Efficient, Lightweight Cyber Intrusion Detection System for IoT Ecosystems Using MI2G Algorithm

• Published in: Computers (Basel) Vol. 11; no. 10; p. 142
• Main Authors: Kaushik, Sunil, Bhardwaj, Akashdeep, Alomari, Abdullah, Bharany, Salil, Alsirhani, Amjad, Mujib Alshahrani, Mohammed
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2022
• Subjects: Accuracy; Algorithms; attacks; Computing costs; cyber; Cybersecurity; Cyberterrorism; Data security; Datasets; Decision trees; Devices; Discriminant analysis; Energy costs; Entropy (Information theory); Feature selection; IDS; Industrial applications; Information theory; Internet; Internet of Things; Intrusion detection systems; IoT; Lightweight; Methods; Neural networks; Prevention; Recall; Safety and security measures; security; Support vector machines; Training; India
• ISSN: 2073-431X; 2073-431X
• DOI: 10.3390/computers11100142

The increase in internet connectivity has led to an increased usage of the Internet of Things (IoT) and devices on the internet. These IoT devices are becoming the backbone of Industry 4.0. The dependence on IoT devices has made them vulnerable to cyber-attacks. IoT devices are often deployed in harsh conditions, challenged with less computational costs, and starved with energy. All these limitations make it tough to deploy accurate intrusion detection systems (IDSs) in IoT devices and make the critical IoT ecosystem more susceptible to cyber-attacks. A new lightweight IDS and a novel feature selection algorithm are introduced in this paper to overcome the challenges of computational cost and accuracy. The proposed algorithm is based on the Information Theory models to select the feature with high statistical dependence and entropy reduction in the dataset. This feature selection algorithm also showed an increase in performance parameters and a reduction in training time of 27–63% with different classifiers. The proposed IDS with the algorithm showed accuracy, Precision, Recall, and F1-Score of more than 99% when tested with the CICIDS2018 dataset. The proposed IDS is competitive in accuracy, Precision, Recall, and training time compared to the latest published research. The proposed IDS showed consistent performance on the UNSWNB15 dataset.