I am an assistant professor of Computer Science at Loyola University Chicago . I received a Ph.D. degree in Computer Science from the University of Central Florida (UCF) in 2020. I also received a Ph.D. degree in Electrical and Computer Engineering from INHA University , (Incheon, Republic of Korea) in 2020. I received a Master degree in Information Technology (Artificial Intelligence) from the National University of Malaysia , (Bangi, Malaysia) in 2013.
I am interested in AI/Deep-Learning-based Information Security, especially Software and Mobile/IoT Security. I am also interested in Machine Learning-based Applications and Adversarial Machine Learning. I have published several peer-reviewed research papers in top-tier conferences and journals such as ACM CCS, PoPETS, IEEE ICDCS, and IEEE IoT-J.
Cybersecurity lab and AI for Secure Computing Research Lab ( AISeC).
We employ advances in machine learning to information and system security.
We study methods for software authorship and vulnerabilities.
Authorship anonymization and behavioral biometrics for authentication.
Robustness and Adversarial ML
We analyze the security proprieties of machine learning models
Applied Machine Learning
We study and employ machine learning in various domains.
We utilize various characterizing features to build machine learning models to detect and analyze malware.
To improve our understanding of systems, and to guide security analytics towards secure systems design, I employ advances in deep machine learning to systems security. The emergence of such learning techniques promises various avenues for attribution in data-driven security analytics, and those techniques constitute the cornerstone of my current and future research interests. My current research contributions have been focused on creating efficient and accurate methods for software security, building, customizing, optimizing, and leveraging deep machine learning techniques for security and privacy.
Software and IoT Security
We work on developing methods and algorithms to understand information from different sources and with different formats.
Information Security and Privacy
AI for social good
Robustness and Adversarial Machine Learning
Deep Neural Networks have achieved state-of-the-art performance in various applications. It is crucial to verify that the high accuracy prediction for a given task is derived from the correct problem representation and not from the misuse of artifacts in the data. Investigating the security proprieties of machine learning models, recent studies have shown various categories of adversarial attacks such as model leakage, data membership inference, model confidence reduction, evasion, and poisoning attacks. We believe it is important to investigate and understand the implications of such attacks on sensitive applications in the field of information security and privacy.
DL-FHMC: Robust classification AdvEdge: AML against IDLSes SGEA: AML against malware detectors Soteria: Robust malware detectors Black-box and Target-specific AML BExploration of Black-box AML
Malware is one of the serious computer security threats. To protect computers from infection, accurate detection of malware is essential. At the same time, malware detection faces two main practical challenges: the speed of malware development and their distribution continues to increase with complex methods to evade detection (such as a metamorphic or polymorphic malware). This project utilizes various characterizing features extracted from each malware using static and dynamic analysis to build seven machine learning models to detect and analyze malware. We investigate the robustness of such machine learning models against adversarial attacks.
MLxPack DL-FHMC: Robust malware classification SGEA: AML against malware detectors Soteria: Robust malware detectors Spectral Representations of CFG ShellCore
Continuous User Authentication
Smartphones have become crucial for our daily life activities and are increasingly loaded with our personal information to perform several sensitive tasks, including mobile banking, communication, and storing private photos and files. Therefore, there is a high demand for applying usable continuous authentication techniques that prevent unauthorized access. We work on a deep learning-based active authentication approach that exploits sensors in consumer-grade smartphones to authenticate a user. We addressed various aspects regarding accuracy, efficiency, and usability.
AUToSen: Continuous Authentication Contemporary Survey on Sensor-Based Continuous Authentication
Machine Learning for Social Good
Computer-aided methods for analyzing white blood cells (WBC) are popular due to the complexity of the manual alternatives. We work on building a deep learning-based method for WBC classification. We proposed W-Net that we evaluated on a real-world large-scale dataset that includes 6,562 real images of the five WBC types. The dataset was provided by The Catholic University of Korea (The CUK), and approved by the Institutional Review Board (IRB) of The CUK. For further benefits, we generate synthetic WBC images using Generative Adversarial Network to be used for education and research purposes through sharing.
W-Net: WBC Classification Online Toxicity in Users Interactions with Mainstream Media Children Exposure to Inappropriate Comments in YouTube Synthetic MRI from CT Sentiment analysis of Users' Reactions on Social Media
Machine Learning for Medical Images
This project is about synthesizing magnetic resonance images (MRI) from computed tomography (CT) simulation scans using deep-learning models for high-dose-rate prostate brachytherapy. For high dose rate prostate brachytherapy, both CT and MRI are acquired to identify catheters and delineate the prostate, respectively. We propose to build a deep-learning model to generate synthetic MRI (sMRI) with enhanced soft-tissue contrast from CT scans. sMRI would assist physicians to accurately delineate the prostate without needing to acquire additional planning MRI.
W-Net: WBC Classification Synthetic MRI from CT
Network Security and Online Privacy
Network monitoring applications such as flow analysis, intrusion detection, and performance monitoring have become increasingly popular owing to the continuous increase in the speed and volume of network traffic. We work on investigating the feasibility of an in-network intrusion detection system that leverages the computation capabilities of commodity switches to facilitate fast and real-time response. Moreover, we explore the traffic sampling techniques that preserve flows’ behavior to apply intelligence in network monitoring and management. We also address the increased privacy concerns regarding website fingerprinting attacks despite the popular anonymity tools such as Tor and VPNs.
Traffic Sampling for NIDSs Multi-X Exploring the Proxy Ecosystem Studying the DDoS Attacks Progression
Contact us for details.
I am always looking for motivated undergraduate and graduate students to work with me on exciting projects in areas such as machine learning for social good, adversarial machine learning, and information security and privacy.
308 Doyle Center, Lake Shore Campus
1052 W Loyola Ave. Chicago, IL 60626
+1 773 508 3557