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Found 9 papers, 1 papers with code
Just another copy and paste? Comparing the security vulnerabilities of ChatGPT generated code and StackOverflow answers
Our findings suggest developers are under-educated on insecure code propagation from both platforms, as we found 274 unique vulnerabilities and 25 types of CWE.
Shifting the Lens: Detecting Malware in npm Ecosystem with Large Language Models
Our baseline comparison demonstrates a notable improvement over static analysis in precision scores above 25% and F1 scores above 15%.
Mining Temporal Attack Patterns from Cyberthreat Intelligence Reports
The goal of this paper is to aid security practitioners in prioritizing and proactive defense against cyberattacks by mining temporal attack patterns from cyberthreat intelligence reports.
From Threat Reports to Continuous Threat Intelligence: A Comparison of Attack Technique Extraction Methods from Textual Artifacts
\textit{The goal of this study is to aid cybersecurity researchers and practitioners choose attack technique extraction methods for monitoring and sharing threat intelligence by comparing the underlying methods from the TTP extraction studies in the literature.}
Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue
Conclusion: Based on this study, we would suggest the use of optimized GANs as an alternative method for security vulnerability data class imbalanced issues.
What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A survey
Cybersecurity researchers have contributed to the automated extraction of CTI from textual sources, such as threat reports and online articles, where cyberattack strategies, procedures, and tools are described.
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack
Conclusion: When employing ensemble defense against adversarial evasion attacks, we suggest creating an ensemble with unexpected models that are distant from the attacker's expected model (i. e., target model) through methods such as hyperparameter optimization.
How to Better Distinguish Security Bug Reports (using Dual Hyperparameter Optimization
For example, in a study of security bug reports from the Chromium dataset, the median recalls of FARSEC and Swift were 15. 7% and 77. 4%, respectively.
Software Engineering
Security Smells in Infrastructure as Code Scripts
We observe agreement for 130 of the responded 187 bug reports, which suggests the relevance of security smells for IaC scripts amongst practitioners.
Cryptography and Security Software Engineering
