Publication

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

* Indicates equal contribution.

In the Pipeline

  1. Zhang, H., Ahmed, F. A., Fatih, D., Kitessa, A., Alhanahnah, M., Leitner, P., & Ali-Eldin, A. (2022). Machine Learning Containers are Bloated and Vulnerable. arXiv. https://doi.org/10.48550/ARXIV.2212.09437
  2. Alhanahnah, M. (2020). Software Quality Assessment for Robot Operating System. arXiv. https://doi.org/10.48550/ARXIV.2012.07196

IoT Safety and Privacy

  1. Alhanahnah, M., Stevens, C., & Bagheri, H. (2020). Scalable Analysis of Interaction Threats in IoT Systems. Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, 272–285. https://doi.org/10.1145/3395363.3397347
  2. Chen, Y., Alhanahnah, M., Sabelfeld, A., Chatterjee, R., & Fernandes, E. (2022). Practical Data Access Minimization in Trigger-Action Platforms. 31st USENIX Security Symposium (USENIX Security 22), 2929–2945. https://www.usenix.org/conference/usenixsecurity22/presentation/chen-yunang-practical
  3. Stevens, C., Alhanahnah, M., Yan, Q., & Bagheri, H. (2020). Comparing formal models of IoT app coordination analysis. Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Security from Design to Deployment, 3–10.
  4. Alhanahnah, M., Stevens, C., Chen, B., Yan, Q., & Bagheri, H. (2022). IoTCOM: Dissecting Interaction Threats in IoT Systems. IEEE Transactions on Software Engineering, 1–1. https://doi.org/10.1109/TSE.2022.3179294

Software Debloating

  1. Alhanahnah, M., Jain, R., Rastogi, V., Jha, S., & Reps, T. (2022). Lightweight, Multi-Stage, Compiler-Assisted Application Specialization. 2022 IEEE 7th European Symposium on Security and Privacy (EuroS&P), 251–269. https://doi.org/10.1109/EuroSP53844.2022.00024
  2. Alhanahnah, M., & Yan, Q. (2018). Towards best secure coding practice for implementing SSL/TLS. IEEE INFOCOM 2018 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), 1–6. https://doi.org/10.1109/INFCOMW.2018.8407011

Adversarial ML

  1. Wang, Y., Alhanahnah, M., Meng, X., Wang, K., Christodorescu, M., & Jha, S. (2022). Robust Learning against Relational Adversaries. In A. H. Oh, A. Agarwal, D. Belgrave, & K. Cho (Eds.), Advances in Neural Information Processing Systems. https://openreview.net/forum?id=WBp4dli3No6

Android Security

  1. Alhanahnah, M., Yan, Q., Bagheri, H., Zhou, H., Tsutano, Y., Srisa-An, W., & Luo, X. (2020). DINA: Detecting Hidden Android Inter-App Communication in Dynamic Loaded Code. IEEE Transactions on Information Forensics and Security, 15, 2782–2797. https://doi.org/10.1109/TIFS.2020.2976556
  2. Alhanahnah, M., Yan, Q., Bagheri, H., Zhou, H., Tsutano, Y., Srisa-an, W., & Luo, X. (2019). Detecting Vulnerable Android Inter-App Communication in Dynamically Loaded Code. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, 550–558. https://doi.org/10.1109/INFOCOM.2019.8737637

Cloud Trust

  1. Alhanahnah, M. J., Jhumka, A., & Alouneh, S. (2016). A Multidimension Taxonomy of Insider Threats in Cloud Computing. The Computer Journal, 59(11), 1612–1622. https://doi.org/10.1093/comjnl/bxw020
  2. Alhanahnah, M., Bertok, P., & Tari, Z. (2017). Trusting Cloud Service Providers: Trust Phases and a Taxonomy of Trust Factors. IEEE Cloud Computing, 4(1), 44–54. https://doi.org/10.1109/MCC.2017.20
  3. Alhanahnah, M., Bertok, P., Tari, Z., & Alouneh, S. (2018). Context-Aware Multifaceted Trust Framework For Evaluating Trustworthiness of Cloud Providers. Future Generation Computer Systems, 79, 488–499. https://doi.org/https://doi.org/10.1016/j.future.2017.09.071
  4. Alhanahnah, M., Ma, S., Gehani, A., Ciocarlie, G. F., Yegneswaran, V., Jha, S., & Zhang, X. (2022). autoMPI: Automated Multiple Perspective Attack Investigation with Semantics Aware Execution Partitioning. IEEE Transactions on Software Engineering, 1–14. https://doi.org/10.1109/TSE.2022.3231242
  5. Alhanahnah, M., & Chadwick, D. (2016). Boosting Usability for Protecting Online Banking Applications Against APTs. 2016 Cybersecurity and Cyberforensics Conference (CCC), 70–76. https://doi.org/10.1109/CCC.2016.13