Software vulnerability analysis and discovery using machine-learning and data-mining techniques: A survey
SM Ghaffarian, HR Shahriari - ACM computing surveys (CSUR), 2017 - dl.acm.org
Software security vulnerabilities are one of the critical issues in the realm of computer
security. Due to their potential high severity impacts, many different approaches have been …
security. Due to their potential high severity impacts, many different approaches have been …
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 …
Dos and don'ts of machine learning in computer security
With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …
massive datasets, machine learning algorithms have led to major breakthroughs in many …
Software vulnerability detection using deep neural networks: a survey
The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …
important concern in the software industry and in the field of cybersecurity, suggesting that …
Deepwukong: Statically detecting software vulnerabilities using deep graph neural network
Static bug detection has shown its effectiveness in detecting well-defined memory errors, eg,
memory leaks, buffer overflows, and null dereference. However, modern software systems …
memory leaks, buffer overflows, and null dereference. However, modern software systems …
Vuldeepecker: A deep learning-based system for vulnerability detection
The automatic detection of software vulnerabilities is an important research problem.
However, existing solutions to this problem rely on human experts to define features and …
However, existing solutions to this problem rely on human experts to define features and …
Sysevr: A framework for using deep learning to detect software vulnerabilities
The detection of software vulnerabilities (or vulnerabilities for short) is an important problem
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …
that has yet to be tackled, as manifested by the many vulnerabilities reported on a daily …
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 …
An Abstract Syntax Tree based static fuzzing mutation for vulnerability evolution analysis
Context: Zero-day vulnerabilities are highly destructive and sudden. However, traditional
static and dynamic testing methods cannot efficiently detect them. Objective: In this paper, a …
static and dynamic testing methods cannot efficiently detect them. Objective: In this paper, a …
Vuldeelocator: a deep learning-based fine-grained vulnerability detector
Automatically detecting software vulnerabilities is an important problem that has attracted
much attention from the academic research community. However, existing vulnerability …
much attention from the academic research community. However, existing vulnerability …