Abstract
An Internet of Things (IoT) network is characterized by ad-hoc connectivity and varying traffic patterns where the routing topology evolves over time to account for mobility. In an IoT network, there can be an overwhelming number of massively connected devices, all of which must be able to communicate to each other with low latency to provide a positive user experience. Various protocols exist to allow for this connectivity, and are vulnerable to attack due to their simple nature. These attacks seek to disrupt or deny communications in the network by taking advantage of these vulnerabilities. These attacks include Blackhole, Grayhole, Flooding and Scheduling attacks. Intrusion Detection Systems (IDS) to prevent these routing attacks exist, and have begun to incorporate Deep Learning (DL) to bring near perfect accuracy of detection of attackers. The DL approach opens up the IDS to the possibility of being the victim of an Adversarial Machine Learning attack. We explore the case of a novel evasion attack applied to a Wireless Sensor Network (WSN) dataset for subversion of the IDS. Additionally, we explore possible mitigations for the proposed evasion attack, through adversarial example training, outlier detection, and a combination of the two. By using the combination, we are able to reduce the possible attack space by nearly two orders of magnitude.
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Lurski, N., Younis, M. (2022). Application and Mitigation of the Evasion Attack against a Deep Learning Based IDS for IoT. In: Renault, É., Boumerdassi, S., Mühlethaler, P. (eds) Machine Learning for Networking. MLN 2021. Lecture Notes in Computer Science, vol 13175. Springer, Cham. https://doi.org/10.1007/978-3-030-98978-1_6
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DOI: https://doi.org/10.1007/978-3-030-98978-1_6
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