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Qawsar Gulzar

Qawsar Gulzar

Assistant Professor , Computer Science & Engineering (CSE)

N/A

About

I am a Doctoral Researcher with a thesis titled "Intelligent Detection of Adversarial Cyberattacks and Anomalies in Cyber-Physical Systems and IoT," seeking an Assistant Professor position in cybersecurity, artificial intelligence, and intelligent systems. My research extensively leverages Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) to develop advanced and resilient intrusion detection systems for cyber-physical systems and IoT networks. I am deeply committed to advancing interdisciplinary and application-driven research in AI-powered cybersecurity, while also contributing to the academic community through high-quality publications, collaborative projects, curriculum development, and student mentorship. I strive to foster an innovative teaching and research environment that empowers the next generation of engineers and researchers to tackle emerging security challenges in critical digital infrastructures.

Experience
  • 4 years
Qualification
  • Pursuing Ph.D.
Research

  • Qawsar Gulzar, et al. (2024). International Conference on Data Analytics & Management (ICDAM). In Proceedings of Data Analytics and Management, pp. 197-224. Springer 03 January 2024, United Kingdom.(Scopus, WoS, & EI Indexed) DOI: 10.1007/978-981-99-6547-2_16
  • Qawsar Gulzar, et al. (2025). International Conference on Data Analytics & Management (ICDAM). In Proceedings of Data Analytics and Management, pp. 281-301. Springer 02 July 2025, United Kingdom. (Scopus, WoS, & EI Indexed) DOI: https://doi.org/10.1007/978-981-96-3352-4_20.
  • Qawsar Gulzar, et al. (2024). An analytical survey of cyber-physical systems in water treatment and distribution: Security challenges, intrusion detection, and future directions. Security and Privacy (ESCIE, IF:2.0) DOI: https://doi.org/10.1002/spy2.440
  • Qawsar Gulzar, et al. (2024). Enhancing Network Security in Industrial IoT Environments: A DeepCLG Hybrid Learning Model for Cyberattack Detection. International Journal of Machine Learning and Cybernetics (Q2, SCIE, IF: 3.2) DOI: https://doi.org/10.1007/s13042-025-02544-w.
  • Qawsar Gulzar, et al. (2024). Interdisciplinary Framework for Cyberattacks and Anomaly Detection in Industrial Control Systems Using Deep Learning. Scientific Reports (Q1, SCIE, IF: 4.1) DOI: 10.1038/s41598-025-89650-5.
  • Qawsar Gulzar, et al. (2025). Protecting IIoT Ecosystems: A Hybrid CNN-LSTM Approach in Federated Learning. Manuscript Under Review in Cluster Computing (Q1, SCIE, IF: 4.1).

Certifications

  • UGC JRF | UGC India | NTA December 2020
  • UGC NET| UGC India | NTA November 2019
  • Reviewer | Applied Intelligence | Springer Nature May 2025

Area of Interest

  • Core CS/ Cybersecurity