Machine Learning Based Prediction of Malicious Intrusions in IoT Empowered Cybersecurity Application

Since the introduction of the Internet of Things, the security of the IoT environment has become the most critical concern. Vulnerabilities related to malicious intrusions are major impacts to the IoT systems, impacting the aspects of their integrity, confidentiality and availability. This paper presents an overview of ML methods that may be used to identify and forecast such intrusions, making it easier to prevent future cyber threats. The current paper offers a review of ML based prediction models for intrusion detection for IoT systems. We discuss several ML classification algorithms, feature extraction techniques, databases, performance measures and some of the research issues found in literature. Further, we turn our focus to how ML can further improve cybersecurity tools for IoT contexts and provide suggestions for future research areas to counter new challenges.