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Author: search.filters.author.Siriwardana, DStart date: 2025

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Now showing 1 - 4 of 4
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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.
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    Fueling the future: unveiling what drives gig worker motivation and engagement in Sri Lanka’s corporate landscape
    (Emerald Publishing, 2025-03-25) Perera, L; Jayasena, C; Hettiarachchi, N; Siriwardana, D; Wisenthige, K; Wickramaarachchi, C
    Purpose: The gig economy has rapidly grown due to economic trends supporting flexible work and digital platforms, leading to increased demand for corporate gig workers. Although numerous studies have explored various aspects of the gig economy, research on the motivational and engagement factors of gig workers remains relatively rare. This study aims to investigate the factors that influence corporate gig workers’ motivation and engagement in the geographical context of Sri Lanka. Specifically, job autonomy, remuneration, social connection and technology and investigated here. Design/methodology/approach: A quantitative study, employing a deductive research approach, was conducted with data gathered through a survey designed using a five-point Likert scale questionnaire. Respondents were conveniently selected from Sri Lankan corporate gig workers. A total of 397 responses were obtained through a snowball sampling technique. The collected data were analyzed using partial least squares structural equation modeling, providing a robust framework for evaluating the hypothesized relationships. Findings: The findings revealed that job autonomy, remuneration, social connection and technology significantly influence corporate gig worker motivation, whereas motivation significantly influences the engagement of corporate gig workers in Sri Lanka. Research limitations/implications: This study faced common limitations. Due to challenges in identifying the framework for the population, a snowball sampling technique was employed. One key limitation is the study’s narrow focus on motivation factors within the Sri Lankan context, which may affect the generalizability of the findings. Additionally, the geographic focus and uneven sample distribution could limit the broader applicability of the conclusions. Future research should adopt a cross-cultural approach to explore the influence of social commerce adoption, enhancing the generalizability of the results. Practical implications: A comprehensive understanding of the factors that influence the corporate gig worker motivation and engagement is provided, facilitating, the decision-makers to gain insight to enhance worker motivation and engagement by adapting strategies. This can lead to higher productivity and job satisfaction among gig workers. Originality/value: Examination of the motivational and engagement factors specific to corporate gig workers in Sri Lanka, a context that has received limited attention in previous research. Also, it contributes to the existing literature by providing a deeper understanding of the gig economy and gig work, particularly in a non-Western setting.