4.5 Article

SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection

Related references

Note: Only part of the references are listed.
Article Computer Science, Information Systems

Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey

Ke He et al.

Summary: Network-based Intrusion Detection System (NIDS) is vital for defending against network attacks, but it is susceptible to adversarial attacks that manipulate input examples. This article reviews the literature on NIDS, adversarial attacks, and defence mechanisms, highlighting the challenges in launching and detecting adversarial attacks against NIDS.

IEEE COMMUNICATIONS SURVEYS AND TUTORIALS (2023)

Article Telecommunications

Investigating the practicality of adversarial evasion attacks on network intrusion detection

Mohamed Amine Merzouk et al.

Summary: This article examines the feasibility of evading network intrusion detection models through adversarial attacks. Through a detailed analysis of the generated adversarial examples, four key criteria for the validity of network traffic are introduced and discussed.

ANNALS OF TELECOMMUNICATIONS (2022)

Article Computer Science, Artificial Intelligence

Gray-Box Shilling Attack: An Adversarial Learning Approach

Zongwei Wang et al.

Summary: Recommender systems are important components of information services, but shilling attacks can weaken their robustness. This article explores potential risks of recommender systems and proposes a gray-box shilling attack model called GSA-GANs based on generative adversarial networks. Experimental results show that GSA-GANs outperform baseline models in attack effectiveness, transferability, and camouflage.

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY (2022)

Article

Sophos: The State of Ransomware 2022

Steve Mansfield-Devine

Computer Fraud and Security (2022)

Article Computer Science, Information Systems

Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System

Xiaokang Zhou et al.

Summary: The study introduces a novel adversarial attack generation method to degrade the classification precision of intelligent intrusion detection in IoT systems by identifying critical feature elements and minimal perturbations. The method also develops a hierarchical node selection algorithm based on random walk with restart to select more vulnerable nodes.

IEEE INTERNET OF THINGS JOURNAL (2022)

Review Computer Science, Information Systems

Deep Learning for Vulnerability and Attack Detection on Web Applications: A Systematic Literature Review

Rokia Lamrani Alaoui et al.

Summary: Web applications are vulnerable to hacking attempts, and traditional Web Application Firewalls and machine learning approaches have limitations in detecting unknown web attacks. This study reviews the literature on using Deep Learning for web attacks detection and identifies key areas for future research.

FUTURE INTERNET (2022)

Article Computer Science, Information Systems

Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection

Joao Vitorino et al.

Summary: Adversarial attacks pose a major threat to machine learning and systems that rely on it. This paper proposes an adaptive perturbation pattern method for generating realistic adversarial examples in a gray-box setting. The method is evaluated in a cybersecurity case study, showing its effectiveness in generating realistic adversarial examples, which can be advantageous for both adversarial training and attacks.

FUTURE INTERNET (2022)

Article Computer Science, Theory & Methods

Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

Ishai Rosenberg et al.

Summary: This article presents a comprehensive summary of recent research on adversarial attacks against security solutions based on machine learning techniques, highlighting the associated risks. The methods of adversarial attacks are characterized based on occurrence stage, attacker goals, and capabilities, while categorizing the applications of attack and defense methods in the cyber security domain. It also discusses the impact of recent progress in adversarial learning fields on future research directions in cyber security.

ACM COMPUTING SURVEYS (2021)

Article Telecommunications

The robust deep learning-based schemes for intrusion detection in Internet of Things environments

Xingbing Fu et al.

Summary: With the rise of IoT, network attacks have become more diversified and intelligent, highlighting the importance of IDS for network security. This study tested the robustness of three IDS models and found that CNN was the most robust to adversarial examples under normal training, while GRU and LSTM significantly improved their robustness after adversarial training.

ANNALS OF TELECOMMUNICATIONS (2021)

Article Computer Science, Artificial Intelligence

A survey on adversarial attacks and defences

Anirban Chakraborty et al.

Summary: Deep learning has become a powerful and efficient framework, but faces threats from adversarial samples that can compromise the system. Ensuring robustness to protect deep learning algorithms is crucial to safeguard against potential attacks.

CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY (2021)

Review Engineering, Electrical & Electronic

Adversarial Learning Targeting Deep Neural Network Classification: A Comprehensive Review of Defenses Against Attacks

David J. Miller et al.

PROCEEDINGS OF THE IEEE (2020)

Article Engineering, Multidisciplinary

Adversarial Attacks and Defenses in Deep Learning

Kui Ren et al.

ENGINEERING (2020)

Review Telecommunications

A review on machine learning-based approaches for Internet traffic classification

Ola Salman et al.

ANNALS OF TELECOMMUNICATIONS (2020)

Article Computer Science, Information Systems

Adversarial Attacks and Defenses on Cyber-Physical Systems: A Survey

Jiao Li et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Computer Science, Information Systems

Study of Adversarial Machine Learning with Infrared Examples for Surveillance Applications

DeMarcus Edwards et al.

ELECTRONICS (2020)

Article Computer Science, Software Engineering

Black-box adversarial sample generation based on differential evolution

Junyu Lin et al.

JOURNAL OF SYSTEMS AND SOFTWARE (2020)

Article Computer Science, Theory & Methods

Evaluating Adversarial Evasion Attacks in the Context of Wireless Communications

Bryse Flowers et al.

IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY (2020)

Article Computer Science, Information Systems

A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity

Sicong Zhang et al.

IEEE ACCESS (2020)

Review Computer Science, Information Systems

Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review

Nuno Martins et al.

IEEE ACCESS (2020)

Review Chemistry, Multidisciplinary

Review of Artificial Intelligence Adversarial Attack and Defense Technologies

Shilin Qiu et al.

APPLIED SCIENCES-BASEL (2019)

Article Computer Science, Artificial Intelligence

Adversarial Examples: Attacks and Defenses for Deep Learning

Xiaoyong Yu et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2019)

Article Computer Science, Artificial Intelligence

One Pixel Attack for Fooling Deep Neural Networks

Jiawei Su et al.

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION (2019)

Review Chemistry, Multidisciplinary

Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey

Hongyu Liu et al.

APPLIED SCIENCES-BASEL (2019)

Review Computer Science, Information Systems

A taxonomy and survey of attacks against machine learning

Nikolaos Pitropakis et al.

COMPUTER SCIENCE REVIEW (2019)

Article Computer Science, Artificial Intelligence

Domain-Adversarial Training of Neural Networks

Yaroslav Ganin et al.

DOMAIN ADAPTATION IN COMPUTER VISION APPLICATIONS (2017)

Proceedings Paper Computer Science, Artificial Intelligence

Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection

Manjula C. Belavagi et al.

TWELFTH INTERNATIONAL CONFERENCE ON COMMUNICATION NETWORKS, ICCN 2016 / TWELFTH INTERNATIONAL CONFERENCE ON DATA MINING AND WAREHOUSING, ICDMW 2016 / TWELFTH INTERNATIONAL CONFERENCE ON IMAGE AND SIGNAL PROCESSING, ICISP 2016 (2016)

Article Medicine, General & Internal

Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement

David Moher et al.

SYSTEMATIC REVIEWS (2015)

Article Computer Science, Artificial Intelligence

Security Evaluation of Pattern Classifiers under Attack

Battista Biggio et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2014)