Soft computing for anomaly detection and prediction to mitigate IoT-based real-time abuse

Cyber-surveillance and connected devices can be misused to monitor, harass, isolate, and otherwise, harm individuals. In particular, these devices gather high volumes of personal data such as account details with shared passwords, person’s behavior and preferences, movements by GPS, and audio-video recordings which can be maneuvered. It is therefore imperative to define approaches that help mitigate the Internet of things (IoT)-based real-time abuse in a pro-active, reactive, or predictive manner. The key objective of this research is to outline and categorize such approaches. Further, to comprehend predictive analytics as a potential solution to mitigate technology abuse, we propose an anomaly detection methodology (MF_EW_Bagging) to classify normal and abnormal use pattern categories in an Intrusion Detection System (IDS) for IoT system. A hybrid feature selection technique based on an ensemble of multiple filter–based techniques and a wrapper algorithm is firstly used as search method for finding an optimal feature subset. Further, ensemble learning technique, namely bagging, is used for final classification into normal and abnormal use pattern categories. The use of ensemble feature selection removes biasness of individual feature selection method during ensemble and identifies the optimal subset with non-redundant and relevant features. The proposed methodology is evaluated on publicly available real-time IDS dataset. The research persuades the need of designing robust and lightweight IDS for IoT-based smart environments which understand the cyber-security risks in a proactive predictive manner as it the best way to defend networks and systems with the growing IoT complexity.