2021
DOI: 10.48550/arxiv.2101.09878
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Ajesh Koyatan Chathoth,
Abhyuday Jagannatha,
Stephen Lee

Abstract: Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation. These devices are used for real-world applications such as sensor monitoring, real-time control. In this work, we look at differentially private (DP) neural network (NN) based network intrusion detection systems (NIDS) to detect intrusion attacks on networks of such IoT devices. Existing NN training solutions in this domain either ignore privacy considerations o… Show more

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Cited by 1 publication
(1 citation statement)
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References 27 publications
(68 reference statements)
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“…However, like in the previous cases, privacy-preserving techniques are not integrated. More related to our proposal, [30] recently proposed two DP-based continuous learning methods that consider heterogeneous privacy requirements for different FL clients in an IDS system. However, the approach is based on a non-IoT-specific dataset (CSE-CIC-IDS2018), and different DP techniques are not compared.…”
Section: Related Workmentioning
confidence: 99%