Publications

Conference Papers

• [C23] M. Nazzal, I. Khalil, A. Khreishah, and N. H. Phan, “PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs),” 31st ACM Conference on Computer and Communications Security (CCS 2024), Salt Lake City, UT, USA, Oct. 2024. [Acceptance rate: 16.7%]. [Covered by a U.S. provisional patent.] Artifacts
• [C22] M. Nazzal, I. Khalil, A. Khreishah, N. H. Phan, and Y. Ma, “Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection,” 45th IEEE Symposium on Security and Privacy (IEEE S&P 2024), San Francisco, CA, USA, May 2024. [Acceptance rate: 17.8%]. Artifacts · Presentation
• [C21] A. Al-Barqawi, M. Nazzal, I. Khalil, A. Khreishah, and N. H. Phan, “ViGText: Deepfake Image Detection with Vision-Language Model Explanations and Graph Neural Networks,” 33rd Network and Distributed System Security Symposium (NDSS 2026), San Diego, CA, USA, Feb. 2026. [Acceptance rate: 16–18%]. Artifacts
• [C20] M. Nazzal*, K. Nguyen*, D. Vungarala*, R. Zand, H. Phan, A. Khreishah, and S. Angizi, “FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design,” IEEE International Conference on LLM-Aided Design (ICLAD 2025), Stanford University, Stanford, CA, Jun. 2025. (*Equal contribution). Artifacts
• [C19] D. Vungarala*, M. Nazzal*, M. Morsali, C. Zhang, A. Ghosh, A. Khreishah, and S. Angizi, “SA-DS: A Dataset for Large Language Model-Driven AI Accelerator Design Generation,” 58th IEEE International Symposium on Circuits and Systems (IEEE ISCAS), London, UK, May 2025. (*Equal contribution). Artifacts
• [C18] T. K. Ton, N. Nguyen, M. Nazzal, A. Khreishah, C. Borcea, N. H. Phan, R. Jin, I. Khalil, and Y. Shen, “Demo: SGCode: A Flexible Prompt-Optimizing System for Secure Generation of Code,” 31st ACM Conference on Computer and Communications Security (CCS 2024), Salt Lake City, UT, USA, Oct. 2024.
• [C17] M. Morsali, M. Nazzal, A. Khreishah, and S. Angizi, “IMA-GNN: In-Memory Acceleration of Centralized and Decentralized Graph Neural Networks at the Edge,” 33rd ACM Great Lakes Symposium on VLSI (GLSVLSI 2023), Knoxville, TN, USA, Jun. 2023. [Best Paper Award].

Journal Paper

• [J14] M. Nazzal, A. Khreishah, J. Lee, S. Angizi, A. Al-Fuqaha, and M. Guizani, “Semi-decentralized Inference in Heterogeneous Graph Neural Networks for Traffic Demand Forecasting: An Edge-Computing Approach,” IEEE Transactions on Vehicular Technology, Jan. 2024. Artifacts

• [J13] M. Anan, M. Nazzal, A. Khreishah, I. Khalil, N. H. Phan, and A. Sawalmeh, “STING: A Stealthy Backdoor Attack on GNN-Based Malicious Domain Detection via DNS Perturbations,” IEEE Open Journal of the Communications Society, vol. 6, pp. 7823–7841, Sep. 2025.
• [J12] C. P. Vizcarra, S. Alhamed, A. Algosaibi, M. Alnaeem, A. Aldalbahi, N. Aljaafari, A. Sawalmeh, M. Nazzal, A. Khreishah, A. Alhumam, and M. Anan, “Deep Learning Adversarial Attacks and Defenses on License Plate Recognition System,” Cluster Computing, vol. 27, no. 8, pp. 11627–11644, Nov. 2024.
• [J11] I. Alsmadi, K. Ahmad, M. Nazzal, F. Alam, A. Al-Fuqaha, A. Khreishah, and A. Algosaibi, “Adversarial NLP for social network applications: Attacks, defenses, and research directions,” IEEE Transactions on Computational Social Systems, vol. 10, no. 6, pp. 3089–3108, Nov. 2022.
• [J10] N. Aljaafari, M. Nazzal, A. Sawalmeh, A. Khreishah, M. Anan, A. Algosaibi, M. Alnaeem, A. Aldalbahi, A. Alhumam, and C. P. Vizcarra, “Investigating the Factors Impacting Adversarial Attack and Defense Performances in Federated Learning,” IEEE Transactions on Engineering Management, vol. 71, pp. 12542–12555, May 2022.
• [J9] I. Alsmadi, N. Aljaafari, M. Nazzal, S. Alhamed, A. Sawalmeh, C. P. Vizcarra, A. Khreishah, M. Anan, A. Algosaibi, M. Alnaeem, A. Aldalbahi, and A. Alhumam, “Adversarial Machine Learning in Text Processing: A Literature Survey,” IEEE Access, vol. 10, pp. 17043–17077, Jan. 2022.
• [J8] H. M. Furqan, M. A. Aygül, M. Nazzal, and H. Arslan, “Primary User Emulation and Jamming Attack Detection in Cognitive Radio via Sparse Coding,” EURASIP Journal on Wireless Communications and Networking, no. 1, pp. 1–9, Apr. 2020.

