No abstract

No abstract

The IoT has been booming in recent years and is evolving rapidly, but attacks against it are also continuing to evolve in a worrying way. In order to take full advantage of these systems, it is worth securing them. Among the greatest security tools to defend IoT against attacks that threaten these low-resource systems (processor, memory, storage, ...), we find Intrusion Detection Systems (IDS). The objective of this paper is to provide a general study on IoT IDS and implementation techniques based on IDS specifically classical methods as well as learning methods.

Recently, connected objects have been the subject of cyber-attacks at an alarming rate. These devices connected to a vast volume data stream have insufficient resources and are not manually configured. Typically, attacks target the usability and exploitation of these vulnerabilities. These attacks make the mission of traditional intrusion detection (IDS) systems more challenging to limit intrusion threats. Machine learning (ML) can solve this problem, mainly since the Internet of Things (IoT) can collect and transfer massive amounts of data. This data is the essence of ML, enabling it to build security and privacy models which can predict or classify malicious nodes and network traffic in the IoT. This article looks at the more common forms of cyberattacks, which could lead to an IoT system failure, as well as a countermeasure capable of limiting their damage. First, we present a general review of IDS and these evaluation measures as a solution to limit these attacks. After reviewing the ML domain and these often-used algorithms, on which the IDS can be based to accomplish its mission, we examine the different datasets researchers use to form their IDS. Finally, we look at a practical example of using Python to evaluate ML methods on a current dataset (TON IoT). The research is based on previous research on the topic. The results enable us to choose the appropriate algorithms for the IDS to achieve the best binary and multi-classification based on the evaluation parameters.

No abstract