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 …
Adversarial attacks against deep learning-based network intrusion detection systems and defense mechanisms
C Zhang, X Costa-Perez… - IEEE/ACM Transactions on …, 2022 - ieeexplore.ieee.org
Neural networks (NNs) are increasingly popular in developing NIDS, yet can prove
vulnerable to adversarial examples. Through these, attackers that may be oblivious to the …
vulnerable to adversarial examples. Through these, attackers that may be oblivious to the …
Evaluating and improving adversarial robustness of machine learning-based network intrusion detectors
Machine learning (ML), especially deep learning (DL) techniques have been increasingly
used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has …
used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has …
Tiki-taka: Attacking and defending deep learning-based intrusion detection systems
Neural networks are increasingly important in the development of Network Intrusion
Detection Systems (NIDS), as they have the potential to achieve high detection accuracy …
Detection Systems (NIDS), as they have the potential to achieve high detection accuracy …
Rallying adversarial techniques against deep learning for network security
Recent advances in artificial intelligence and the increasing need for robust defensive
measures in network security have led to the adoption of deep learning approaches for …
measures in network security have led to the adoption of deep learning approaches for …
Enhancing robustness against adversarial examples in network intrusion detection systems
MJ Hashemi, E Keller - 2020 IEEE Conference on Network …, 2020 - ieeexplore.ieee.org
The increase of cyber attacks in both the numbers and varieties in recent years demands to
build a more sophisticated network intrusion detection system (NIDS). These NIDS perform …
build a more sophisticated network intrusion detection system (NIDS). These NIDS perform …
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 …
Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems
Intrusion detection systems (IDSs) are an essential cog of the network security suite that can
defend the network from malicious intrusions and anomalous traffic. Many machine learning …
defend the network from malicious intrusions and anomalous traffic. Many machine learning …
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 examples against the deep learning based network intrusion detection systems
Deep learning begins to be widely applied in security applications, but the vulnerability of
deep learning in front of adversarial examples raises people's concern. In this paper, we …
deep learning in front of adversarial examples raises people's concern. In this paper, we …