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Biography Dr. Sadaf is a Lecturer of Cybersecurity at the School of Science, Engineering and Environment (SSEE) at the University of Salford, Manchester, UK. She received her PhD and MSc. degrees in Information Technology, with research dissertations in the domain of Information Security, from the Universiti Teknologi PETRONAS (UTP), Malaysia. She has profound experience in teaching, research, and leadership in multi-national universities. Sadaf is the program leader for a B.Sc in Computer Science with Cybersecurity program in SSEE. She is serving as REF 2029 Research Output Lead for UoA11. She is also a leading member of the IoT Research and Innovation Lab (IRIL) at Al-Khwarizmi Institute of Computer Science. She has been a co-chair and technical program committee (TPC) member at many international conferences. She has published in various impact factor journals and reputable conferences with distinguished indices like SCOPUS and ISI. She has won eminent national and international research funding in her academic tenure. She is also a prominent reviewer of high-quality journals.
Research Interests AI/ ML in Cyber/ Information Security, IoT/ IIoT Vulnerability Analysis and Security, Zero-Trust Architecture for IIoT, Multilevel Behaviour Profiling in Industrial Control Systems, Security Information and Event Management, Malware Analysis and Prevention, Threat Hunting and Modelling in Smart Cyber-Physical Systems, AI-Aided Optimization of IDS/ IPS, Wireless Attacks Detection and Prevention, Security of AI.
Teaching and Learning Dr. Sadaf's teaching subjects are Information Security, Information Security Management, Network Penetration Testing, Security and Privacy in IoT, and Fundamentals of Cybersecurity. She is actively supervising BSc. final year and MSc projects/ dissertations on Information/ Cybersecurity domains. She is also supervising PhD candidates in the diverse domains of AI-aided Security.
PhD Supervision Availability Yes
PhD Topics i) Modelling autonomous agent for cyber offensive security operations, ii) Multistage detection of DDoS attacks in software-defined networking using a hybrid convolutional-deep neural network model, and iii) Adaptable RL-based honeypot framework for detection of zero-day attacks.