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 四色AV 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鈥檚 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, 鈥淭oward hardware-assisted malware detection utilizing explainable machine learning: A survey,鈥 IEEE Access, 2023.

[J2] S. A. H. Ibrahim and M. Nassar, 鈥淥n the security of deep learning novelty detection,鈥 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, 鈥淎 user-centric mechanism for sequentially releasing graph datasets under blowfish privacy,鈥 ACM Transactions on Internet Technology (TOIT), vol. 21, no. 1, pp. 1鈥25, 2021.

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

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

[J6] E. Chicha, B. Al Bouna, M. Nassar, and R. Chbeir, 鈥淐loud-based differentially private image classification,鈥 Wireless Networks, pp. 1鈥8, 2018.

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

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

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

Conference publications

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

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

[C3] K. Samrouth, M. Nassar, and H. Harb, 鈥淩evisiting attack trees for modeling machine pwning in training environments,鈥 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, 鈥淎 survey of reinforcement learning toolkits for gaming: Applications, challenges and trends,鈥 in Proceedings of the Future Technologies Conference, pp. 165鈥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, 鈥淪mart parking system (sps): An intelligent imageprocessing based parking solution,鈥 in Conference on Sustainable Urban Mobility, pp. 291鈥299, Springer, 2022.

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

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

[C8] S. Hajj Ibrahim and M. Nassar, 鈥淗ack the box: Fooling deep learning abstraction-based monitors,鈥 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, 鈥淕ame theoretical model for cybersecurity risk assessment of industrial control systems,鈥 in 2021 11th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pp. 1鈥7, IEEE, 2021.

[C10] N. M. Farroukh, M. Nassar, S. Elbassuoni, and H. Safa, 鈥淜eep it flat (kif): Resource management in integrated cloud-fog networks,鈥 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, 鈥淰ip blowfish privacy in communication graphs,鈥 in Proceedings of the 17th International Joint Conference on e-Business and Telecommunications, fICETEg, vol. 2, pp. 459鈥467, Lieusaint, Paris, France, July 8-10, 2020, 2020.

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

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

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

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

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

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

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

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

[C20] M. Nassar and H. Safa, 鈥淭hrottling malware families in 2d,鈥 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, 鈥淢odeling malware as a language,鈥 in 2018 IEEE International Conference on Communications (ICC), pp. 1鈥6, IEEE, 2018.

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

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

[C24] M. Nassar, N. Wehbe, and B. Al Bouna, 鈥淜-nn classification under homomorphic encryption: application on a labeled eigen faces dataset,鈥 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鈥552, IEEE, 2016.

[C25] S. Barakat, B. A. Bouna, M. Nassar, and C. Guyeux, 鈥淥n the evaluation of the privacy breach in disassociated set-valued datasets,鈥 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, 鈥淧aillier鈥檚 encryption: Implementation and cloud applications,鈥 in 2015 International Conference on Applied Research in Computer Science and Engineering (ICAR), pp. 1鈥5, IEEE, 2015.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[C45] M. Nassar, O. Festor, et al., 鈥淰oip honeypot architecture,鈥 in Integrated Network Management, 2007. IM鈥07. 10th IFIP/IEEE International Symposium on, pp. 109鈥118, IEEE, 2007.

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

Chapter in book and arxiv

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

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