Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

Papadopoulos, Pavlos; Thornewill von Essen, Oliver; Pitropakis, Nikolaos; Chrysoulas, Christos; Mylonas, Alexios; Buchanan, William J.

doi:10.3390/jcp1020014

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Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

Pavlos Papadopoulos

^1,*

Oliver Thornewill von Essen

¹,

^1,*

and

Blockpass ID Lab, School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK

Department of Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK

Authors to whom correspondence should be addressed.

Academic Editor: Nour Moustafa

J. Cybersecur. Priv. 2021, 1(2), 252-273; https://doi.org/10.3390/jcp1020014

Received: 19 February 2021 / Revised: 17 April 2021 / Accepted: 20 April 2021 / Published: 23 April 2021

(This article belongs to the Collection Intelligent Security and Privacy Approaches against Cyber Threats)

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Abstract

As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven Internet of Things market where security is an afterthought. Traditional defending approaches are no longer sufficient to detect both known and unknown attacks to high accuracy. Machine learning intrusion detection systems have proven their success in identifying unknown attacks with high precision. Nevertheless, machine learning models are also vulnerable to attacks. Adversarial examples can be used to evaluate the robustness of a designed model before it is deployed. Further, using adversarial examples is critical to creating a robust model designed for an adversarial environment. Our work evaluates both traditional machine learning and deep learning models’ robustness using the Bot-IoT dataset. Our methodology included two main approaches. First, label poisoning, used to cause incorrect classification by the model. Second, the fast gradient sign method, used to evade detection measures. The experiments demonstrated that an attacker could manipulate or circumvent detection with significant probability. View Full-Text

Keywords: adversarial; machine learning; network IDS; Internet of Things

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MDPI and ACS Style

Papadopoulos, P.; Thornewill von Essen, O.; Pitropakis, N.; Chrysoulas, C.; Mylonas, A.; Buchanan, W.J. Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT. J. Cybersecur. Priv. 2021, 1, 252-273. https://doi.org/10.3390/jcp1020014

AMA Style

Papadopoulos P, Thornewill von Essen O, Pitropakis N, Chrysoulas C, Mylonas A, Buchanan WJ. Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT. Journal of Cybersecurity and Privacy. 2021; 1(2):252-273. https://doi.org/10.3390/jcp1020014

Chicago/Turabian Style

Papadopoulos, Pavlos, Oliver Thornewill von Essen, Nikolaos Pitropakis, Christos Chrysoulas, Alexios Mylonas, and William J. Buchanan. 2021. "Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT" Journal of Cybersecurity and Privacy 1, no. 2: 252-273. https://doi.org/10.3390/jcp1020014

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