• [J7] M. Aygül, M. Nazzal, and H. Arslan, “Sparsifying dictionary learning for beamspace channel representation and estimation in millimeter-wave massive MIMO,” IEEE Access, vol. 11, pp. 98436–98451, Sep. 2023.
• [J6] S. Shao, M. Nazzal, A. Khreishah, and M. Ayyash, “Self-optimizing Data Offloading in Mobile Heterogeneous Radio-Optical Networks: A Deep Reinforcement Learning Approach,” IEEE Network Magazine, vol. 36, no. 2, pp. 100–106, May 2022.
• [J5] A. Alenezi, M. Nazzal, A. Sawalmeh, A. Khreishah, S. Shao, and M. Almutiry, “Machine Learning Regression-based RETRO-VLP for Real-time and Stabilized Indoor Positioning,” Cluster Computing, vol. 27, pp. 299–311, Dec. 2022.
• [J4] M. A. Aygül, M. Nazzal, and H. Arslan, “Using OMP and SD Algorithms Together in mm-Wave mMIMO Channel Estimation,” Signal, Image and Video Processing, vol. 16, pp. 1205–1213, Jan. 2022.
• [J3] M. A. Aygül, M. Nazzal, M. İ. Sağlam, D. B. da Costa, H. F. Ates, and H. Arslan, “Efficient Spectrum Occupancy Prediction Exploiting Multidimensional Correlations through Composite 2D-LSTM Models,” Sensors, vol. 21, no. 1, pp. 135–153, Dec. 2020.
• [J2] M. Nazzal, A. R. Ekti, A. Gorcin, and H. Arslan, “Exploiting sparsity recovery for compressive spectrum sensing: A machine learning approach,” IEEE Access, vol. 7, pp. 126098–126110, Apr. 2019.
• [J1] M. Nazzal, F. Yeganli, and H. Ozkaramanli, “A strategy for residual component-based multiple structured dictionary learning,” IEEE Signal Processing Letters, vol. 22, no. 11, pp. 2059–2063, Nov. 2015.

