Real-Time Anomaly Detection with Subspace Periodic Clustering Approach

Finding real-time anomalies in any network system is recognized as one of the most challenging studies in the field of information security. It has so many applications, such as IoT and Stock Markets. In any IoT system, the data generated is real-time and temporal in nature. Due to the extreme exposure to the Internet and interconnectivity of the devices, such systems often face problems such as fraud, anomalies, intrusions, etc. Discovering anomalies in such a domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time stamp associated with the data, time-dependent patterns including periodic clusters can be generated, which could be helpful for the efficient detection of anomalies by providing a more in-depth analysis of the system. Another issue related to the aforesaid data is its high dimensionality. In this paper, all the issues related to anomaly detection are addressed, and a clustering-based approach is proposed for finding real-time anomalies. The method employs rough set theory, a dynamic k-means clustering algorithm, and an interval superimposition approach for finding periodic, partially periodic, and fuzzy periodic clusters in the subspace of the dataset. The data instances are thought to be anomalous if they either belong to sparse clusters or do not belong to any clusters. The efficacy of the method can be assessed by means of both time-complexity analysis and comparative studies with existing clustering-based anomaly detection algorithms on a synthetic and a real-life dataset. It can be found experimentally that our method outperforms others and runs in cubic time.