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] A systematic literature review of methods and datasets for anomaly-based network intrusion detection
As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and
threatening. Network intrusion detection has been widely accepted as an effective method to …
threatening. Network intrusion detection has been widely accepted as an effective method to …
[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) …
An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning
OA Alghanam, W Almobaideen, M Saadeh… - Expert Systems with …, 2023 - Elsevier
With the rapid growth of the number of connected devices that exchange personal, sensitive,
and important data through the IoT based global network, attacks that are targeting security …
and important data through the IoT based global network, attacks that are targeting security …
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 …
Fed-anids: Federated learning for anomaly-based network intrusion detection systems
As computer networks and interconnected systems continue to gain widespread adoption,
ensuring cybersecurity has become a prominent concern for organizations, regardless of …
ensuring cybersecurity has become a prominent concern for organizations, regardless of …
Adversarial machine learning: A multilayer review of the state-of-the-art and challenges for wireless and mobile systems
J Liu, M Nogueira, J Fernandes… - … Surveys & Tutorials, 2021 - ieeexplore.ieee.org
Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …
normal samples but have some imperceptible noise added to them with the intention of …
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 …
A performance overview of machine learning-based defense strategies for advanced persistent threats in industrial control systems
Cybersecurity incident response is a very crucial part of the cybersecurity management
system. Adversaries emerge and evolve with new cybersecurity tactics, techniques, and …
system. Adversaries emerge and evolve with new cybersecurity tactics, techniques, and …
Generating realistic cyber data for training and evaluating machine learning classifiers for network intrusion detection systems
M Chalé, ND Bastian - Expert Systems with Applications, 2022 - Elsevier
Cyberspace operations, in conjunction with artificial intelligence and machine learning
enhanced cyberspace infrastructure, make it possible to connect sensors directly to shooters …
enhanced cyberspace infrastructure, make it possible to connect sensors directly to shooters …