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Subject: search.filters.subject.anomaly detection

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    ItemOpen Access
    Enhancing IoT Resilience: Machine Learning Techniques for Autonomous Anomaly Detection and Threat Mitigation
    (Elsevier B.V., 2025-03-05) Lokuliyana, S; Kalupahanage A.G.A; Herath H.M.S.D; Siriwardana, D; Bulathsinhala D.N.; Herath H.M.T.M
    The explosive growth of the Internet of Things (IoT) has had a substantial impact on daily life and businesses, allowing for real-time monitoring and decision-making. However, increased connectivity also brings higher security risks, such as botnet attacks and the need for stronger user authentication. This research explores how machine learning can enhance Internet of Things security by identifying abnormal activity, utilizing behavioral biometrics to secure cloud-based dashboards, and detecting botnet threats early. Researchers tested numerous machine learning methods, including K-Nearest Neighbors (KNN), Decision Trees, Logistic Regression, and XGBoost on publicly available datasets. The Decision Tree model earned an impressive accuracy rate of 0.73 for anomaly identification, proving its supremacy in dealing with complex security risks, while the XGBoost model demonstrated strong performance with a 92% accuracy rate for detecting TCP SYN flood attacks. Research findings show the effectiveness of these strategies in enhancing the security and reliability of IoT devices. This study provides significant insights into the use of machine learning to protect IoT devices while also addressing crucial concerns such as power consumption and privacy.
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    ItemEmbargo
    Predictive Models for Urban Air Quality Management Using AI
    (Institute of Electrical and Electronics Engineers Inc., 2026-03-19) Liyanage, D; Vithanage, N; Wijewardane, I; Fernando, N; Wijendra, D; Dassanayake, T
    Air pollution threatens public health in datascarce urban areas like Sri Lanka, where sparse monitoring hinders proactive management. We propose an integrated AI framework: hybrid SARIMAX-Temporal Fusion Transformer for multi-pollutant forecasting, ensemble spatial estimation for gap-filling, CEEMDAN-Seq2Seq for 24-hour AQI risk alerting, GRU for anomaly detection, and XAI for transparency. Validated on Central Environmental Authority data (20192024), the model achieves an 81.6% decrease in the value of the RMSE metric for ozone forecasting, as well as an R2 value of 0.9077 for high-risk AQI prediction, outperforming the baseline methods by 15-81%. The framework is modular in nature, thereby providing policymakers with the ability to use real-time dashboards, thus making Sri Lanka move from reactive to proactive management.
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    Bovitrack:Animal behavior monitoring using Machine learning and IoT
    (Institute of Electrical and Electronics Engineers Inc., 2025) Viraj, H; Wijesekara, S; Tharuka, K; Fernando, S; Jayakody, A; Wijesiri, P
    Analyzing dairy cattle behavior and anomalies is a critical component of precision livestock farming, allowing farmers to remotely monitor animals for health and behavior. In order to accomplish this task better, the use of IoT technology and machine learning algorithms is more appropriate as per the time. The YOLO (you only look once) object recognition algorithm is more suitable for that, and the use of this algorithm allows these processes to be performed automatically and in real time with high accuracy. YOLO's ability to recognize multiple objects in images or videos makes Yolo ideal for cattle detection and tracking.