Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

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

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Article number1020014
Pages (from-to)252-273
Number of pages22
JournalJournal of Cybersecurity and Privacy
Volume1
Issue number2
Early online date23 Apr 2021
DOIs
Publication statusPublished - Jun 2021

Keywords

  • adversarial
  • Internet of Things
  • machine learning
  • network IDS

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