ML Cyber Security: Making the Digital Safe
U.S. patent holder for reducing false positive results in static code analysis!
U.S. patent holder for reducing false positive results in static code analysis!
Our information society is driven by software created by organizations in the private and public sector. These software applications are rarely perfect. They often contain cyber security vulnerabilities (software “bugs”) which when exploited by malicious attackers can lead to dangerous intrusions of computer systems. 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 propose an efficient Machine Learning approach to identify both true positives and false positives with very high accuracy: greater than 90%. This valuable solution reduces the time required to find vulnerabilities by a factor of ten (10) or more, and the results are highly reliable.
We guarantee customer success by providing specialized support to meet customer needs. Our team will guide you on every step of developing a machine learning solution that will improve true and false positive vulnerability detection while reducing significantly time, resources and cost.
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Sat | Closed | |
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