Mohamad Nassar, Ph.D.

Mohamad Nassar Image — Assistant Professor

Electrical & Computer Engineering and Computer Science Department
Tagliatela College of Engineering

Education

Bachelor of Engineering, Communication & Computer Engineering, Lebanese University
M.S., Computer Science, University of Lorraine
Doctor of Philosophy, Computer Science, University of Lorraine

About Mohamad

Dr. Mohamad Nassar is a tenure-track assistant professor in computer science and data science at the University of New Haven. He served in a similar position at The University of Alabama in Huntsville (2023-24), UNewHaven (2021-23) and the American University of Beirut (AUB) (2016-21). Before joining AUB, he completed a postdoctoral research stay at the department of computer science and engineering at Qatar University. Nassar received his research master’s degree (DEA) in computer science in 2005 and the Ph.D. degree in 2009, both from Nancy University (currently University of Lorraine), France. He worked as an expert research engineer at INRIA Nancy, France (2009-10) and Ericsson, Ireland (2011). Nassar has published more than 40 peer-reviewed conference and journal articles. He is active in research on AI for cybersecurity and explainable AI.

Publications

[J1] Y. Nasser and M. Nassar, “Toward hardware-assisted malware detection utilizing explainable machine learning: A survey,” IEEE Access, 2023.

[J2] S. A. H. Ibrahim and M. Nassar, “On the security of deep learning novelty detection,” Expert Systems with Applications, vol. 207, p. 117964, 2022.

[J3] E. Chicha, B. A. Bouna, M. Nassar, R. Chbeir, R. A. Haraty, M. Oussalah, D. Benslimane, and M. N. Alraja, “A user-centric mechanism for sequentially releasing graph datasets under blowfish privacy,” ACM Transactions on Internet Technology (TOIT), vol. 21, no. 1, pp. 1–25, 2021.

[J4] M. Nassar, K. Salah, M. H. ur Rehman, and D. Svetinovic, “Blockchain for explainable and trustworthy artificial intelligence,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, p. e1340, 2019.

[J5] K. Dassouki, H. Safa, M. Nassar, and A. Hijazi, “Protecting from cloud-based sip flooding attacks by leveraging temporal and structural fingerprints,” Computers & Security, vol. 70, pp. 618–633, 2017.

[J6] E. Chicha, B. Al Bouna, M. Nassar, and R. Chbeir, “Cloud-based differentially private image classification,” Wireless Networks, pp. 1–8, 2018.

[J7] M. Nassar, Q. Malluhi, M. Atallah, and A. Shikfa, “Securing aggregate queries for dna databases,” IEEE Transactions on Cloud Computing, vol. 7, no. 3, pp. 827–837, 2017.

[J8] T. Jung, S. Martin, M. Nassar, D. Ernst, and G. Leduc, “Outbound spit filter with optimal performance guarantees,” Computer Networks, vol. 57, no. 7, pp. 1630–1643, 2013.

[J9] Y. Rebahi, M. Nassar, T. Magedanz, and O. Festor, “A survey on fraud and service misuse in voice over ip (voip) networks,” information security technical report, vol. 16, no. 1, pp. 12–19, 2011.

Conference publications

[C1] M. Mekni, S. Atilho, B. Greenfield, B. Placzek, and M. Nassar, “Real-time smart parking integration in intelligent transportation systems (its),” in Proceedings of the Future Technologies Conference, pp. 212–236, Springer, 2023.

[C2] C. Barone, M. Mekni, and M. Nassar, “Gargoyle guard: Enhancing cybersecurity with artificial intelligence techniques,” in The Third Intelligent Cybersecurity Conference (ICSC2023), https://www.icsc-conference.org/2023/index.php, 2023.

[C3] K. Samrouth, M. Nassar, and H. Harb, “Revisiting attack trees for modeling machine pwning in training environments,” in The Third Intelligent Cybersecurity Conference (ICSC2023), https://www.icsc-conference.org/2023/index.php, 2023.

[C4] C. S. Jayaramireddy, S. Naraharisetti, S. S. Veera Venkata, M. Nassar, and M. Mekni, “A survey of reinforcement learning toolkits for gaming: Applications, challenges and trends,” in Proceedings of the Future Technologies Conference, pp. 165–184, Springer, Cham, 2023.

[C5] K. L. Pasala, C. S. Jayaramireddy, S. Naraharisetti, S. S. Veera Venkata, S. Atilho, B. Greenfield, B. Placzek, M. Nassar, and M. Mekni, “Smart parking system (sps): An intelligent imageprocessing based parking solution,” in Conference on Sustainable Urban Mobility, pp. 291–299, Springer, 2022.

[C6] T. Edwards, S. McCullough, M. Nassar, and I. Baggili, “On exploring the subdomain of artificial intelligence (ai) model forensics,” in EAI ICDF2C, https://icdf2c.eaiconferences. org/2021/, 2021.

