Md Hasan Shahriar

Md Hasan Shahriar

PhD Candidate | Graduate Research Assistant | CS @ Virginia Tech | CNSR Lab

Publications

You can also find my articles on my Google Scholar profile.

VehiGAN: Generative Adversarial Networks for Adversarially Robust V2X Misbehavior Detection Systems

Published in ACM Transactions on Cyber-Physical Systems, 2025

This paper is the extension of the ICDCS paper on generative adversarial networks (GAN)-based efficient and adversarially robust MBDS for V2X.

Recommended citation: Shahriar, M. H., Ansari, M. R., Monteuuis, J.-P., Haque, M. S., Chen, C., Petit, J., Hou, Y. T., & Lou, W. (2025). VehiGAN: Generative adversarial networks for adversarially robust V2X misbehavior detection systems. ACM Transactions on Cyber-Physical Systems (ACM TCPS), Just Accepted. https://doi.org/10.1145/3745787
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Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks

Published in 30th European Symposium on Research in Computer Security (ESORICS), 2025, 2025

This paper proposes NoiSec that disentangles the noise from the test input, extracts the underlying features from the noise, and leverages them to recognize systematic malicious manipulation (adversarial or backdoor)

Recommended citation: Shahriar, M. H., Wang, N., Ramakrishnan, N., Hou, Y. T., & Lou, W. (2024). Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks. 30th European Symposium on Research in Computer Security (ESORICS), 2025
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VehiGAN: Generative Adversarial Networks for Adversarially Robust V2X Misbehavior Detection Systems

Published in 44th IEEE International Conference on Distributed Computing Systems (ICDCS), 2024

This paper proposed a generative adversarial networks (GAN)-based efficient and adversarially robust MBDS for V2X.

Recommended citation: Shahriar, M. H., Ansari, M. R., Monteuuis, J.-P., Chen, C., Petit, J., Hou, Y. T., & Lou, W. (2024). VehiGAN: Generative adversarial networks for adversarially robust V2X misbehavior detection systems. In Proceedings of the 44th IEEE International Conference on Distributed Computing Systems (ICDCS).
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Formal Analytics for Stealthy Attacks Against Contingency Analysis in Power Grids

Published in Sustainable Energy, Grids and Networks, 2024

This paper formally model complex attacks on power grid contingency analysis and solve them using Satisfiability Modulo Theories (SMT)

Recommended citation: Shahriar, M. H., Rahman, M. A., Jafari, M., & Paudyal, S. (2024). Formal analytics for stealthy attacks against Contingency Analysis in power grids. Sustainable Energy, Grids and Networks, 38, 101310.
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CANShield: Deep-Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal Level

Published in IEEE Internet of Things Journal, 2023

This paper proposed a deep learning-based signal level intrusion detection framework for the controller area network (CAN) bus.

Recommended citation: Shahriar, M. H., Xiao, Y., Moriano, P., Lou, W., & Hou, Y. T. (2023). CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level. IEEE Internet of Things Journal.
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MS-PTP: Protecting Network Timing from Byzantine Attacks

Published in 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks (WiSec), 2023

This paper present the security vulnerabilities of precision time protocol (PTP) and discuss limitations of existing defenses.

Recommended citation: Shi, S., Xiao, Y., Du, C., Shahriar, M. H., Li, A., Zhang, N., Hou, Y, & Lou, W. (2023). Ms-ptp: Protecting network timing from byzantine attacks. In Proceedings of the 16th ACM Conference on Security and Privacy in Wireless and Mobile Networks (pp. 61-71).
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CANtropy: Time Series Feature Extraction-Based Intrusion Detection Systems for Controller Area Networks

Published in Symposium on Vehicles Security and Privacy (VehicleSec), 2023

This paper proposed a manual feature engineeringbased lightweight IDS for controller area network (CAN) bus.

Recommended citation: Shahriar, M. H., Lou, W., & Hou, Y. T. (2023). CANtropy: Time series feature extraction-based intrusion detection systems for controller area networks. In Proc. of Symp. on Vehicles Security and Privacy (VehicleSec) (pp. 1-8).
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iDDAF: An Intelligent Deceptive Data Acquisition Framework for Secure Cyber-Physical Systems

Published in 17th EAI International Conference on Security and Privacy in Communication Networks (SecureComm), 2021

This paper proposed an intelligent deception defense-based data acquisition framework for hierarchical CPSs network.

Recommended citation: Shahriar, M. H., Rahman, M. A., Haque, N. I., Chowdhury, B., & Whisenant, S. G. (2021). iDDAF: An Intelligent Deceptive Data Acquisition Framework for Secure Cyber-Physical Systems. In Security and Privacy in Communication Networks: 17th EAI International Conference, SecureComm 2021, Virtual Event, September 6–9, 2021, Proceedings, Part II 17 (pp. 338-359). Springer International Publishing.
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iAttackGen: Generative Synthesis of False Data Injection Attacks in Cyber-physical Systems

Published in The 9th IEEE Conference on Communications and Network Security (CNS), 2021

This paper proposed a generative adversarial networks (GAN)-based threat analysis framework for CPS.

Recommended citation: Shahriar, M. H., Khalil, A. A., Rahman, M. A., Manshaei, M. H., & Chen, D. (2021, October). iattackgen: Generative synthesis of false data injection attacks in cyber-physical systems. In 2021 IEEE Conference on Communications and Network Security (CNS) (pp. 200-208). IEEE.
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False Relay Operation Attacks in Power Systems with High Renewables

Published in IEEE Power & Energy Society General Meeting (PESGM), 2021

This paper analyzed the feasibility of launching False Data Injection (FDI) attack in the power systems with high renewables.

Recommended citation: Jafari, M., Shahriar, M. H., Rahman, M. A., & Paudyal, S. (2021, July). False relay operation attacks in power systems with high renewables. In 2021 IEEE Power & Energy Society General Meeting (PESGM) (pp. 01-05). IEEE.
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DDAF: Deceptive Data Acquisition Framework against Stealthy Attacks in Cyber-Physical Systems

Published in EEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), 2021

This paper proposed a deception defense-based data acquisition framework for any hierarchical CPSs network.

Recommended citation: Shahriar, M. H., Rahman, M. A., Haque, N. I., & Chowdhury, B. (2021, July). DDAF: Deceptive Data Acquisition Framework against Stealthy Attacks in Cyber-Physical Systems. In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 725-734). IEEE.
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G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System

Published in IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 2020

This paper proposes a generative adversarial network (GAN)-based intrusion detection system (G-IDS).

Recommended citation: Shahriar, M. H., Haque, N. I., Rahman, M. A., & Alonso, M. (2020, July). G-ids: Generative adversarial networks assisted intrusion detection system. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) (pp. 376-385). IEEE.
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