Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
Browse
3 results
Search Results
Publication Open Access Hybrid ABC–HBA feature optimization with self-training using simulated unlabelled data for robust intrusion detection(Elsevier Ltd, 2026) Harischandra, S; Rajapaksha, U.U. S; Silva, B.N; Jayawardena, CThe increasing scale and heterogeneity of network traffic pose significant challenges for intrusion detection systems (IDS), particularly in detecting extremely rare attack classes and generalising to previously unseen threats under severe class imbalance. This study proposes a hybrid intrusion detection framework that integrates swarm intelligence–based feature optimisation with self-training using unlabelled data simulation to address these limitations. A novel ABC–HBA feature selection strategy is introduced, combining the efficient exploration capability of the Artificial Bee Colony (ABC) algorithm with the strong global exploitation and fast convergence of the Honey Badger Algorithm (HBA), resulting in a highly discriminative and compact feature subset. A Random Forest(RF) classifier augmented with a pseudo-labelling mechanism is then employed to enhance learning from unlabelled and unseen attack samples, enabling effective detection of novel attack patterns absent from the training set. To further mitigate extreme class imbalance, a hybrid resampling strategy is applied. Experimental evaluation on the KDD Cup 1999 dataset demonstrates that the proposed framework achieves an overall accuracy of 99.95% and a detection rate of 98.16%, while significantly improving the recognition of extremely rare attack classes, including a 92.86% detection rate for U2R attacks, which constitute less than 0.01% of the dataset. The proposed method consistently outperforms baseline RF, ABC-based, and several other state-of-the-art meta-heuristic and deep learning approaches, confirming its effectiveness in enhancing rare attack detection and generalisation to unseen threats in realistic intrusion detection scenarios.Publication Embargo High-resolution optical imaging for sustainable fish freshness and safety assessment(Elsevier GmbH, 2026-04) Madhubhashini, M. N; Kahandawala, B.S; Sandaruwan, H.H.P. B; Silva, B.N; Wijenayake, U; Wijesinghe, R.EFish freshness evaluation is crucial to ensure consumer safety, and rapid assessment is essential for effective and accurate quality control. To overcome the limitations of the gold standards, such as lack of structural depth information, high-time consumption, and labor-intensiveness, high-resolution Optical Coherence Tomography (OCT) was employed for real-time monitoring of fish freshness non-invasively. Microstructural changes of eye and skin of Indian Anchovies ( Stolephorus indicus ) specimens were considered as the main freshness parameters during refrigeration storage. Both eye and skin tissues exhibited decreased internal scattering, loss of clarity, boundary weakening, and gradual structural degradations through the OCT observations. The quantitatively assessed variance intensity, entropy, energy, and edge density clearly revealed the internal tissue disruption over storage time due to protein denaturation, oxidative damage, and fluid imbalance. The findings of this study indicate that OCT shows an insightful correlation with microbiological and biochemical spoilage processes, enabling the advanced identification of subtle microstructural changes in fish skin and eye, even at a prior stage of deterioration. Such capability offers an objective and rapid freshness evaluation approach that could greatly benefit supply chain management and post-harvest seafood quality monitoring.Publication Embargo Brewing plastics: OCT reveals microplastic release from nylon tea bags in simulated brewed tea infusions(Royal Society of Chemistry, 2026-02-12) Jayasekara, P.M; Abhishek, P; Kahandawala,B.S; Damith, N; Weerasinghe, M; Kahatapitiya, N.S; Silva, B.N; Karunaratne, S; Wijesinghe, R.E; Wijenayake, UThe release of microplastics (MPs) from nylon tea bags poses a critical concern for human exposure; however,their detection and quantification remain challenging especially in beverage matrices, and hence, this study pioneers the use of high-resolution optical coherence tomography (OCT) integrated with an image processing algorithm to rapidly detect and quantify the size and count of the MPs directly in the water extractions simulating tea brewing. The water extractions prepared by simulating tea brewing conditions, hot (100 °C, 1–5min), cold (2 °C, 1 h), and ambient (30 °C, 1 h), were observed employing OCT imaging and validated through Nile Red (NR) staining and digital microscopy. The nylon tea bags steeped in hot water for 5 minutes released 16 000 to 24 000 LMPs (>30 mm) and SMPs (12–30 mm) per millilitre. The estimated daily intake (EDI) of MPs indicates a higher exposure for children (ranging from 0.201 to 0.349 mm3 kg−1 day−1 ) compared to adults (0.046 to 0.080 mm3 kg−1 day−1 ). In contrast, cold brewing for 1 hour released fewer LMPs but an equal quantity of small MPs (SMPs) compared to hot brewing. This OCT-based approach offers a rapid, versatile platform for the detection and quantification of MPs from diverse packaging materials and provides a powerful tool for comprehensive risk assessment when combined with chemical and toxicological analyses.