RAIDS: robust autoencoder-based intrusion detection system model against adversarial attacks
Abstract Machine learning-based intrusion detection systems (IDS) are essential security
functions in conventional and software-defined networks alike. Their success and the …
functions in conventional and software-defined networks alike. Their success and the …
Generative adversarial attacks against intrusion detection systems using active learning
Intrusion Detection Systems (IDS) are increasingly adopting machine learning (ML)-based
approaches to detect threats in computer networks due to their ability to learn underlying …
approaches to detect threats in computer networks due to their ability to learn underlying …
A case study with CICIDS2017 on the robustness of machine learning against adversarial attacks in intrusion detection
Intrusion detection systems (IDS) play a key role to assure security properties of modern
computer networks. IDS are often based on machine and deep learning techniques; as …
computer networks. IDS are often based on machine and deep learning techniques; as …
AIDTF: Adversarial training framework for network intrusion detection
WD Xiong, KL Luo, R Li - Computers & Security, 2023 - Elsevier
Abstract Network Intrusion Detection Systems (IDS) have achieved high accuracy by widely
applying Machine Learning (ML) models. However, most current ML-based IDSs can not …
applying Machine Learning (ML) models. However, most current ML-based IDSs can not …
Defending network intrusion detection systems against adversarial evasion attacks
M Pawlicki, M Choraś, R Kozik - Future Generation Computer Systems, 2020 - Elsevier
Intrusion Detection and the ability to detect attacks is a crucial aspect to ensure
cybersecurity. However, what if an IDS (Intrusion Detection System) itself is attacked; in other …
cybersecurity. However, what if an IDS (Intrusion Detection System) itself is attacked; in other …
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) …
[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) …
A simple framework to enhance the adversarial robustness of deep learning-based intrusion detection system
Deep learning based intrusion detection systems (DL-based IDS) have emerged as one of
the best choices for providing security solutions against various network intrusion attacks …
the best choices for providing security solutions against various network intrusion attacks …
Deep learning-based intrusion detection with adversaries
Z Wang - IEEE Access, 2018 - ieeexplore.ieee.org
Deep neural networks have demonstrated their effectiveness in most machine learning
tasks, with intrusion detection included. Unfortunately, recent research found that deep …
tasks, with intrusion detection included. Unfortunately, recent research found that deep …
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 …