Shenify, M.; Mazarbhuiya, F.A.; Wungreiphi, A.S. Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms. Appl. Sci.2024, 14, 1264.
Shenify, M.; Mazarbhuiya, F.A.; Wungreiphi, A.S. Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms. Appl. Sci. 2024, 14, 1264.
Shenify, M.; Mazarbhuiya, F.A.; Wungreiphi, A.S. Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms. Appl. Sci.2024, 14, 1264.
Shenify, M.; Mazarbhuiya, F.A.; Wungreiphi, A.S. Detecting IoT Anomalies Using Fuzzy Subspace Clustering Algorithms. Appl. Sci. 2024, 14, 1264.
Abstract
There are many applications of anomaly detection in IoT domain. IoT technology consists of large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to Internet, they frequently meet with the challenges of illegitimate accesses in the form of intrusions, anomaly, fraud, etc. Identifying these illegitimate accesses in IoT domain can be an exciting research problem. In numerous applications fuzzy clustering and rough set theory have been successfully employed. As the data generated in IoT domains are high-dimensional, the clustering methods used for lower dimensional data cannot be applied efficiently. In this article, mixed approaches consisting of nano topology and fuzzy clustering techniques are proposed for anomaly detection. First of all, the nano topology is generated to find lower dimensional apace and then a couple of well-known fuzzy clustering techniques are employed on it for the efficient anomaly detection. The effectiveness of the proposed approaches is evaluated using time-complexity analysis, experimental studies with a synthetic dataset and a real-life dataset along with comparative studies with traditional fuzzy clustering approaches namely fuzzy c-means clustering (FCM) algorithm, Gustafson-Kessel (GK) Algorithm, Gath-Geva (GG) Algorithm, Mahalanobis Distance based Fuzzy C-Means algorithm (M-FCM), and Common Mahalanobis Distance based Fuzzy C-Means algorithm (CM-FCM). Experimentally, it has been found that the proposed approaches outperform the aforesaid algorithms in terms of detection rates, accuracy rates, false alarm rates and computational times.
Keywords
Anomaly detection; Information system; High-dimensional data; Dominance relation; Fuzzy Clustering method, CORE of attribute set; Mahalanobis distance.
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.
