Dr. Sana Aurangzeb

Faculty Member

Dr. Sana Aurangzeb

Assistant Professor

HEC Approved PhD Supervisor
(+9251 111 128 128) 3305
sana.aurangzeb@nu.edu.pk

Education

PhD (Computer Science), National University of Computer and Emerging Sciences, FAST-NUCES, 2024

MS (Computer Science), Capital University of Science and Technology, Islamabad (CUST), 2018

MSc (Computer Science), The University of Lahore, Lahore, 2014

BS (hons) (Psychology), Lahore College for Women University, Lahore, 2011

Summary

Dr. Sana Aurangzeb is an Assistant Professor in the Department of Cyber Security at the National University of Computer and Emerging Sciences (NUCES-FAST), Islamabad, Pakistan. She earned her Ph.D. from NUCES-FAST, Islamabad, and her MS is in Computer Science from the Capital University of Science and Technology in 2018 with distinction. Her research primarily focuses on malware analysis, deep learning and security services. Her work explores innovative methods to improve robustness against obfuscation, enhance generalization across tasks, and advance the practical deployment of AI systems in security-sensitive environments. She has published several research articles in this domain. She is also providing her services as an Academic Reviewer in multiple impact factor journals.

Publications

Journal Papers

  1. Aurangzeb S., Aleem M., Azhar M., Islam MA, Sana Aurangzeb, Muhammad Aleem, Muhammad Azhar, and Muhammad Arshad Islam Ransomware: A Survey and Trends 2017 in Journal of Information Assurance and Security (JIAS) 2017 [ISI Index 1554-1010, ESCI]
  2. Aurangzeb S., Rais RNB., Aleem M., Azhar M., Islam MA, Sana Aurangzeb, Rao Naveed Bin Rais, Muhammad Aleem, Muhammad Arshad Islam, and Muhammad Azhar Iqbal, “On the Classification of Ransomware Using Hardware Performance Counters”, 2021 in PeerJ Computer Science7, e361 2021 [IF: 3.091] http://dx.doi.org/10.7717/peerj-cs.361
  3. Aurangzeb S., Anwar H., Naeem MA., Aleem M., Sana Aurangzeb, Haris Anwar, Muhammad Asif Naeem, Muhammad Aleem, “BigRC-EML: Big-Data based Ransomware Classification using Ensemble Machine Learning”, in Cluster Computing, Springer, 2022 [IF:1.809] http://dx.doi.org/10.1007/s10586-022-03569-4
  4. Hayat RF., Aurangzeb S., Aleem M., Rana Faisal Hayat, Sana Aurangzeb, Muhammad Aleem, “ML-DDoS: A Blockchain-based Multi-level DDoS Mitigation Mechanism for IoT Environment”, in IEEE Transactions on Engineering Management, 2022 [IF:8.709] DOI:10.1109/TEM.2022.3170519
  5. Khan J., Aurangzeb S., Aleem M., Srivastave G., Lei JCW, Junaid Khan, Sana Aurangzeb, Muhammad Aleem, Gautam Srivastava; Jerry Chun-Wei Lei, “RThreatDroid: A Ransomware Detection Approach to Secure IoT based Healthcare Systems”, in IEEE Transactions on Network Science and Engineering, 2022 [IF: 5.033]  DOI:10.1109/TNSE.2022.3188597
  6. Aurangzeb S., Aleem M., Sana Aurangzeb, Muhammad Aleem, “A Systematic State-of-the-Art Survey and Future Research Directions for Modern Malware Detection and Analysis”, 2026
  7. Aurangzeb S., Aleem M., Sana Aurangzeb, Muhammad Aleem, “Evaluation and Classification of Obfuscated Android Malware through Deep Learning using Ensemble Voting Mechanism_Scientific Reports”, Scientific Reports, Springer Journal, 2023 [IF: 4.996] Q1 Medallion: Platinum https://doi.org/10.1038/s41598-023-30028-w
  8. Aurangzeb S., Aleem M., Khan MT., Loukas G., Sakellari G,  Sana Aurangzeb, Muhammad Aleem, Muhammad Taimoor Khan, George Loukas and Georgia Sakellari. “AndroDex: Android Dex Images of Obfuscated Malware”, Scientific Data, Springer Journal 2024 [IF: 9.8] Q1 Medallion: Platinum https://doi.org/10.1038/s41597-024-03027-3
  9. Masood MY., Aurangzeb S., Aleem M., Chilwan A., Awais M., Muhammad Yasir Masood, Sana Aurangzeb, Muhammad Aleem, Ameen Chilwan, Muhammad Awais . Demand-side load forecasting in smart grids using machine learning techniques, PeerJ Computer Science 10, e1987, 2024 [IF 3.091] https://doi.org/10.7717/peerj-cs.1987

Conference Papers

  1. Mohsin A., Aurangzeb S., Aleem M., Khan MT, Ali Mohsin, Sana Aurangzeb, Muhammad Aleem and Muhammad Taimoor Khan, “ On the Performance and Scalability of Simulators for Improving Security and Safety of Smart Cities”, 2022 IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA) DOI: 10.1109/ETFA52439.2022.9921600