An analysis of intrusion detection techniques for IoT network

The Internet of Things (IoT) is a rapidly expanding system with more and more devices connecting to each other. This interconnectedness presents attackers with an ever-growing range of opportunities to launch malicious activities, such as Distributed Denial of Service (DDoS) attacks, unauthorized access, and data theft. To address these threats, organizations must ensure that their IoT networks are adequately protected. Intrusion Detection Systems (IDSs) can play an important role in providing such protection by detecting and alerting organizations to suspicious or malicious activities. This paper provides an analysis of various IDS techniques for IoT networks. The paper starts with a general overview of the IoT and its security risks. It then examines the different types of IDS techniques, such as signature-based, anomaly-based, and machine learning-based techniques, and discusses their relevance to IoT networks. Finally, the paper assesses the effectiveness of each IDS technique and highlights its potential benefits and limitations.

کلیدواژه ها:

Intrusion Detection System ، Machine Learning ، Internet of Things