I'm a researcher and PhD candidate at Security Lab at George Mason University, Fairfax, Virginia working with Damon McCoy. I am also member of the Center for Evidence-based Security Research (CESR).

My interests are in the fields of Cyber-Physical Security. More specifically, my focuses are: Cyber Threat Intelligence, Reverse Engineering (both Hardware and Software), Vulnerability Analysis and Ethical Hacking. The fundamental topics that I have worked on and influenced my research interests are: System Security, Network Security, and Modern Cryptography.

Contact Details


Department of Computer Science,
George Mason University,
4400 University Drive MSN 4A5,
Fairfax, VA 22030 USA

GitHub
linkedin
Email: mrezaeir [at] gmu [dot] edu
pgp.mit.edu
Key Fingerprint:
BA91 5FB7 17B0 D855 4622
2108 15BD EAA0 46A2 0284

Publication

Project

RAT Ecosystem Measurement:
In this work, we design, implement, and deploy improved methodologies for accurately measuring real victims that connect to our sinkhole, RAT-Hole, and identifying RAT controllers using our scanner, RAT-Scan. The task of identifying victims at scale is made difficult by the amount of pollution sinkholes receive from increasingly high-fidelity scanners and sandboxes. Differentiating between real controllers and sinkholes is also a nontrivial undertaking due to higher fidelity sinkholes. This increasing fidelity in RAT scanners that emulate more of a victim’s behavior and sinkholes that emulate more of a real RAT controller’s protocol has likely created an arms-race between entangled threat intelligence operations which we call Intelligence Pollution. This leads to inaccurate measurements and wasted notification efforts, wherein researchers and security vendors may confuse beneficent sinkholes for malicious controllers, or scanners and sandboxes for actual victims.

RAT Operators Behavioral Study:
This project aimed to shed a light on DarkComet RAT operators from the behavioral perspective. This includes, operator life cycle and motivation when engaged with a victim machine.
In this work we study the use of DarkComet, a popular commercial RAT. We collected 19,109 samples of DarkComet malware found in the wild, and in the course of two, severalweek-long experiments, ran as many samples as possible in our honeypot environment. By monitoring a sample’s behavior in our system, we are able to reconstruct the sequence of operator actions, giving us a unique view into operator behavior. We report on the results of 2,747 interactive sessions captured in the course of the experiment. During these sessions operators frequently attempted to interact with victims via remote desktop, to capture video, audio, and keystrokes, and to exfiltrate files and credentials. To our knowledge, we are the first large-scale systematic study of RAT use.

IVI Security Assessment and Analysis:
In this project, we performed a comprehensive security analysis on an IVI system that is included in at least one 2015 model vehicle from a major automotive manufacturer. We documented and demonstrated insecurities in the MirrorLink protocol and IVI implementation that could potentially enable an attacker with control of a driver’s smartphone to send malicious messages on the vehicle’s internal network. This work was funded by General Motors and DHS.