A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection

• URL: http://arxiv.org/abs/2111.13149v1
• Date: Thu, 25 Nov 2021 16:14:54 GMT
• Title: A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection
• Authors: Jo\~ao Vitorino, Rui Andrade, Isabel Pra\c{c}a, Orlando Sousa, Eva Maia
• Abstract summary: This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQN)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The digital transformation faces tremendous security challenges. In particular, the growing number of cyber-attacks targeting Internet of Things (IoT) systems restates the need for a reliable detection of malicious network activity. This paper presents a comparative analysis of supervised, unsupervised and reinforcement learning techniques on nine malware captures of the IoT-23 dataset, considering both binary and multi-class classification scenarios. The developed models consisted of Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Isolation Forest (iForest), Local Outlier Factor (LOF) and a Deep Reinforcement Learning (DRL) model based on a Double Deep Q-Network (DDQN), adapted to the intrusion detection context. The best performance was achieved by LightGBM, closely followed by SVM. Nonetheless, iForest displayed good results against unknown attacks and the DRL model demonstrated the possible benefits of employing this methodology to continuously improve the detection. Overall, the obtained results indicate that the analyzed techniques are well suited for IoT intrusion detection.

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