Published in 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '23), 2023
Recommended citation: Chenggang Wang, Mabon Ninan, Shane Reilly, Joel Ward, William Hawkins, Boyang Wang, and John M. Emmert. 2023. Portability of Deep-Learning Side-Channel Attacks against Software Discrepancies. In Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec '23). Association for Computing Machinery, New York, NY, USA, 227-238. https://doi.org/10.1145/3558482.3590177 Download Paper
Published in 2023 International Conference on Machine Learning and Applications (ICMLA), 2023
Recommended citation: A. Kosikowski, D. Cho, M. Ninan, A. Ralescu and B. Wang, 'EvilELF: Evasion Attacks on Deep-Learning Malware Detection over ELF Files,' 2023 International Conference on Machine Learning and Applications (ICMLA), Jacksonville, FL, USA, 2023, pp. 1702-1709, doi: 10.1109/ICMLA58977.2023.00258. Download Paper
Published in 2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2024
Recommended citation: H. Li, M. Ninan, B. Wang and J. M. Emmert, 'TinyPower: Side-Channel Attacks with Tiny Neural Networks,' 2024 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), Tysons Corner, VA, USA, 2024, pp. 320-331, doi: 10.1109/HOST55342.2024.10545382. Download Paper
Published in 27th International Symposium on Research in Attacks, Intrusions and Defenses (RAID '24), 2024
Recommended citation: M. Ninan, E. Nimmo, S. Reilly, C. Smith, W. Sun, B. Wang, and J. M. Emmert, 'A Second Look at the Portability of Deep Learning Side-Channel Attacks over EM Traces,' Proceedings of the 27th International Symposium on Research in Attacks, Intrusions and Defenses (RAID '24), Padua, Italy, 2024, pp. 630-643. doi: 10.1145/3678890.3678900. Download Paper
Published in 2025 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), 2025
Recommended citation: L. Reichling, R. Evans, M. Ninan, P. Mai, B. Wang, Y. Fei, and J. M. Emmert, "MicroPower: Micro Neural Networks for Side-Channel Attacks," in Proc. of the 2025 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp. 462-473, 2025. DOI: 10.1109/HOST64725.2025.11050048. Download Paper
Published in Advancements in Hardware Design and Trust, CRC Press (ISBN 9781032840420), 2025
Recommended citation: L. Reichling, M. Ninan, B. Wang, and J. M. Emmert, 'Deep Learning Side-Channel Attacks: Challenges and Opportunities,' in Advancements in Hardware Design and Trust, CRC Press, 2025. ISBN 9781032840420.
Recommended citation: S. Hegde, M. Ninan, J. R. Dillman, S. Hayatghaibi, L. Babcock, and E. Somasundaram, 'Can Modern NLP Systems Reliably Annotate Chest Radiography Exams? A Pre-Purchase Evaluation and Comparative Study of Solutions from AWS, Google, Azure, John Snow Labs, and Open-Source Models on an Independent Pediatric Dataset,' Research Square preprint, 2025. doi: 10.21203/rs.3.rs-6772394/v1. Download Paper
Published in IEEE National Aerospace and Electronics Conference (NAECON 2025), 2025
Recommended citation: M. Ninan, R. Evans, L. Reichling, N. Ghose, and B. Wang, 'TinyRadio: Tiny Neural Networks for Fingerprinting Radio Frequency Signals,' IEEE National Aerospace and Electronics Conference (NAECON), 2025. doi: 10.1109/NAECON65708.2025.11235437. Download Paper
Published in Journal of Imaging Informatics in Medicine, 2026
Recommended citation: S. Hegde, M. M. Ninan, J. R. Dillman, S. Hayatghaibi, and E. Somasundaram, 'Evaluating Clinical NLP Services for Chest Radiograph Report Labeling: A Comparative Study on an Independent Pediatric Dataset,' Journal of Imaging Informatics in Medicine, 2026.
Investigated the robustness of deep-learning-based side-channel attacks across different software implementations, analyzing performance variations in diverse execution environments.
Presented research on leveraging tiny neural networks for side-channel attacks, highlighting their potential to extract cryptographic keys with minimal computational overhead.
Explored the application of tiny neural networks in side-channel attacks, demonstrating their effectiveness in low-resource environments for cryptographic key extraction.
Discussed federated learning techniques for malware detection across different regions, focusing on enhancing model generalization while maintaining data privacy.
Malware analysis tasks are as fundamental for modern cybersecurity as they are challenging to perform. More than depending on any tool capability, malware analysis tasks depend on human analysts’ abilities, experiences, and practices when using the tools. Academic research has traditionally been focused on producing solutions to overcome malware analysis technical challenges, but are these solutions adopted in practice by malware analysts? Are these solutions useful? If not, how can the academic community improve its practices to foster adoption and cause a greater impact? To answer these questions, we surveyed 21 professional malware analysts working in different companies, from CSIRTs to AV companies, to hear their opinions about existing tools, practices, and the challenges they face in their daily tasks. In 31 questions, we cover a broad range of aspects, from the number of observed malware variants to the use of public sandboxes and the tools the analysts would like to exist to make their lives easier. We aim to bridge the gap between academic developments and malware practices. To do so, on the one hand, we suggest to the analysts the solutions proposed in the literature that could be integrated into their practices. On the other hand, we also point out to the academic community possible future directions to bridge existing development gaps that significantly affect malware analysis practices.
Presented research analyzing the portability of deep learning-based side-channel attacks across different electromagnetic traces, highlighting cross-device effectiveness and challenges.
The AI for Cybersecurity Research Lunch is a weekly event at Texas A&M University where students and researchers present their latest work in cybersecurity and AI. Talks are open to all, but regular attendees are expected to present their research at least once.
Undergraduate course, University of Cincinnati, Learning Commons, 2022
Facilitated and led interactive group learning sessions for Calculus-based Physics 2.
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