Relevant bibliographies by topics / False negative attacks

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Academic literature on the topic 'False negative attacks'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "False negative attacks"

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Su, Man Nam, and Ho Cho Tae. "A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS." Advances in Vision Computing: An International Journal (AVC) 3, no. 3 (2016): 09–16. https://doi.org/10.5281/zenodo.3518809.

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In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative a
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Su, Man Nam, and Ho Cho Tae. "A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS." Advances in Vision Computing: An International Journal (AVC) 3, no. 3 (2016): 09–16. https://doi.org/10.5281/zenodo.3689099.

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In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative a
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Shang, Yilun. "False Positive and False Negative Effects on Network Attacks." Journal of Statistical Physics 170, no. 1 (2017): 141–64. http://dx.doi.org/10.1007/s10955-017-1923-7.

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Nan, Haymarn Oo, Cahyadi Risdianto Aris, Chaw Ling Teck, and Htein Maw Aung. "Flooding Attack Detection and Mitigation in SDN with Modified Adaptive Threshold Algorithm." International Journal of Computer Networks & Communications (IJCNC) 12, no. 3 (2020): 75–95. https://doi.org/10.5281/zenodo.3946219.

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Flooding attack is a network attack that sends a large amount of traffic to the victim networks or services to cause denial-of-service. In Software-Defined Networking (SDN) environment, this attack might not only breach the hosts and services but also the SDN controller. Besides, it will also cause a disconnection of links between the controller and the switches. Thus, an effective detection and mitigation technique of flooding attacks is required. Statistical analysis techniques are widely used for the detection and mitigation of flooding attacks. However, the effectiveness of these technique
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Shahriar, Hossain, Hisham Haddad, and Vamshee Krishna Devendran. "Request and Response Analysis Framework for Mitigating Clickjacking Attacks." International Journal of Secure Software Engineering 6, no. 3 (2015): 1–25. http://dx.doi.org/10.4018/ijsse.2015070101.

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This paper addresses the detection of clickjacking attacks, which is an emerging web application security issue. The authors propose a web application request and response page analysis framework to detect clickjacking attacks. Their framework considers not only inspects visual features related to frame, JavaScript code pattern in details to match with known attack signatures. The proposed approach is able to detect advanced clickjacking attacks such as cursorjacking, double click, and history object-based attacks. The authors evaluate the proposed approach with a set of legitimate and malicio
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Ajdani, Mahdi. "Deep Learning-Based Intrusion Detection Systems." International Journal of Information Security and Privacy 19, no. 1 (2025): 1–15. https://doi.org/10.4018/ijisp.383299.

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Given the increasing growth of cyber-attacks, the need for intrusion detection systems (IDS) with higher accuracy and efficiency is critical. This paper presents a novel approach using Generative Adversarial Networks (GANs) for intrusion detection. The proposed model leverages deep learning to extract complex features and uses GANs to generate synthetic data, improving IDS accuracy and efficiency. This approach reduces false positive and negative rates while increasing the accuracy of detecting unknown attacks. Experimental results on the NSL-KDD and CICIDS2017 datasets show 98.2% accuracy, a
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Akram, Muhammad, Muhammad Ashraf, and Tae Ho Cho. "Enhancing the Statistical Filtering Scheme to Detect False Negative Attacks in Sensor Networks." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 52. http://dx.doi.org/10.30537/sjcms.v1i1.7.

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In this paper, we present a technique that detects both false positive and false negative attacks in statistical filtering-based wireless sensor networks. In statistical filtering scheme, legitimate reports are repeatedly verified en route before they reach the base station, which causes heavy energy consumption. While the original statistical filtering scheme detects only false reports, our proposed method promises to detect both attacks.
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Dalvi, Sailee, Gilad Gressel, and Dr Krishnashree Achuthan. "Tuning the False Positive Rate / False Negative Rate with Phishing Detection Models." International Journal of Engineering and Advanced Technology 9, no. 1s5 (2019): 7–13. http://dx.doi.org/10.35940/ijeat.a1002.1291s519.

