Article
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Finding IoT Anomaly using Rough Fuzzy Periodic Subspace Clustering Approach
Version 1
: Received: 22 May 2023 / Approved: 23 May 2023 / Online: 23 May 2023 (12:00:44 CEST)
A peer-reviewed article of this Preprint also exists.
Mazarbhuiya, F.A.; Shenify, M. Real-Time Anomaly Detection with Subspace Periodic Clustering Approach. Appl. Sci. 2023, 13, 7382. Mazarbhuiya, F.A.; Shenify, M. Real-Time Anomaly Detection with Subspace Periodic Clustering Approach. Appl. Sci. 2023, 13, 7382.
Abstract
Finding anomalies in the real-time system is recognized as one of most challenging study in information security. It has so many applications like IoT, and Stock-Market. In any IoT system the data generated are real-time, and temporal in nature. Since due to the extreme exposure to Internet and interconnectivity of devices, the IoT systems often face issues like fraud, anomalies, intrusions etc. Discovering anomaly in such domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time-stamp associated with IoT data, time-dependent patterns like periodic clusters can be generated which could be helpful for the efficient detection of anomalies by providing more in-depth analysis of the system. In this paper, a mixed method comprising of nano topology, a modified k-means clustering and an interval superimposition technique is used for finding fuzzy periodic clusters in the subspace generated by the nano topology. For every clusters there will be an associated sequence of time-intervals where it exists. The sequence time-intervals accompanying with each clusters may exhibit some remarkable patterns. For example, there may exist different types of periodicity namely yearly, monthly, daily, and hourly etc. For finding such fuzzy periodicity, an operation called interval-superimposition has been used. The time-intervals associated with each cluster are superimposed if they have reasonable overlapping. Each superimposed time-interval generates a fuzzy time-interval. The data instances are thought to be anomalous if they either belong to sparse clusters or don't 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 with a real-life and a synthetic dataset. It can been found experimentally that our method can extract anomaly with 98% of accuracy and it runs cubic time approximately.
Keywords
Anomaly detection; Information system; High-dimensional data; Dominance relation; CORE of attribute set; Distance function; k-means algorithm
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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