A Distributed Vulnerability Scanning on Machine Learning
Xiaopeng Tian, Di Tang · 2019
Traditional scanners are often isolated and single in the enterprise environment and cannot adapt to the development trend of future cypher security defense in depth, coordination, and system. With the emergence and development of ransomware and APT attacks, it is particularly important to design an automated vulnerability detection framework that identifies related threats. This paper designs a new distributed vulnerability detection framework based on machine learning technology, which optimizes scanning profiles, enhances new attack detection capabilities, and realizes linkage with other security devices to form a security management system for vulnerability detection, identification and interception.