@inproceedings{10302510,author={Lakha, Bishal and Duran, Jason and Serra, Edoardo and Spezzano, Francesca},booktitle={2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)},title={Prediction of Future Nation-initiated Cyberattacks from News-based Political Event Graph},year={2023},volume={},number={},pages={1-8},doi={10.1109/DSAA60987.2023.10302510},}
@inproceedings{10197836,author={Lakha, Bishal and Bhetwal, Kalyan and Eisty, Nasir U.},booktitle={2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA)},title={Analysis of Software Engineering Practices in General Software and Machine Learning Startups},year={2023},volume={},number={},pages={39-46},doi={10.1109/SERA57763.2023.10197836},}
Springer
Unveiling Truth Amidst the Pandemic: Multimodal Detection of COVID-19 Unreliable News
Royal Pathak, Bishal Lakha, Rohan Raut, and 2 more authors
@inproceedings{Pathak_Lakha_Raut_Kim_Spezzano_2023,address={Cham},title={Unveiling Truth Amidst the Pandemic: Multimodal Detection of COVID-19 Unreliable News},isbn={978-3-031-47896-3},abstractnote={The prevalence of social media as a primary news source raises concerns due to the rapid spread of fake news. A significant majority of Twitter users (59%) and Facebook users (54%) rely on these platforms for their day-to-day news consumption, as observed by the PEW Research Center. This reliance extends to other social media platforms like Reddit, YouTube, and TikTok. The increasing dependence on social media for news has significant impacts, particularly in critical areas such as healthcare during the COVID-19 pandemic, election outcomes, emergency management, and public trust in institutions. To combat the detrimental effects of fake news, computational analysis techniques that incorporate multimodal features are crucial for effective detection and countermeasures. This study proposes a multimodal approach utilizing text embeddings from Fine-tuned BERT and image embeddings from CLIP to detect unreliable news. Experimental results on a ReCOVery COVID-19 dataset demonstrate the model’s superiority over competitive baselines, particularly in detecting unreliable news. The findings highlight the potential of this approach in identifying and mitigating the spread of fake news. By combining text and image embeddings, this research offers a promising strategy for enhancing fake news detection capabilities and fostering trust in news dissemination on social media platforms.},booktitle={Disinformation in Open Online Media},publisher={Springer Nature Switzerland},author={Pathak, Royal and Lakha, Bishal and Raut, Rohan and Kim, Hongmin (Steven) and Spezzano, Francesca},editor={Ceolin, Davide and Caselli, Tommaso and Tulin, Marina},year={2023},pages={119–131},}
@inproceedings{10020336,author={Lakha, Bishal and Mount, Sara Lilly and Serra, Edoardo and Cuzzocrea, Alfredo},booktitle={2022 IEEE International Conference on Big Data (Big Data)},title={Anomaly Detection in Cybersecurity Events Through Graph Neural Network and Transformer Based Model: A Case Study with BETH Dataset},year={2022},volume={},number={},pages={5756-5764},doi={10.1109/BigData55660.2022.10020336},}