Computer & Network Security - , ,
We have developed a high-reliability, automated approach to analyzing software code for vulnerabilities that is a leap forward in technology when compared to competing commercial solutions. We provide an efficient Machine Learning approach to identify both true positives and false positives with very high accuracy: greater than 90%. This solution reduces the time required to find vulnerabilities by a factor of ten (10) or more, and the results are highly reliable. Why ML4Cyber?Save money, time, and resources by identifying only the exploitable and relevant findings of SAST tools for a fraction of the cost and time!Maryland Innovation Initiative (MII) Funded Award ProjectMinority Business Enterprise (MBE)Licensed technology from the University of Maryland
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