Abstract
The market for Internet of things (IoT) has expanded tremendously. This needs the creation of an ecosystem that is user-friendly and secure. Internet connected IoT devices provide rich user experience and add-on services. Eventually, the IoT devices are interconnected to a management system. Attacking the IoT ecosystem is rising at the same rate as those of the physical attacks and vandalism happening in the IT world. The cyberattack is more severe than the physical attack as it may affect thousands of the IoT devices at the same time. Intrusion Detection is vital in defending diverse types of attacks and unauthorized activities. Fundamentally, the Intrusion Detection System’s (IDS) problem is a classification problem. The IDS tries to find out each traffic stream is legitimate or malicious. Furthermore, the IDS can identify the type of the malicious traffic. This study is a benchmark of different machine learning classifier algorithms for finding fraudulent traffic specifically in the IoT network environment. A typical IoT dataset obtained from actual IoT traffic is used in the study. We compare various classification algorithms that is placed inline to the traffic and contain different types of attacks targeting the IoT network. The obtained results from the different algorithms would improve the stability and significantly reduce the amount of cyberattacks that could disrupt daily living activities in the IoT Environment.
Published Version
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