Sara Baradaran

Sara Baradaran

Computer Science PhD Student

Publications

CN2F: A Cloud-Native Cellular Network Framework

Published in Cluster Computing (Recently Accepted), 2025

In this paper, we share our findings, accompanied by a comprehensive online codebase, about the best practice of using different open-source projects in order to realize a flexible testbed for academia and industrial Research and Development (R&D) activities on the future generation of cellular networks.

Recommended citation: Ganji, S., Behnaminia, S., Ahangarpour, A., Mazaheri, E., Baradaran, S., Zali, Z., Heidarpour, M.R., Rakhshan, A. and Shoyari, M.F., 2023. CN2F: A Cloud-Native Cellular Network Framework. arXiv preprint arXiv:2305.18778

A Unit-Based Symbolic Execution Method for Detecting Memory Corruption Vulnerabilities in Executable Codes

Published in International Journal of Information Security, 2023

This paper proposes a method for restricting the scope of symbolic analysis and combining it with ML techniques for detecting memory corruption vulnerabilities in executable codes.

Recommended citation: Baradaran, S., Heidari, M., Kamali, A. et al. A unit-based symbolic execution method for detecting memory corruption vulnerabilities in executable codes. Int. J. Inf. Secur. 22, 1277–1290 (2023). https://doi.org/10.1007/s10207-023-00691-1

A Unit-Based Symbolic Execution Method for Detecting Heap Overflow Vulnerability in Executable Codes

Published in Tests and Proofs. TAP 2022. Lecture Notes in Computer Science, vol 13361. Springer, Cham., 2022

This paper proposes a method for improving the efficiency of symbolic execution and detecting heap overflow vulnerability in executable codes using the combination of symbolic execution and machine learning techniques.

Recommended citation: Mouzarani, M., Kamali, A., Baradaran, S., Heidari, M. (2022). A Unit-Based Symbolic Execution Method for Detecting Heap Overflow Vulnerability in Executable Codes. In: Kovács, L., Meinke, K. (eds) Tests and Proofs. TAP 2022. Lecture Notes in Computer Science, vol 13361. Springer, Cham. https://doi.org/10.1007/978-3-031-09827-7_6