Research output per year
Research output per year
Personal profile
Biography
Zahra 's work is focused on the stateful fuzzing techniques that can discover vulnerabilities that could otherwise be used for remote arbitrary execution attacks. She received the BS degree in Software Engineering from Sharif University of Technology and the MSc and PhD degrees in Artificial Intelligence from Iran University of Science and Technology. She has been teaching and supervising in several undergraduate and graduate level courses (e.g. Pattern Recognition, Data Mining, Artificial Intelligence, Operating Systems, Database, Fundamentals of Programming and Advanced Programming) at Shahid Rajaee University (SRU) since her PhD candidacy.
Research interests
Cybersecurity
Machine Learning
Artificial Intelligence
Research area keywords
- Cybersecurity
- Automated software engineering
- Machine Learning
- Artificial intelligence
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Stateful Greybox Fuzzing
Ba, J., Böhme, M., Mirzamomen, Z. & Roychoudhury, A., 2022, Conference Proceedings, 31st USENIX Security Symposium. Butler, K. & Thomas, K. (eds.). CA USA: The USENIX Association, p. 3255-3272 18 p.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access69 Citations (Scopus) -
Exploiting associations between class labels in multi-label classification
Mirzamomen, Z. & Ghafooripour, K., 2019, In: Journal of Artificial Intelligence and Data Mining. 7, 1, p. 35-45 12 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile -
A framework to induce more stable decision trees for pattern classification
Mirzamomen, Z. & Kangavari, M. R., 2017, In: Pattern Analysis and Applications . 20, 4, p. 991-1004 14 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile6 Citations (Scopus) -
Evolving fuzzy min–max neural network based decision trees for data stream classification
Mirzamomen, Z. & Kangavari, M. R., 2017, In: Neural Processing Letters. 45, 1, p. 341-363 23 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile17 Citations (Scopus) -
Fuzzy min-max neural network based decision trees
Mirzamomen, Z. & Kangavari, M. R., 15 Jun 2016, In: Intelligent Data Analysis. 20, 4, p. 767-782 16 p.Research output: Contribution to journal › Article › Research › peer-review
7 Citations (Scopus)