TempoCode-IoT: temporal codebook-based encoding of flow features for intrusion detection in Internet of Things

In the recent years, the Internet of Things has been becoming a vulnerable target of intrusion attacks. As the academia and industry move towards bringing the Internet of Things (IoT) to every sector of our lives, much attention needs to be given to develop advanced Intrusion Detection Systems (IDS) to detect such attacks. In this work, we propose a novel network-based intrusion detection method which learns patterns of benign flows in a temporal codebook. Based on the temporally learnt codebook, we propose a feature representation method to transform the raw flow-based statistical features into more discriminative representations, called TempoCode-IoT. We develop an ensemble of machine learning-based classifiers optimized to discriminate the malicious flows from the benign ones, based on the proposed TempoCode-IoT. The effectiveness of the proposed method is empirically evaluated on a state-of-the-art realistic intrusion detection dataset as well as on a real botnet-infected IoT dataset, achieving high accuracies and low false positive rates across a variety of intrusion attacks. Moreover, the proposed method outperforms several state-of-the-art works based on the used datasets, proving the effectiveness of Tempo-Code-IoT over raw flow features, both in terms of accuracies and processing speeds.