Abstract
With the emphasis on data-driven decisions, the use of Internet of Things (IoT) devices to collect data has increased significantly. Because of the small size, IoT devices can be easily deployed in remote areas where continuous monitoring is not practically possible. To detect these, we have proposed a two-stage intrusion detection technique to identify attacks on the network, such as local access from remote and user to root access. Our proposed model uses the Naive Bayes classifier as the first step of intrusion detection and then passes the records that pretend to be normal for the second stage of the classifier, i.e., the k-means. We have shown results based on NSL-KDD standard data set. The results indicate an accuracy of 86.46% of the proposed approach.
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Vishwakarma, M., Kesswani, N. (2022). A Two-Stage Intrusion Detection System (TIDS) for Internet of Things. In: Troiano, L., et al. Advances in Deep Learning, Artificial Intelligence and Robotics. Lecture Notes in Networks and Systems, vol 249. Springer, Cham. https://doi.org/10.1007/978-3-030-85365-5_9
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DOI: https://doi.org/10.1007/978-3-030-85365-5_9
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