Moreover, scanning software can report thousands of issues, which makes performing operations, such as analysis and prioritization, very costly from an organizational point of view. In this paper, we ...

The driving force of the digital world is written code that is known as software. Software engineers meticulously write thousands of lines of code over a period of time according to the problem ...

Current research in software vulnerability detection includes frameworks like GRACE and ChatGPT-driven models that leverage deep learning and LLMs for better detection accuracy. These approaches ...

The third iteration of the Exploit Prediction Scoring System (EPSS) performs 82% better than previous versions, giving companies a better tool for evaluating vulnerabilities and prioritizing patching.

This replication experiment aims to understand how software vulnerability detection is implemented in a deep learning settings using source codes as the input features. An eye opening example that ...

Analyzing large data sets: Machine learning algorithms can process vast amounts of data to identify patterns and anomalies in system configurations, aiding in the detection of potential security ...

Transforming Information Security: The Architect of Advanced Vulnerability Management Using Automated Machine Learning Techniques Created by Carl Williams Updated: Aug 30 2024, 01:19 AM EDT ...