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Phishing attacks have risen by 209% in the last 10 years according to the Anti Phishing Working Group (APWG) statistics [19]. Machine learning is commonly used to detect phishing attacks. Researchers have traditionally judged phishing detection models with either accuracy or F1-scores, however in this paper we argue that a single metric alone will never correlate to a successful deployment of machine learning phishing detection model. This is because every machine learning model will have an inherent trade-off between it’s False Positive Rate (FPR) and False Negative Rate (FNR). Tuning the tra
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Oluwole Ogini, Nicholas, Wilfred Adigwe, and Noah Oghenefego Ogwara. "Distributed Denial of Service Attack Detection and Prevention Model for IoT based Computing Environment using Ensemble Machine Learning Approach." International Journal of Network Security & Its Applications 14, no. 4 (2022): 39–53. http://dx.doi.org/10.5121/ijnsa.2022.14403.

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Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environme
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Nicholas, Oluwole Ogini, Adigwe Wilfred, and Oghenefego Ogwara Noah. "DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION AND PREVENTION MODEL FOR IOTBASED COMPUTING ENVIRONMENT USING ENSEMBLE MACHINE LEARNING APPROACH." International Journal of Network Security & Its Applications (IJNSA) 14, no. 4 (2022): 39–53. https://doi.org/10.5281/zenodo.7002129.

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Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environme
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Book chapters on the topic "False negative attacks"

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Bhardwaj, Akashdeep. "Comparison of IoT Communication Protocols Using Anomaly Detection with Security Assessments of Smart Devices." In Smart Home and Industrial IoT Devices: Critical Perspectives on Cyberthreats, Frameworks and Protocols. BENTHAM SCIENCE PUBLISHERS, 2024. http://dx.doi.org/10.2174/9789815256710124010009.

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The authors implemented an attack scenario simulating attacks to compromise node and sensor data. This research proposes a framework with algorithms that generate automated malicious commands, which conform to device protocol standards and bypass compromise detection. The authors performed attack detection testing with three different home setup simulations and referred to accuracy of detection, ease of precision, and attack recall, with F1-score as the parameters. The results obtained for anomaly detection of IoT logs and messages used K-nearest neighbor, multi-layer perceptron, logistic regr
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Jamieson, Kathleen Hall. "Countering Attacks: Pitting the Propositional Against the Primal." In Dirty Politics. Oxford University PressNew York, NY, 1992. http://dx.doi.org/10.1093/oso/9780195078541.003.0005.

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Abstract From the beginnings of the Republic, politicians and their partisans have recognized that an attack unrebutted is an attack believed. “A falsehood that remains uncontradicted for a month, begins to be looked upon as a truth,” wrote William Corbett in Porcupine’s Gazette in 1797, “and when the detection at last makes its appearance, it is often as useless as that of the doctor who finds his patient expired.” But how can one respond to telegraphic, televisual attack that prompts false conclusions? The comparative power of the negative accounts for the fact that: In 1984 and 1988, the fi
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Jeyaram, A., and A. Muthukumaravel. "Adversarial Attacks on Deep Learning-Based Intrusion Detection Systems on Challenges and Countermeasures." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-4672-4.ch009.

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DL-based IDS are crucial for detecting and reducing security risks in network settings. Nevertheless, these systems are susceptible to adversarial assaults that exploit model flaws. This research presents a defensive architecture that combines dynamic retraining, ensemble learning, real-time monitoring, and model interpretability to improve the resilience of IDS. Over subsequent years, the performance assessment reveals significant improvements in accuracy (from 0.85 to 0.94), precision (from 0.78 to 0.91), recall (from 0.89 to 0.95), and F1 score (from 0.83 to 0.93). Significantly, there was
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Subathra, P., and S. Sivagurunathan. "Secure Route Discovery in DSR against Black Hole Attacks in Mobile Ad Hoc Networks." In Next Generation Data Communication Technologies. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-61350-477-2.ch006.

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A Mobile Ad hoc Network (MANET) is a collection of wireless nodes communicating over multi-hop paths without any infrastructure. Nodes must cooperate to provide necessary network functionalities. The security in routing protocols like Dynamic Source Routing (DSR) can be compromised by a “Black Hole” attack. Here, a malicious node claims to have the shortest path to the destination and attracts all traffic and drops them, leading to performance degradation. The situation becomes worse when two or more nodes cooperate and perform the “Cooperative black hole” attack. This chapter proposes a solut
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Sigamani, Rama Mercy Sam. "Adoption of Machine Learning With Adaptive Approach for Securing CPS." In Handbook of Research on Machine and Deep Learning Applications for Cyber Security. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9611-0.ch018.

