Intelligent Time Series Anomaly Detection in IoT Using Feature Extraction and Hybrid Classification

Intelligent Time Series Anomaly Detection in IoT Using Feature Extraction and Hybrid Classification | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 14 February 2025 V1 Latest version Share on Intelligent Time Series Anomaly Detection in IoT Using Feature Extraction and Hybrid Classification Authors : Sri [email protected] and Srinivasa Rao Bittla Authors Info & Affiliations https://doi.org/10.22541/au.173956861.13125465/v1 240 views 115 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract IoT cyberattacks are becoming more frequent and complicated, threatening individuals and organizations. IoT networks are vulnerable to internal and external cyberattacks because to their openness and self-configuration. DoS attacks are particularly destructive, stopping genuine users from accessing key services. Traditional anomaly detection approaches fail to identify complex temporal correlations and are inaccurate and not robust. This study introduces a feature extraction-based VGGNet model for time series anomaly detection using the Artificial Butterfly Optimization (ABO) algorithm for feature selection and a hybrid Capsule Network (CapsNet) deep learning model for accurate attack classification. VGGNet extracts hierarchical temporal features to improve representation quality, whereas ABO effectively picks the most relevant features to reduce computing cost. The hybrid CapsNet classifier captures spatial and hierarchical connections among selected characteristics to improve anomaly detection accuracy. Experimental results on MSL and PSM time series datasets show high classification accuracy, reduced false alarms, and improved precision-recall metrics, exceeding conventional methods. This scalable, adaptable approach detects anomalies in real time, enabling deep learning-driven cybersecurity solutions. Supplementary Material File (ssrn-5127727.pdf) Download 384.95 KB Information & Authors Information Version history V1 Version 1 14 February 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords anomaly detection artificial butterfly optimization cybersecurity denial of service hybrid capsnet internet of things Authors Affiliations Sri [email protected] View all articles by this author Srinivasa Rao Bittla View all articles by this author Metrics & Citations Metrics Article Usage 240 views 115 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sri, Srinivasa Rao Bittla. Intelligent Time Series Anomaly Detection in IoT Using Feature Extraction and Hybrid Classification. Authorea . 14 February 2025. DOI: https://doi.org/10.22541/au.173956861.13125465/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. 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