The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches
The detection of software vulnerability requires critical attention during the development
phase to make it secure and less vulnerable. Vulnerable software always invites hackers to …
phase to make it secure and less vulnerable. Vulnerable software always invites hackers to …
Android source code vulnerability detection: a systematic literature review
The use of mobile devices is rising daily in this technological era. A continuous and
increasing number of mobile applications are constantly offered on mobile marketplaces to …
increasing number of mobile applications are constantly offered on mobile marketplaces to …
A survey of learning-based automated program repair
Automated program repair (APR) aims to fix software bugs automatically and plays a crucial
role in software development and maintenance. With the recent advances in deep learning …
role in software development and maintenance. With the recent advances in deep learning …
VUDENC: vulnerability detection with deep learning on a natural codebase for Python
Context: Identifying potential vulnerable code is important to improve the security of our
software systems. However, the manual detection of software vulnerabilities requires expert …
software systems. However, the manual detection of software vulnerabilities requires expert …
Neural transfer learning for repairing security vulnerabilities in c code
Z Chen, S Kommrusch… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this paper, we address the problem of automatic repair of software vulnerabilities with
deep learning. The major problem with data-driven vulnerability repair is that the few …
deep learning. The major problem with data-driven vulnerability repair is that the few …
Fixing hardware security bugs with large language models
Novel AI-based code-writing Large Language Models (LLMs) such as OpenAI's Codex have
demonstrated capabilities in many coding-adjacent domains. In this work we consider how …
demonstrated capabilities in many coding-adjacent domains. In this work we consider how …
Deep learning-based software engineering: progress, challenges, and opportunities
Researchers have recently achieved significant advances in deep learning techniques,
which in turn has substantially advanced other research disciplines, such as natural …
which in turn has substantially advanced other research disciplines, such as natural …
On hardware security bug code fixes by prompting large language models
Novel AI-based code-writing Large Language Models (LLMs) such as OpenAI's Codex have
demonstrated capabilities in many coding-adjacent domains. In this work, we consider how …
demonstrated capabilities in many coding-adjacent domains. In this work, we consider how …
Understanding software-2.0: A study of machine learning library usage and evolution
Enabled by a rich ecosystem of Machine Learning (ML) libraries, programming using
learned models, ie, Software-2.0, has gained substantial adoption. However, we do not …
learned models, ie, Software-2.0, has gained substantial adoption. However, we do not …
Cryptoguard: High precision detection of cryptographic vulnerabilities in massive-sized java projects
Cryptographic API misuses, such as exposed secrets, predictable random numbers, and
vulnerable certificate verification, seriously threaten software security. The vision of …
vulnerable certificate verification, seriously threaten software security. The vision of …