Generative adversarial attacks against intrusion detection systems using active learning

Shu, Dule; Leslie, Nandi; Kamhoua, Charles A.; Tucker, Conrad S.

doi:10.1145/3395352.3402618

Cited by 42 publications

(18 citation statements)

References 22 publications

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“…Problems of labelling data and retraining systems provide an impetus to explore unsupervised and active learning. Unfortunately adversarial attacks are possible on active learning systems [146]. Lin et al [76] describe an enchanting attack to lure a machine learning system to a target state through crafting a series of adversarial examples.…”

Section: Takeawaysmentioning

confidence: 99%

Functionality-preserving Adversarial Machine Learning For Robust Classification In Cybersecurity And Intrusion Detection Domains: A Survey

McCarthy¹,

Ghadafi²,

Andriotis³

et al. 2022

Preprint

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Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introduce vulnerabilities to a security defence whereby a learnt model is unaware of so-called adversarial examples that may intentionally result in mis-classification and therefore bypass a system. Adversarial machine learning has been a research topic for over a decade and is now an accepted but open problem. Much of the early research on adversarial examples has addressed issues related to computer vision, yet as machine learning continues to be adopted in other domains, then likewise it is important to assess the potential vulnerabilities that may occur. A key part of transferring to new domains relates to functionality-preservation, such that any crafted attack can still execute the original intended functionality when inspected by a human and/or a machine. In this literature survey, our main objective is to address the domain of adversarial machine learning attacks and examine the robustness of machine learning models in the cybersecurity and intrusion detection domains. We identify the key trends in current work observed in the literature, and explore how these relate to the research challenges that remain open for future works. Inclusion criteria were: articles related to functionality-preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification. Generally, we excluded works that are not yet peer-reviewed; however, the authors include some significant papers that make a clear contribution to the domain. There is a risk of subjective bias in the selection of non-peer reviewed articles; however, this is mitigated by co-author review. We selected the following databases with a sizeable computer science element to search and retrieve literature: IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, SpringerLink,Google Scholar. The literature search was conducted upto January 2022. We have striven to ensure a comprehensive coverage of the domain to the best of our knowledge. We have performed systematic searches of the literature, noting our search terms and results, and following up on all materials that appear relevant and fit within the topic domains of this review. This research was funded by the Partnership PhD scheme at the University of the West of England in collaboration with Techmodal Ltd.

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Section: Takeawaysmentioning

confidence: 99%

Functionality-preserving Adversarial Machine Learning For Robust Classification In Cybersecurity And Intrusion Detection Domains: A Survey

McCarthy¹,

Ghadafi²,

Andriotis³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Shu et al [30] introduced the Generative Adversarial Active Learning (Gen-AAL) algorithm that utilizes GANs with active learning to craft adversarial attacks against black-box MLbased NIDS. It requires a limited number of queries to the targeted model for labeled instances to train the GANs and does not demand a large training dataset.…”

Section: A Generation Of Aes To Attack Ml-based Nids Modelsmentioning

confidence: 99%

Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review

Alatwi¹,

Morisset²

2021

Preprint

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“…After only 400 epochs, they were able to reduce the traffic blocking percentage to zero. Similarly, in [119], the authors used a GAN with active learning to generate successful adversarial traffic with minimal training labels.…”

Section: Evasion Techniquesmentioning

confidence: 99%

Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective

Xiong,

Buffett,

Iqbal

et al. 2021

Preprint

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Machine Learning (ML) technologies have been widely adopted in many mission critical fields, such as cyber security, autonomous vehicle control, healthcare, etc. to support intelligent decision-making. While ML has demonstrated impressive performance over conventional methods in these applications, concerns arose with respect to system resilience against ML-specific security attacks and privacy breaches as well as the trust that users have in these systems. In this article, firstly we present our recent systematic and comprehensive survey on the state-of-the-art ML robustness and trustworthiness technologies from a security engineering perspective, which covers all aspects of secure ML system development including threat modeling, common offensive and defensive technologies, privacy-preserving machine learning, user trust in the context of machine learning, and empirical evaluation for ML model robustness. Secondly, we then push our studies forward above and beyond a survey by describing a metamodel we created that represents the body of knowledge in a standard and visualized way for ML practitioners. We further illustrate how to leverage the metamodel to guide a systematic threat analysis and security design process in a context of generic ML system development, which extends and scales up the classic process. Thirdly, we propose future research directions motivated by our findings to advance the development of robust and trustworthy ML systems. Our work differs from existing surveys in this area in that, to the best of our knowledge, it is the first of its kind of engineering effort to (i) explore the fundamental principles and best practices to support robust and trustworthy ML system development; and (ii) study the interplay of robustness and user trust in the context of ML systems.CCS Concepts: • Computing methodologies → Machine learning; • Security and privacy → Software and application security; Software security engineering; • Human-centered computing → HCI design and evaluation methods.

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Generative adversarial attacks against intrusion detection systems using active learning

Cited by 42 publications

References 22 publications

Functionality-preserving Adversarial Machine Learning For Robust Classification In Cybersecurity And Intrusion Detection Domains: A Survey

Functionality-preserving Adversarial Machine Learning For Robust Classification In Cybersecurity And Intrusion Detection Domains: A Survey

Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review

Towards a Robust and Trustworthy Machine Learning System Development: An Engineering Perspective

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