• [C16] S. Satpathy, M. Nazzal, and A. Khreishah, “Learning Ray-Tracing Propagation with Graph Neural Networks for Efficient Base Station Planning,” accepted for presentation at the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2026), Singapore, Sep. 2026.
• [C15] M. Nazzal, N. Aljaafari, A. Sawalmeh, A. Khreishah, M. Anan, A. Algosaibi, M. Alnaeem, A. Aldalbahi, A. Alhumam, C. P. Vizcarra, and S. Alhamed, “Genetic Algorithm-Based Dynamic Backdoor Attack on Federated Learning-Based Network Traffic Classification,” FMEC 2023, Tartu, Estonia, Sep. 18–20, 2023.
• [C14] M. A. Aygül, M. Nazzal, and H. Arslan, “Estimating Multi-Dimensional Sparsity Level for Spectrum Sensing,” IEEE WCNC 2023, Glasgow, Scotland, UK, Mar. 2023.
• [C13] M. A. Aygül, H. M. Furqan, M. Nazzal, and H. Arslan, “Deep Learning-Assisted Detection of PUEA and Jamming Attacks in Cognitive Radio Systems,” IEEE VTC2020-Fall, Victoria, BC, Canada, Oct. 2020.
• [C12] M. Nazzal, A. Sawalmeh, S. Shao, M. Anan, A. Khreishah, and A. Alanazi, “Retro-VLP: Towards Single Light Source-based Real-time Indoor Positioning,” ICICS 2022, Irbid, Jordan, Jun. 2022.
• [C11] M. Nazzal, M. A. Aygül, and H. Arslan, “Estimation and Exploitation of Multidimensional Sparsity for MIMO-OFDM Channel Estimation,” IEEE WCNC 2022, Austin, TX, USA, Apr. 2022.
• [C10] M. A. Aygül, M. Nazzal, and H. Arslan, “Deep RL-Based Spectrum Occupancy Prediction Exploiting Time and Frequency Correlations,” IEEE WCNC 2022, Austin, TX, USA, Apr. 2022.
• [C9] M. Nazzal, M. A. Aygül, and H. Arslan, “Sparse Coding with Enhanced Atom Selection for FDD Massive MIMO Channel Estimation,” IEEE VTC2021-Fall, Norman, OK, USA, Sep. 2021.
• [C8] M. A. Aygül, M. Nazzal, and H. Arslan, “Using OMP and SD Algorithms Together in Millimeter Wave Massive MIMO Channel Estimation,” SIU 2021, Istanbul, Turkey, Jun. 2021. [Best Paper Award].
• [C7] M. A. Aygül, M. Nazzal, and H. Arslan, “Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations,” IEEE WCNC 2021, Nanjing, China, Mar. 2021.
• [C6] M. A. Aygül, M. Nazzal, A. R. Ekti, A. Gorcin, D. B. da Costa, H. F. Ates, and H. Arslan, “Spectrum Occupancy Prediction Exploiting Time and Frequency Correlations Through 2D-LSTM,” IEEE VTC2020-Spring, Antwerp, Belgium, May 2020.
• [C5] M. Nazzal, O. Hasekioğlu, A. R. Ekti, A. Gorcin, and H. Arslan, “Compressed spectrum sensing using sparse recovery convergence patterns through machine learning classification,” IEEE PIMRC 2019, Istanbul, Turkey, Sep. 2019.
• [C4] M. Nazzal, M. A. Aygül, A. Görçin, and H. Arslan, “Dictionary learning-based beamspace channel estimation in millimeter-wave massive MIMO systems with a lens antenna array,” IWCMC 2019, Tangier, Morocco, Jun. 2019.
• [C3] M. Nazzal, M. A. Aygül, A. Görçin, and H. Arslan, “Sparse Coding for transform domain-based sparse OFDM channel estimation,” SIU 2019, Sivas, Turkey, Apr. 2019.
• [C2] M. Nazzal, H. M. Furqan, and H. Arslan, “FDD massive MIMO channel estimation by sparse coding over AoA/AoD cluster dictionaries,” IEEE PIMRC 2018, Bologna, Italy, Sep. 2018.
• [C1] H. M. Furqan, M. Nazzal, and H. Arslan, “Iterative tap pursuit for channel shortening equalizer design,” ICCCE 2018, pp. 416–420, Kuala Lumpur, Malaysia, Sep. 2018.

Selected / Related Patent Application

• [P5] M. Nazzal, I. Khalil, A. Khreishah, and N. H. Phan, “Method and System for Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models,” WIPO (PCT) Patent Application, Pub. No. WO2025188204A1, 2025. Google Patents

Issued Patents

• [P4] M. A. Aygül, M. Nazzal, and H. Arslan, “Learning-Based Spectrum Occupancy Prediction Exploiting Multi-Dimensional Correlation,” U.S. Patent No. US12526644B2, Jan. 13, 2026.
• [P3] M. A. Aygül, H. M. Furqan, M. Nazzal, and H. Arslan, “Primary User Emulation / Signal Jamming Attack Detection Method,” U.S. Patent No. US12177005B2, Dec. 24, 2024.

Invention Disclosures Under Evaluation

• [P2] A. AlBarqawi, I. M. Khalil, A. Khreishah, N. H. Phan, M. Nazzal, “ViGText: Deepfake Detection Using Vision–Language Model Explanations and Graph Neural Networks,” Invention Disclosure (Under Evaluation for Patent Filing), HBKU IP Office, 2025.
• [P1] A. AlBarqawi, I. M. Khalil, A. Khreishah, N. H. Phan, M. Nazzal, “CAPTCHA Framework Using Perceptual Puzzles for Enhanced Robustness Against Vision–Language Model Attacks,” Invention Disclosure (Under Evaluation for Patent Filing), HBKU IP Office, 2025.

• [BC1] M. Nazzal, M. A. Aygül, and H. Arslan, “Channel Modeling for 5G and Beyond,” in H. Arslan and E. Basar, Flexible and Cognitive Radio Access Technologies for 5G and Beyond, Institution of Engineering and Technology, 2020.