Omid Mirzaei
Postdoctoral Research Associate
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Research Interests
- Artificial intelligence
- Machine learning
- Security and privacy
- Systems and networks
Education
- PhD in Computer Science, Universidad Carlos III de Madrid – Spain
Pronouns
he/him/his
Biography
Omid Mirzaei is a postdoctoral research associate in the Systems Security Lab at Northeastern University, working with Engin Kirda. Within systems and cybersecurity, he is particularly interested in mobile security, malware analysis, reverse engineering, and applied machine learning in security. In addition, Omid is eager to tackle security issues from a multi-objective perspective, such as trying to deal with such problems by consuming the least possible amount of in-hand resources.
Before this, Omid was an assistant professor at Universidad Carlos III de Madrid, where he spent four years at the COmputer SECurity Lab (COSEC) as a doctoral student. During his doctoral studies, he worked on Android malware analysis and triage, where his primary focus was on developing systems to identify potentially risky Android applications. As a postdoctoral researcher, he has dedicated his research to solving security and privacy issues in systems that have access to users’ personal information. Going forward, Omid would like to continue working in his field, to find problems that are critical and important for people which can be solved from a security and privacy point of view.
Recent Publications
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SCRUTINIZER: Detecting Code Reuse in Malware via Decompilation and Machine Learning
Citation: Mirzaei, O., Vasilenko, R., Kirda, E., Lu, L., & Kharraz, A. (2021). SCRUTINIZER: Detecting Code Reuse in Malware via Decompilation and Machine Learning. DIMVA. -
Preventing server-side request forgery attacks
Citation: Bahruz Jabiyev, Omid Mirzaei, Amin Kharraz, and Engin Kirda. 2021. Preventing Server-Side Request Forgery Attacks. In The 36th ACM/SIGAPP Symposium on Applied Computing (SAC ’21), March 22–26, 2021, Virtual Event, Republic of Korea. ACM, New York, NY, USA, 10 pages. https://doi. org/10.1145/3412841.3442036 -
AndrEnsemble: Leveraging API Ensembles to Characterize Android Malware Families
Citation: Mirzaei, Omid & Suarez-Tangil, Guillermo & De Fuentes, José María & Tapiador, Juan & Stringhini, Gianluca. (2019). AndrEnsemble: Leveraging API Ensembles to Characterize Android Malware Families. 10.1145/3321705.3329854