Resource-Constraint Deep Forest-Based Intrusion Detection Method in Internet of Things for Consumer Electronic

Abstract

Consumer Electronics (CEs) are smart devices using IoT for connectivity. They're susceptible to attacks like DoS, DDoS, and Web attacks, impairing functions and enabling remote hijacking. Attackers can exploit CEs to target other systems, like vehicles. Malicious code can propagate through networks or CEs, causing vehicle failures. Existing ML/DL based IDS have high classification accuracy and robustness in traditional Internet environments, but they are overly complex for performance improvement, which hinders their deployment in edge small computing environments. Furthermore, the comparison experiments of these intrusion detection algorithms with other algorithms are not sufficiently comprehensive to evaluate their performance in small computing environments. Therefore, balancing 'detection performance and resource consumption' is a key issue in CE network detection. To address this issue, this paper proposes a hybrid feature selection model based on chi-square test and information gain combined Ig-Chi, which effectively reduces the feature dimensionality and improves the classification accuracy of classifiers for high-dimensional data sets. Additionally, layered intrusion detection is employed to perform intrusion detection on the data after feature selection. The experiments on four public data sets demonstrate that this method surpasses six ML/DL algorithms in terms of accuracy and resource indicators.