%0 Book Section
%T Machine learning methods for anomaly detection in IoT networks, with illustrations
%+ ESIEE Paris
%+ Laboratoire d'Informatique Gaspard-Monge (LIGM)
%A Bonandrini, Vassia
%A Bercher, Jean-François
%A Zangar, Nawel
%B Boumerdassi S., Renault É., Mühlethaler P. (eds), Machine Learning for Networking, Lecture Notes in Computer Science, vol 12081. Springer
%P 287-295
%8 2020-04-20
%D 2020
%R 10.1007/978-3-030-45778-5_19
%K Internet of Things
%K IoT
%K IDS
%K NIDS
%K Intrusion Detection System
%K rules
%K CIDDS-001
%Z Computer Science [cs]/Artificial Intelligence [cs.AI]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Book sections
%X IoT devices have been the target of 100 million attacks in the first half of 2019 [1]. According to [2], there will be more than 64 billion Internet of Things (IoT) devices by 2025. It is thus crucial to secure IoT networks and devices, which include significant devices like medical kit or autonomous car. The problem is complicated by the wide range of possible attacks and their evolution, by the limited computing resources and storage resources available on devices. We begin by introducing the context and a survey of Intrusion Detection System (IDS) for IoT networks with a state of the art. So as to test and compare solutions, we consider available public datasets and select the CIDDS-001 Dataset. We implement and test several machine learning algorithms and show that it is relatively easy to obtain reproducible results [20] at the state-of-the-art. Finally, we discuss embedding such algorithms in the IoT context and point-out the possible interest of very simple rules.
%G English
%2 https://hal.science/hal-02977813/document
%2 https://hal.science/hal-02977813/file/vassia_ml_paper_final.pdf
%L hal-02977813
%U https://hal.science/hal-02977813
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