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The cyber physical system safety and security is the major concern on the incorporated components with interface standards, communication protocols, physical operational characteristics, and real-time sensing. The seamless integration of computational and distributed physical components with intelligent mechanisms increases the adaptability, autonomy, efficiency, functionality, reliability, safety, and usability of cyber-physical systems. In IoT-enabled cyber physical systems, cyber security is an essential challenge due to IoT devices in industrial control systems. Computational intelligence
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Snášel, Václav, Jan Platoš, Pavel Krömer, and Ajith Abraham. "Designing Light Weight Intrusion Detection Systems." In Socioeconomic and Legal Implications of Electronic Intrusion. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-204-6.ch013.

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Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This chapter presents some of the challenges in designing efficient ad light weight intrusion detection systems, which could provide high accuracy, low f
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Snášel, Václav, Jan Platoš, Pavel Krömer, and Ajith Abraham. "Designing Light Weight Intrusion Detection Systems." In Machine Learning. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-60960-818-7.ch213.

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Recently cyber security has emerged as an established discipline for computer systems and infrastructures with a focus on protection of valuable information stored on those systems from adversaries who want to obtain, corrupt, damage, destroy or prohibit access to it. An Intrusion Detection System (IDS) is a program that analyzes what happens or has happened during an execution and tries to find indications that the computer has been misused. This chapter presents some of the challenges in designing efficient ad light weight intrusion detection systems, which could provide high accuracy, low f
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Williams, Monnica T. "Understanding Racial Microaggressions." In Managing Microaggressions. Oxford University Press, 2020. http://dx.doi.org/10.1093/med-psych/9780190875237.003.0001.

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Abstract: This chapter introduces the concept of microaggressions. It reviews stigmatized racial and ethnic identities and the importance of terminology. It outlines the meaning of Whiteness and White racial identity. It then describes and defines what microaggressions are specifically and the research surrounding this concept. Common stereotypes of ethnic and racial groups are outlined. Types of microaggressive behavior include categories termed not a true citizen; racial categorization and sameness; assumptions about intelligence, competence, and status; false color-blindness/invalidating ra
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Jose, Morales. "Gonioscopy." In Glaucoma. Oxford University Press, 2012. http://dx.doi.org/10.1093/oso/9780199757084.003.0007.

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Gonioscopy allows visualization of the anterior chamber angle using special lenses. The anterior chamber angle cannot be visualized by direct observation because of its position in the angle recess and the phenomenon of total internal reflection of the light caused by the cornea. Goniolenses with appropriate optical properties and mirrors or prisms allow adequate visualization of its structures through a “periscope effect” and provide a more favourable reflection angle. Clinical circumstances where gonioscopy is essential or helpful in the diagnosis and management of glaucoma are as follows: •
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Bhimavarapu, Usharani. "Advanced Deep Learning Frameworks for Cyber Security in IoT-Based Healthcare." In Critical Phishing Defense Strategies and Digital Asset Protection. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-8784-9.ch014.

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The rapid adoption of IoT-based healthcare devices has revolutionized the medical industry by enabling real-time monitoring and data collection, yet it raises significant privacy and security concerns. These devices are vulnerable to various cyber-attacks, including man-in-the-middle attacks, spoofing, and data injection, which can compromise sensitive patient information and disrupt critical healthcare operations. To address these challenges, this study explores the application of deep learning techniques, particularly ResNet-50, to classify unforeseen cyber-attacks in IoT-based healthcare sy
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Conference papers on the topic "False negative attacks"

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O'Meara, John William, Mahmoud Said Elsayed, Takfarinas Saber, and Anca Delia Jurcut. "SDN Intrusion Detection: An Ensemble Approach to Reducing False Negative Rate for Novel Attacks." In 2022 32nd International Telecommunication Networks and Applications Conference (ITNAC). IEEE, 2022. http://dx.doi.org/10.1109/itnac55475.2022.9998363.

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Schuartz, Fabio Cesar, Anelise Munaretto, and Mauro Fonseca. "Uma Comparação entre os Sistemas de Detecção de Ameaças Distribuídas de Rede Baseado no Processamento de Dados em Fluxo e em Lotes." In XXIV Workshop de Gerência e Operação de Redes e Serviços. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/wgrs.2019.7681.

