Working from Home

Research and Projects

 

Ongoing Research Activities

I am interested in different aspects of cybersecurity that deals with detection, characterization, and mitigation of potential attacks against IoT devices and Cyber-Physical Systems (CPS). This involves system and network vulnerability assessment identifying security weaknesses, quantifying and analyzing them, and remediating them. My recent research activities in collaboration with students at Concordia and George Mason University include: Trusted Computing Systems and Container security, Fingerprinting online EV charging station management systems and assessing their security; Ransomware detection and classification using pre-attack paranoia activities; IoT malware detection, classification, and family attribution; and Software security and patch management. 

Previous Research

IoT Security

My PhD research at Concordia University was focused on investigating Internet of Things (IoT) security. We aimed at addressing the lack of understanding about compromised IoT devices and their malicious activities on the Internet. We developed data-driven approaches that leveraged publicly available IoT device information resources coupled with passive network measurements to infer malware-infected IoT devices and characterize their behaviors as a part of well-coordinated IoT botnets. You can view our recent publication for further information.

Health Information Sharing on Social Networking Sites

While at UBC, I was focused on usable security and privacy. My research was focused on studying users' perspectives on sharing their health information on Facebook. Through a series of in-depth interviews and online surveys with online social networking site users who also had chronic health conditions, my work highlighted user perceptions and concerns when sharing health information with online peers. We also provided recommendations, which can enhance user experience online and encourage more informed health information sharing in order to increase the associated benefit. You can refer to our SOUPS'16 paper for detailed information.

Modeling Information Diffusion in Online Social Networks

In this project, we tried to model and characterize information diffusion on online social networks by introducing a diffusion parameter. We studied different social network structures and showed that our introduced diffusion parameter can characterize the network topology. We also studied a real life online social network and showed that while most human networks represent small-world topologies, these new online communities have a unique topology that can be described as a small-world with randomness.