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An Empirical Comparison of Machine Learning Algorithms for Attack Detection in Internet of Things Edge

This research work is aimed to perform a comparative analysis of different machine learning algorithms for attack detection at the Internet of Things (IoT) edge. Due to the rising development of IoT, attack detection has become extremely important in network security, as it protects the IoT network from suspicious activities. The self-configuring and open nature of IoT devices is vulnerable to both internal and external attacks. The statistical method of attack detection is not suitable for fast and accurate detection due to the multi-dimensional nature of attacks. Machine learning-based edge computing can rectify these issues through automated response and shifting the computation physically closer to the device edge where the information is generated. In this paper, we have compared the performances of eight machine learning (ML) algorithms to identify the optimal ML algorithm for attack detection in IoT Edge.