Mabon Ninan

Mabon Ninan

he/him/his

PhD Student at Texas A&M University@[Botacin's Lab]

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

publications

Portability of Deep-Learning Side-Channel Attacks against Software Discrepancies

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
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EvilELF: Evasion Attacks on Deep-Learning Malware Detection over ELF Files

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.
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TinyPower: Side-Channel Attacks with Tiny Neural Networks

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.
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A Second Look at the Portability of Deep Learning Side-Channel Attacks over EM Traces

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.
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MicroPower: Micro Neural Networks for Side-Channel Attacks

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.
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Deep Learning Side-Channel Attacks: Challenges and Opportunities

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.

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

Published in Preprint, Research Square, 2025

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.
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talks

Side-Channel Attacks and Tiny Neural Networks

Published:

Explored the application of tiny neural networks in side-channel attacks, demonstrating their effectiveness in low-resource environments for cryptographic key extraction.

What do malware analysts want from academia? A survey on the state-of-the-practice to guide research developments

Published:

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.

Deep-Learning Side-Channel Attacks

Published:

About the Event

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.

teaching