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        Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT

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        Author
        Papadopoulos, Pavlos
        Essen, Oliver Thornewill von
        Pitropakis, Nikolaos
        Chrysoulas, Christos
        Mylonas, Alexios
        Buchanan, William J.
        Attention
        2299/24485
        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.
        Publication date
        2021-04-23
        Published in
        Journal of Cybersecurity and Privacy
        Published version
        https://doi.org/10.3390/jcp1020014
        Other links
        http://hdl.handle.net/2299/24485
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