Adaptive Deep Learning Framework for Privilege Escalation Attack Detection and Mitigation in Cloud Environments

Privilege escalation attacks in cloud computing pose a very significant security threat, which sometimes leads to unauthorized access and leakage of data. This paper proposes the use of deep learning-based techniques for the system to detect such attacks by evaluating user behaviour patterns and access activities. The technique uses supervised learning to identify already known attack vectors from historical data, while other unsupervised learning techniques identify previously unknown threats. The system integrates into cloud security frameworks to automate the response actions and, thus, provide real-time enforcement of security policies. This methodology reduces false positives and decreases manual intervention, resulting in a dynamic and adaptive approach to cloud security. The system improves threat detection accuracy, operational efficiency, and general protection against privilege escalations initiated by unauthorized activity in an ever-changing threat landscape.