AISSAM · Automated Vulnerability Detection in Software Development Using AI Techniques

In today's software development problematic become new security threats that always appearing. To combat this, we can integrate penetration testing right into the development framework. With penetration testing, we can make the program mimic an attacker and simulate several different ways an attacker would try to breach the system. This gives us some immediate and valuable feedback on the system's potential weaknesses. Even when we use additional tools to do the actual pen testing, we can still derive value from doing them in the development 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 deliver on its own. Technological impact will be made on up to 20 SME’s and will significantly increase efficiency of bug detection accuracy by efficiently integrating symbolic execution. SME’s will get also an economic impact by saving funding for cybersecurity challenges solving. ""