Dr. Sana Aurangzeb

About

A cybersecurity researcher with strong expertise in ransomware analysis, malware detection, and intelligent threat classification using advanced machine learning (ML) and deep learning (DL) methods. Skilled in handling large-scale datasets and developing robust, ensemble-based classification models for both Windows and Android malware, including obfuscated and evasive variants. Having 10 years of teaching experience, I am currently serving as an Assistant Professor in FAST-NUCES. Published my research work as a primary author in the top Platinum journals: Scientific Reports, Springer Nature and Scientific Data, Springer Nature. My research focus is on Malware Analysis and Security Services.
Proven experience in:
Ransomware behavior analysis and trend forecasting (e.g., Ransomware: a Survey and Trends)
Building big data-powered ML ensembles for ransomware detection (e.g., BigRC-EML)
Developing hardware-assisted ransomware classifiers using low-level system features
Applying deep learning with ensemble voting for detecting obfuscated Android malware
Investigating cyber threats to smart and autonomous vehicle infrastructures
Dataset contribution and malware profiling via DEX image analysis (e.g., AndroDex)
Well-versed in the development of hybrid approaches combining static, dynamic, and hardware-based analysis for advanced malware detection. Adept at leveraging interdisciplinary methods to address emerging cybersecurity challenges in platforms ranging from mobile devices to smart cities.

Areas of Expertise