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With the advancement of technology, allowing a greater massification of devices connected to the Internet of Things, there is a huge increase in the communication that circulates through the network, resulting in a growing number of vulnerability exploitations detected every year. Thus, faster and more accurate systems are needed to efficiently detect distributed denial of service attacks and port scans. This paper proposes a system for on-line detection of distributed network threats using data stream processing. The results obtained by the proposed system are compared with the results obtain
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Ferraz, André, and Carlos Ferraz. "Digital Identity Challenge: The Security and Convenience Dilemma." In Seminário Integrado de Software e Hardware. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/semish.2021.15829.

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This paper argues that the essential pieces of an enduring digital identity should be privacy, security, and convenience. Authentication should be frictionless. In this sense, the core of the digital identity of the future will be created around location sensing techniques. Incognia proposes a solution to secure and frictionless authentication for mobile apps that is composed of five steps. Its proprietary technology called environment fingerprinting can identify location spoofing and precisely determine the devices actual location. Incognia has found that most mobile logins, sensitive transac
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Zhou, Tao, Qi Ye, Wenhan Luo, Kaihao Zhang, Zhiguo Shi, and Jiming Chen. "F&F Attack: Adversarial Attack against Multiple Object Trackers by Inducing False Negatives and False Positives." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.00422.

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Khan Pathan, Shadman Mahmood, and Sakan Binte Imran. "False Negative Induction in Brain Tumor Segmentation by Trained Noise Attack." In 2023 26th International Conference on Computer and Information Technology (ICCIT). IEEE, 2023. http://dx.doi.org/10.1109/iccit60459.2023.10441583.

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Wang, Shen, Zhengzhang Chen, Xiao Yu, et al. "Heterogeneous Graph Matching Networks for Unknown Malware Detection." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/522.

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Information systems have widely been the target of malware attacks. Traditional signature-based malicious program detection algorithms can only detect known malware and are prone to evasion techniques such as binary obfuscation, while behavior-based approaches highly rely on the malware training samples and incur prohibitively high training cost. To address the limitations of existing techniques, we propose MatchGNet, a heterogeneous Graph Matching Network model to learn the graph representation and similarity metric simultaneously based on the invariant graph modeling of the program's executi
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Prasad, Romesh, and Young Moon. "Adaptive Intrusion Detection System for Cyber-Manufacturing System." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-70017.

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Abstract While Cyber-Manufacturing System security must involve three separate yet interrelated processes (prediction, detection, and prevention), the detection process is the focus of research presented in this paper. Current intrusion detection systems often result in high false positive and false negative rates. Also, the actual detection time may take long time-up to several months. The current intrusion detection systems rely heavily on the network data, but do not utilize the physical data such as side channel, sensor reading, image, keystrokes., which are generated during manufacturing
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Kale, Alexander, Ganesh Madhuranath, Viswanathan Shanmugham, Manju Nanda, Giresh Singh, and Umut Durak. "Formal Technique for Fault Detection and Identification of Control Intensive Application of Stall Warning System Using System Theoretic Process Analysis." In AeroCON 2024. SAE International, 2024. http://dx.doi.org/10.4271/2024-26-0471.

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<div class="section abstract"><div class="htmlview paragraph">Faults if not detected and processed will create catastrophe in closed loop system for safety critical applications in automotive, space, medical, nuclear, and aerospace domains. In aerospace applications such as stall warning and protection/prevention system (SWPS), algorithms detect stall condition and provide protection by deploying the elevator stick pusher. Failure to detect and prevent stall leads to loss of lives and aircraft.</div><div class="htmlview paragraph">Traditional Functional Hazard and Fault
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Reports on the topic "False negative attacks"

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Drury, J., S. Arias, T. Au-Yeung, et al. Public behaviour in response to perceived hostile threats: an evidence base and guide for practitioners and policymakers. University of Sussex, 2023. http://dx.doi.org/10.20919/vjvt7448.

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Background: Public behaviour and the new hostile threats • Civil contingencies planning and preparedness for hostile threats requires accurate and up to date knowledge about how the public might behave in relation to such incidents. Inaccurate understandings of public behaviour can lead to dangerous and counterproductive practices and policies. • There is consistent evidence across both hostile threats and other kinds of emergencies and disasters that significant numbers of those affected give each other support, cooperate, and otherwise interact socially within the incident itself. • In emerg
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