymes, the first enzymes of sugar metabolism, regulate key processes in plants suc
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Fagúndez, Jaime, Laura Lagos, José Antonio Cortés Vázquez, and Flávia Canastra. Galician Wild Ponies. Socio-Economic Context and Environmental Benefits: Galicia Area Report and Case Study for GrazeLIFE (LIFE18 PRE NL 002). Publishing Service-University of A Coruña, 2022. http://dx.doi.org/10.17979/spudc.9788497498234.

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The University of A Coruña is partner of the GRAZELIFE LIFE preparatory project (LIFE 18 PRE/NL002). We contributed to the main aim of the project of promoting sustainable grazing by large herbivores, with the study of the particular case of Galician wild ponies as a natural grazing semi-wild land use model, and alternative land uses of short and long-term afforestation, extensive grazing and abandonment. We selected two sub-areas in Galicia representing different situations in dominant land uses and the wild ponies’ system. Xistral, in the north, is a protected Natura 2000 site covered by wet
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Fagúndez, Jaime, Laura Lagos, José Antonio Cortés Vázquez, and Flávia Canastra. Galician Wild Ponies. Socio-Economic Context and Environmental Benefits: Galicia Area Report and Case Study for GrazeLIFE (LIFE18 PRE NL 002). Publishing Service-University of A Coruña, 2022. http://dx.doi.org/10.17979/spudc.9788497498241.

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The University of A Coruña is partner of the GRAZELIFE LIFE preparatory project (LIFE 18 PRE/NL002). We contributed to the main aim of the project of promoting sustainable grazing by large herbivores, with the study of the particular case of Galician wild ponies as a natural grazing semi-wild land use model, and alternative land uses of short and long-term afforestation, extensive grazing and abandonment. We selected two sub-areas in Galicia representing different situations in dominant land uses and the wild ponies’ system. Xistral, in the north, is a protected Natura 2000 site covered by wet
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Griswold, Jim. Monitoring and Evaluation of Smolt Migration in the Columbia Basin, Volume XIV; Evaluation of 2006 Prediction of the Run-Timing of Wild and Hatchery-Reared Salmon and Steelhead at Rock Island, Lower Granite, McNary, John Day and Bonneville Dams using Program Real Time, Technical Report 2006. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/963283.

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