Evolutionary Approaches for Adversarial Attacks on Neural Source Code Classifiers
As the prevalence and sophistication of cyber threats continue to increase, the development of robust vulnerability detection techniques becomes paramount in ensuring the security of computer systems. Neural models have demonstrated significant potential in identifying vulnerabilities; however, they...
Main Authors: | Valeria Mercuri, Martina Saletta, Claudio Ferretti |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-10-01
|
Series: | Algorithms |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-4893/16/10/478 |
Similar Items
-
<span style="font-variant: small-caps">AdVulCode</span>: Generating Adversarial Vulnerable Code against Deep Learning-Based Vulnerability Detectors
by: Xueqi Yu, et al.
Published: (2023-02-01) -
A Framework for Robust Deep Learning Models Against Adversarial Attacks Based on a Protection Layer Approach
by: Mohammed Nasser Al-Andoli, et al.
Published: (2024-01-01) -
Maxwell’s Demon in MLP-Mixer: towards transferable adversarial attacks
by: Haoran Lyu, et al.
Published: (2024-03-01) -
On the Effectiveness of Adversarial Training in Defending against Adversarial Example Attacks for Image Classification
by: Sanglee Park, et al.
Published: (2020-11-01) -
A Hybrid Adversarial Attack for Different Application Scenarios
by: Xiaohu Du, et al.
Published: (2020-05-01)