[C7] D. Al Bared and M. Nassar, “Segmentation fault: A cheap defense against adversarial machine learning,” in 2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM), pp. 37–42, IEEE, 2021.

[C8] S. Hajj Ibrahim and M. Nassar, “Hack the box: Fooling deep learning abstraction-based monitors,” in The 2nd Workshop on Artificial Intelligence for Anomalies and Novelties (AI4AN 2021), co-located with IJCAI 2021, 2021.

[C9] M. Nassar, J. Khoury, A. Erradi, and E. Bou-Harb, “Game theoretical model for cybersecurity risk assessment of industrial control systems,” in 2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–7, IEEE, 2021.

[C10] N. M. Farroukh, M. Nassar, S. Elbassuoni, and H. Safa, “Keep it flat (kif): Resource management in integrated cloud-fog networks,” in ICWMC 2021, The Seventeenth International Conference on Wireless and Mobile Communications, no. ISBN: 978-1-61208-878-5, IARIA, 2021.

[C11] M. Nassar, E. Chicha, B. A. Bouna, and R. Chbeir, “Vip blowfish privacy in communication graphs,” in Proceedings of the 17th International Joint Conference on e-Business and Telecommunications, fICETEg, vol. 2, pp. 459–467, Lieusaint, Paris, France, July 8-10, 2020, 2020.

[C12] J. Khoury and M. Nassar, “A hybrid game theory and reinforcement learning approach for cyber-physical systems security,” in NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium, pp. 1–9, IEEE, 2020.

[C13] M. Nassar, A. Itani, M. Karout, M. El Baba, and O. A. S. Kaakaji, “Shoplifting smart stores using adversarial machine learning,” in AICCSA, 2019.

[C14] N. Khan and M. Nassar, “A look into privacy-preserving blockchains,” in 2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA), pp. 1–6, IEEE, 2019.

[C15] M. Nassar, H. Safa, A. A. Mutawa, A. Helal, and I. Gaba, “Chi squared feature selection over apache spark,” in Proceedings of the 23rd International Database Applications & Engineering Symposium, pp. 1–5, 2019.

[C16] M. A. Kadri, M. Nassar, and H. Safa, “Transfer learning for malware multi-classification,” in Proceedings of the 23rd International Database Applications & Engineering Symposium, p. 19, ACM, 2019.

[C17] M. Nassar, B. Rawda, and M. Mardini, “select: Secure election as a service,” in Proceedings of the 23rd International Database Applications & Engineering Symposium, 2019.

[C18] H. Safa, M. Nassar, and W. A. R. Al Orabi, “Benchmarking convolutional and recurrent neural networks for malware classification,” in 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 561–566, IEEE, 2019. Mohamad Nassar Page 6 of 12

[C19] M. Nassar, Q. Malluhi, and T. Khan, “A scheme for three-way secure and verifiable e-voting,” in 15th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2018), 2018.

[C20] M. Nassar and H. Safa, “Throttling malware families in 2d,” in 12th International Conference on Autonomous Infrastructure, Management and Security (IFIP AIMS 2018), http://www.aims-conference.org/2018/program.html, 2018.

[C21] Y. Awad, M. Nassar, and H. Safa, “Modeling malware as a language,” in 2018 IEEE International Conference on Communications (ICC), pp. 1–6, IEEE, 2018.

[C22] H. Bou-Ammar, M. Jaber, and M. Nassar, “Correctness-by-learning of infinite-state component-based systems,” in International Conference on Formal Aspects of Component Software, pp. 162–178, Springer, Cham, 2017.

[C23] M. Jaber, M. Nassar, W. A. R. Al Orabi, B. A. Farraj, M. O. Kayali, and C. Helwe, “Reconfigurable and adaptive spark applications.,” in CLOSER - 7th International Conference on Cloud Computing and Services Science, pp. 84–91, 2017.

[C24] M. Nassar, N. Wehbe, and B. Al Bouna, “K-nn classification under homomorphic encryption: application on a labeled eigen faces dataset,” in 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES), pp. 546–552, IEEE, 2016.

[C25] S. Barakat, B. A. Bouna, M. Nassar, and C. Guyeux, “On the evaluation of the privacy breach in disassociated set-valued datasets,” in Proceedings of the 13th International Joint Conference on e-Business and Telecommunications (ICETE 2016), SECRYPT, Lisbon, Portugal,, vol. 4, 2016.

[C26] M. Nassar, A. Erradi, and Q. M. Malluhi, “Paillier’s encryption: Implementation and cloud applications,” in 2015 International Conference on Applied Research in Computer Science and Engineering (ICAR), pp. 1–5, IEEE, 2015.

[C27] M. Nassar, A. A.-R. Orabi, M. Doha, and B. Al Bouna, “An sql-like query tool for data anonymization and outsourcing,” in 2015 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), pp. 1–3, IEEE, 2015.

