Addressing Internet of Things security by enhanced sine cosine metaheuristics tuned hybrid machine learning model and results interpretation based on SHAP approach

• Published in: PeerJ. Computer science Vol. 9; p. e1405
• Main Authors: Dobrojevic, Milos, Zivkovic, Miodrag, Chhabra, Amit, Sani, Nor Samsiah, Bacanin, Nebojsa, Mohd Amin, Maifuza
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 30.06.2023; PeerJ Inc
• Subjects: Adaptive and Self-Organizing Systems; Algorithms; Analysis; Artificial Intelligence; CNN; Hyperparameter optimization; Intrusion detection; Machine learning; Metaheuristics optimization; Neural Networks; Optimization Theory and Computation; Safety and security measures; Sine cosine algorithm; CNN; SHAP; Intrusion detection; ELM; Machine learning; Metaheuristics optimization; Hyperparameter optimization; Sine cosine algorithm; Internet of Things
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.1405

An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e ., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.