[HTML][HTML] Artificial intelligence for cybersecurity: Literature review and future research directions
R Kaur, D Gabrijelčič, T Klobučar - Information Fusion, 2023 - Elsevier
Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate
repetitive tasks, accelerate threat detection and response, and improve the accuracy of their …
repetitive tasks, accelerate threat detection and response, and improve the accuracy of their …
Fuzzing: a survey for roadmap
Fuzz testing (fuzzing) has witnessed its prosperity in detecting security flaws recently. It
generates a large number of test cases and monitors the executions for defects. Fuzzing has …
generates a large number of test cases and monitors the executions for defects. Fuzzing has …
Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models
Deep Learning (DL) systems have received exponential growth in popularity and have
become ubiquitous in our everyday life. Such systems are built on top of popular DL …
become ubiquitous in our everyday life. Such systems are built on top of popular DL …
Combining graph-based learning with automated data collection for code vulnerability detection
This paper presents FUNDED (Flow-sensitive vUl-Nerability coDE Detection), a novel
learning framework for building vulnerability detection models. Funded leverages the …
learning framework for building vulnerability detection models. Funded leverages the …
Universal fuzzing via large language models
Fuzzing has achieved tremendous success in discovering bugs and vulnerabilities in
various software systems. Systems under test (SUTs) that take in programming or formal …
various software systems. Systems under test (SUTs) that take in programming or formal …
A survey on deep learning for software engineering
In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …
and an improved model training method to break the bottleneck of neural network …
The art, science, and engineering of fuzzing: A survey
Among the many software testing techniques available today, fuzzing has remained highly
popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of …
popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of …
Fairfuzz: A targeted mutation strategy for increasing greybox fuzz testing coverage
In recent years, fuzz testing has proven itself to be one of the most effective techniques for
finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing …
finding correctness bugs and security vulnerabilities in practice. One particular fuzz testing …
Collafl: Path sensitive fuzzing
Coverage-guided fuzzing is a widely used and effective solution to find software
vulnerabilities. Tracking code coverage and utilizing it to guide fuzzing are crucial to …
vulnerabilities. Tracking code coverage and utilizing it to guide fuzzing are crucial to …
[PDF][PDF] REDQUEEN: Fuzzing with Input-to-State Correspondence.
Automated software testing based on fuzzing has experienced a revival in recent years.
Especially feedback-driven fuzzing has become well-known for its ability to efficiently …
Especially feedback-driven fuzzing has become well-known for its ability to efficiently …