Research Papers - Dept of Computer Systems Engineering

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Author: search.filters.author.Yapa, KSubject: search.filters.subject.Machine Learning

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    PublicationOpen Access
    A cost effective machine learning based network intrusion detection system using Raspberry Pi for real time analysis
    (PLOS ONE, 2025-12-29) Wijethilaka R.W.K.S; Yapa, K; Siriwardena, D
    In an increasingly interconnected world, the security of sensitive data and critical operations is paramount. This study presents the development of a Network Intrusion Detection System (NIDS) that analyzes both inbound and outbound network traffic to detect and classify various cyber attacks. The research begins with an extensive review of existing intrusion detection techniques, highlighting the limitations of traditional methods when addressing the unique security challenges posed by distributed networks. To overcome these limitations, advanced machine learning algorithms, including Random Forest, Long Short Term Memory (LSTM) networks, Artificial Neural Networks (ANN), XGBoost, and Naive Bayes, are employed to create a robust and adaptive intrusion detection system. The practical implementation utilizes a Raspberry Pi as the central processing unit for real time traffic analysis, supported by hardware components such as Ethernet cables, LEDs, and buzzers for continuous monitoring and immediate threat response. A comprehensive alert system is developed, sending email notifications to administrators and activating physical indicators to signify detected threats. Our proposed NIDS achieves 96.5 detection accuracy on the NF-UQ-NIDS dataset, with a significantly reduced false positive rate after applying SMOTE. The system processes real time network traffic with an average response time of 50 milliseconds, outperforming traditional IDS solutions in accuracy and efficiency. Evaluation using the NF-UQ-NIDS dataset demonstrates a significant improvement in detection accuracy and response time, establishing the system as an effective tool for safeguarding networks against emerging cyber threats.
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    PublicationEmbargo
    Human and Organizational Threat Profiling Using Machine Learning
    (IEEE, 2021-12-09) Kumara, P. M. I. N; Dananjaya, K. G. S; Amarasena, N. P. N. H; Pinto, H. M. S; Yapa, K; Rupasinghe, L
    The usage of online social networking sites is increasing rapidly. But the downside is that the growth of various kinds of ongoing social media threats such as fake profiles, cyberbullying, and fake news. Many important observations can be made to increase the existing knowledge about social media threats by studying various information exchanged through public and organizations. One direction is to conduct studies on human behavior and personality traits using public user profile data and the organizational threat classifying. This research aims to build a system to predict human personality behaviors on social media profiles based on the OCEAN Model and company-based threat profiling. All the data collected relating to everyone in the consumer’s friend list is analyzed to obtain the threatening behaviors and classified according to the OCEAN to generate a threat report. Organizational network gathered log data for filtered log protection against malware. Logs received from these endpoints will be collected by collectors. Those logs will be forwarded to our filter, made of a Machine Learning Algorithm (MLA). This will be a custom MLA specially designed for this purpose. MLA will classify and categorize threats according to their severity, filtered log protection system against malware and other threats.