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Author: search.filters.author.Siriwardana, D

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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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    ML-Based System to Detect GPS Spoofing and Signal Jamming via Signal Logs
    (Institute of Electrical and Electronics Engineers Inc., 2026-08-04) Harshani S.U.E; Wickramasinghe V.D.A; Muthukuda M.A.D.H.N; Siriwardhane H.H.D.V.; Siriwardana, D; Wijesooriya, A
    This paper describes the design, implementation, and analysis of a machine learning-based and forensic-grade desktop system to detect GPS spoofing attacks and signal jamming attacks. The system processes telemetry logs collected from Unmanned Aerial Vehicles (UAVs) and other GNSS-enabled systems to detect any malicious signal manipulations and disruptions. It utilizes a pipeline comprising modules: GPS logging, feature extraction, and an unsupervised machine learning detection engine based on Isolation Forest, One-Class SVM, and LSTM Autoencoder models. The system learns the normal behavioral patterns and is trained on actual GPS data, therefore, identifying the previously unknown attacks. One of the contributions is that forensic concepts, such as hash-SHA-256, chain-of-custody logging, and read-only processing, are factored into the human process, thus supporting evidence integrity and traceability. The system generates elaborate visual and textual reports, giving a user-friendly timeline of the attack with severity ratings. Through the experiment with real and synthetic interfered datasets, the system is found to be effective in predictably distinguishing between spoofing (jumping coordinates and unrealistic kinematics) and jamming (significantly lost signal and large drift variance) with substantial detection. This tool offers an essential feature to cybersecurity forensic investigators, drone operators, and other critical infrastructure defenders to diagnose and record GNSS susceptibility.
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    AgriSense LK: Weekly Automated Machine Learning for Sri Lankan Produce Prices with Business Continuity Plan, Market Opportunity Ranking, Cultivation Targeting, and Yield Quality Valuation
    (Institute of Electrical and Electronics Engineers Inc., 2026-05-22) Matharaarachchi, Charaka J.; Samarasinghe, Ravindu T; Vidyasarani G.G.T.; Fasnas, M; Siriwardana, D; Wijesooriya, A
    In Sri Lanka, agricultural decision-making remains largely traditional: decisions are often based on historical practices, informal consultation, and heuristic judgment. The primary barrier is that market price data is difficult to interpret without analytical expertise, resulting in unpredictable price volatility and suboptimal farmer income. AgriSense LK is a machine learning platform that converts historical price records into actionable recommendations for farmers and traders. The system comprises four components: business strategy classification, market opportunity ranking, cultivation targeting, and smartphone-based produce quality grading. The platform was trained on 123,985 real price records sourced from the Central Bank of Sri Lanka (CBSL), spanning 2017 to 2025. Key results include a MAPE of 0.7% and MAE of Rs. 1.86 on weekly price forecasting (a 98.1% improvement over the naive baseline), a ROC-AUC of 0.9056 on cultivation targeting, and 91.49% crop classification accuracy with 89.84% quality grade accuracy in the computer vision component. Direct price regression over a seven-day horizon proved unreliable; a binary profitability classifier was adopted instead and substantially outperformed the regression approach. While results are promising, further validation under real-world deployment conditions is required.
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    Project HyperAdapt: An Agent-Based Intelligent Sandbox Design to Deceive and Analyze Sophisticated Malware
    (Institute of Electrical and Electronics Engineers Inc., 2025) Perera, S; Dias, S; Vithanage, V; Dilhara, A; Senarathne, A; Siriwardana, D; Liyanapathirana, C
    Malware increasingly employs sophisticated evasion techniques to bypass sandbox-based analysis, rendering traditional detection methods ineffective. This research presents Project HyperAdapt: Agent-Based Intelligent Sandbox, a framework that integrates both offensive and defensive machine learning models to enhance malware detection, deception, and behavioral analysis. The offensive RL model generates evasive malware samples, challenging the sandbox, while the defensive models including hybrid evasion detection, GAN-based behavior simulation, and a dynamically adapting RL agent work collectively to improve sandbox resilience. By continuously learning from evasive malware behavior, the defensive RL agent adapts in real-time, strengthening detection capabilities. Experimental results demonstrate that this approach enhances sandbox effectiveness, ensuring long-term adaptability against evolving malware threats.