Sara Baradaran

Sara Baradaran

Computer Science PhD Student

Publications

Reusing Legacy Code in WebAssembly: Key Challenges of Cross-Compilation and Code Semantics Preservation

Accepted at SANER 2026, 2026

In this paper, we inquire (1) what challenges arise when cross-compiling a high-level language codebase into WebAssembly and (2) how faithfully WebAssembly compilers preserve code semantics in this new binary.

Recommended citation: Baradaran, S., Huang, L., Raghothaman, M., & Wang, W. (2024). Reusing Legacy Code in WebAssembly: Key Challenges of Cross-Compilation and Code Semantics Preservation. ArXiv, abs/2412.20258.

CN2F: A Cloud-Native Cellular Network Framework

Cluster Computing, 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. et al. CN2F: a cloud-native cellular network framework. Cluster Comput 28, 493 (2025). https://doi.org/10.1007/s10586-025-05155-w

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

International Journal of Information Security, 2023

This paper proposes a method for restricting the scope of symbolic analysis and combining it with ML techniques to detect 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

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