A holistic review of machine learning adversarial attacks in IoT networks
With the rapid advancements and notable achievements across various application
domains, Machine Learning (ML) has become a vital element within the Internet of Things …
domains, Machine Learning (ML) has become a vital element within the Internet of Things …
Adversarial machine learning in network intrusion detection domain: A systematic review
HA Alatwi, C Morisset - arXiv preprint arXiv:2112.03315, 2021 - arxiv.org
Due to their massive success in various domains, deep learning techniques are increasingly
used to design network intrusion detection solutions that detect and mitigate unknown and …
used to design network intrusion detection solutions that detect and mitigate unknown and …
Edge computing technology enablers: A systematic lecture study
With the increasing stringent QoS constraints (eg, latency, bandwidth, jitter) imposed by
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …
novel applications (eg, e-Health, autonomous vehicles, smart cities, etc.), as well as the …
Evasion Attack and Defense On Machine Learning Models in Cyber-Physical Systems: A Survey
Cyber-physical systems (CPS) are increasingly relying on machine learning (ML)
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
Mitigation of black-box attacks on intrusion detection systems-based ml
Intrusion detection systems (IDS) are a very vital part of network security, as they can be
used to protect the network from illegal intrusions and communications. To detect malicious …
used to protect the network from illegal intrusions and communications. To detect malicious …
Adversarial Attacks and Defenses in 6G Network-Assisted IoT Systems
The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G)
networks due to dense connectivity, ultrareliability, low latency, and high throughput …
networks due to dense connectivity, ultrareliability, low latency, and high throughput …
[HTML][HTML] Robust network anomaly detection using ensemble learning approach and explainable artificial intelligence (XAI)
MK Hooshmand, MD Huchaiah, AR Alzighaibi… - Alexandria Engineering …, 2024 - Elsevier
Abstract Intrusion Detection Systems, specifically Network Anomaly Detection Systems
(NADSs) are vital tools in network security. The NADSs are affected by data imbalance …
(NADSs) are vital tools in network security. The NADSs are affected by data imbalance …
Anomaly-based intrusion on iot networks using aigan-a generative adversarial network
Adversarial attacks have threatened the credibility of machine learning models and cast
doubts over the integrity of data. The attacks have created much harm in the fields of …
doubts over the integrity of data. The attacks have created much harm in the fields of …
Constraining adversarial attacks on network intrusion detection systems: transferability and defense analysis
Adversarial attacks have been extensively studied in the domain of deep image
classification, but their impacts on other domains such as Machine and Deep Learning …
classification, but their impacts on other domains such as Machine and Deep Learning …
Threat modeling for machine learning-based network intrusion detection systems
HA Alatwi, C Morisset - … Conference on Big Data (Big Data), 2022 - ieeexplore.ieee.org
Network Intrusion Detection Systems (NIDS) monitor networking environments for
suspicious events that could compromise the availability, integrity, or confidentiality of the …
suspicious events that could compromise the availability, integrity, or confidentiality of the …