Decentralized time-window based real-time anomaly detection mechanism (DTRAD) in Iot

Abstract

Detecting intrusions has become a mandatory service in IoT environments. This is due to the power and resource con-strained nature of the networks. This paper presents a Decentralized Time-Window based Anomaly Detection (DTRAD) model for cost and time effective intrusion detection in IoT environments. The proposed model is composed of time win-dow based training data selection module, which enables better detection and reduced bias. Training data are selected based on their temporal significance and the bag creation process is also temporally performed such that data with similar temporal signatures are grouped into same bags. The ensemble model is created and weighted voting is performed to ena-ble better results. The data reinforcement module enables new data to be appended to the training data, hence maintaining the recency of the data. Further, the entire process is decentralized, hence enabling data processing at appropriate nodes. This keeps the size of the training data low, hence reducing the computational complexity of the model to a large extent. Experiments were performed with benchmark data and comparisons were performed with recent models. Results indicate high performance of the proposed models.