[C28] M. Nassar, A. Erradi, and Q. M. Malluhi, “A domain specific language for secure outsourcing of computation to the cloud,” in 2015 IEEE 19th International Enterprise Distributed Object Computing Conference, pp. 134–141, IEEE, 2015.

[C29] F. Sabry, A. Erradi, M. Nassar, and Q. M. Malluhi, “Automatic generation of optimized workflow for distributed computations on large-scale matrices,” in International Conference on Service-Oriented Computing, pp. 79–92, Springer, 2014.

[C30] M. Nassar, A. Erradi, F. Sabry, and Q. M. Malluhi, “A model driven framework for secure outsourcing of computation to the cloud,” in 2014 IEEE 7th International Conference on Cloud Computing, pp. 968–969, IEEE, 2014.

[C31] M. Nassar, B. al Bouna, and Q. Malluhi, “Secure outsourcing of network flow data analysis,” in 2013 IEEE International Congress on Big Data, pp. 431–432, IEEE, 2013.

[C32] S. Wang, M. Nassar, M. Atallah, and Q. Malluhi, “Secure and private outsourcing of shapebased feature extraction,” in International conference on information and communications security, pp. 90–99, Springer, Cham, 2013. Mohamad Nassar Page 7 of 12

[C33] M. Nassar, A. Erradi, and Q. M. Malluhi, “Practical and secure outsourcing of matrix computations to the cloud,” in 2013 IEEE 33rd International Conference on Distributed Computing Systems Workshops, pp. 70–75, IEEE, 2013.

[C34] M. Nassar, A. Erradi, F. Sabri, and Q. M. Malluhi, “Secure outsourcing of matrix operations as a service,” in 2013 IEEE Sixth International Conference on Cloud Computing, pp. 918–925, IEEE, 2013.

[C35] M. Wang, S. B. Handurukande, and M. Nassar, “Rpig: A scalable framework for machine learning and advanced statistical functionalities,” in 4th IEEE International Conference on Cloud Computing Technology and Science Proceedings, pp. 293–300, IEEE, 2012.

[C36] M. Nassar, S. Martin, G. Leduc, and O. Festor, “Using decision trees for generating adaptive spit signatures,” in Proceedings of the 4th international conference on Security of information and networks, pp. 13–20, ACM, 2011.

[C37] R. Do Carmo, M. Nassar, and O. Festor, “Artemisa: An open-source honeypot back-end to support security in voip domains,” in 12th IFIP/IEEE International Symposium on Integrated Network Management (IM 2011) and Workshops, pp. 361–368, IEEE, 2011.

[C38] M. Nassar, O. Dabbebi, R. Badonnel, and O. Festor, “Risk management in voip infrastructures using support vector machines,” in 2010 International Conference on Network and Service Management, pp. 48–55, IEEE, 2010.

[C39] M. Nassar, R. State, and O. Festor, “A framework for monitoring sip enterprise networks,” in 2010 Fourth International Conference on Network and System Security, pp. 1–8, IEEE, 2010.

[C40] M. Nassar, R. State, and O. Festor, “Labeled voip data-set for intrusion detection evaluation,” Networked Services and Applications-Engineering, Control and Management, pp. 97–106, 2010.

[C41] M. Nassar, R. State, and O. Festor, “Voip malware: Attack tool & attack scenarios,” in 2009 IEEE International Conference on Communications, pp. 1–6, IEEE, 2009.

[C42] M. Nassar, R. State, and O. Festor, “Monitoring sip traffic using support vector machines,” in Recent Advances in Intrusion Detection, pp. 311–330, Springer, 2008.

[C43] M. Nassar, S. Niccolini, R. State, and T. Ewald, “Holistic voip intrusion detection and prevention system,” in Proceedings of the 1st international conference on Principles, systems and applications of IP telecommunications, pp. 1–9, 2007.

[C44] M. Nassar, O. Festor, et al., “Ibgp confederation provisioning,” in IFIP International Conference on Autonomous Infrastructure, Management and Security, pp. 25–34, Springer, Berlin, Heidelberg, 2007.

[C45] M. Nassar, O. Festor, et al., “Voip honeypot architecture,” in Integrated Network Management, 2007. IM’07. 10th IFIP/IEEE International Symposium on, pp. 109–118, IEEE, 2007.

[C46] M. Nassar, R. State, and O. Festor, “Intrusion detection mechanisms for voip applications,” in Third annual VoIP security workshop (VSW’06), 2006.

Chapter in book and arxiv

[B1] Y. Rebahi, R. Ruppelt, M. Nassar, and O. Festor, “Scamstop: A platform for mitigating fraud in voip environments,” in Network and Traffic Engineering in Emerging Distributed Computing Applications, pp. 302–325, IGI Global, 2013.

[B2] M. Nassar, “A practical scheme for two-party private linear least squares,” arXiv preprint arXiv:1901.09281, 2019.

�鶹��ý

�鶹��ý Visit Opportunities