A Novel Network Intrusion Detection System Based on Semi-Supervised Approach for IoT

Abstract

An intrusion detection system (IDS) is one of the most effective ways to secure a network and prevent unauthorized access and security attacks. But due to the lack of adequately labeled network traffic data, researchers have proposed several feature representations models over the past three years. However, these models do not account for feature generalization errors when learning semantic similarity from the data distribution and may degrade the performance of the predictive IDS model. In order to improve the capabilities of IDS in the era of Big Data, there is a constant need to extract the most important features from large-scale and balanced network traffic data. This paper proposes a semi-supervised IDS model that leverages the power of untrained autoencoders to learn latent feature representations from a distribution of input data samples. Further, distance function-based clustering is used to find more compact code vectors to capture the semantic similarity between learned feature sets to minimize reconstruction loss. The proposed scheme provides an optimal feature vector and reduces the dimensionality of features, reducing memory requirements significantly. Multiple test cases on the IoT dataset MQTTIOT2020 are conducted to demonstrate the potential of the proposed model. Supervised machine learning classifiers are implemented using a proposed feature representation mechanism and are compared with shallow classifiers. Finally, the comparative evaluation confirms the efficacy of the proposed model with low false positive rates, indicating that the proposed feature representation scheme positively impacts IDS performance