Adversarial machine learning for network intrusion detection systems: A comprehensive survey
Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …
network attacks that compromise the security of the data, systems, and networks. In recent …
[HTML][HTML] Adversarial machine learning attacks against intrusion detection systems: A survey on strategies and defense
A Alotaibi, MA Rassam - Future Internet, 2023 - mdpi.com
Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
Adversarial attacks against network intrusion detection in IoT systems
Deep learning (DL) has gained popularity in network intrusion detection, due to its strong
capability of recognizing subtle differences between normal and malicious network activities …
capability of recognizing subtle differences between normal and malicious network activities …
Modeling realistic adversarial attacks against network intrusion detection systems
The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …
creating novel defensive opportunities but also new types of risks. Multiple researches have …
[HTML][HTML] Feature extraction for machine learning-based intrusion detection in IoT networks
A large number of network security breaches in IoT networks have demonstrated the
unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network …
unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network …
Adversarial machine learning for network intrusion detection: A comparative study
H Jmila, MI Khedher - Computer Networks, 2022 - Elsevier
Intrusion detection is a key topic in cybersecurity. It aims to protect computer systems and
networks from intruders and malicious attacks. Traditional intrusion detection systems (IDS) …
networks from intruders and malicious attacks. Traditional intrusion detection systems (IDS) …
Secfednids: Robust defense for poisoning attack against federated learning-based network intrusion detection system
Federated learning-based network intrusion detection system (FL-based NIDS) has
demonstrated tremendous potential in protecting the security of IoT network. It enables …
demonstrated tremendous potential in protecting the security of IoT network. It enables …
Graph neural networks for intrusion detection: A survey
T Bilot, N El Madhoun, K Al Agha, A Zouaoui - IEEE Access, 2023 - ieeexplore.ieee.org
Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …
A gradient-based approach for adversarial attack on deep learning-based network intrusion detection systems
Intrusion detection systems are an essential part of any cybersecurity architecture. These
systems are critical in defending networks against a variety of security threats. In recent …
systems are critical in defending networks against a variety of security threats. In recent …
[HTML][HTML] SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection
Abstract Machine Learning (ML) can be incredibly valuable to automate anomaly detection
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …