SOURCE-LINKED INTELLIGENCE

Automated Vulnerability Detection in Software Development Using AI Techniques

CORDIS · observation · Publication date unknown

velopment framework. These ""tests"" are also valuable based in real time. The project aims to ensure more effectively uncover software vulnerabilities by combining static and symbolic analysis with artificial intelligence (AI) advances. The project objectives: 1. to analyse existing practices on minimizing positives and enhance method efficiency by harnessing AI capabilities, for results to decrease Cybersecurity problems. 2. to develop an AI-based model that improves bug detection accuracy by efficiently integrating symbolic execution with static analysis. 3. to develop AI based prototype on static and symbolic analysis improving penetration testing accuracy. AI-powered prototype will enhance current techniques by improving the resource-intensive symbolic execution process and minimizing false positives, which are frequently linked to static analysis. While current AI tools typically handle either symbolic analysis or static analysis, the AI model we will develop will cover both of them. AISSAM promises more comprehensive problem finding tool than either kind of analysis can

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recordType

award

status

SIGNED

region

EU

value

181136.16

unit

EUR