DATA-DRIVEN SEMI-GLOBAL ALIGNMENT TECHNIQUE FOR MASQUERADE DETECTION IN STAND-ALONE AND CLOUD COMPUTING SYSTEMS

摘要

Systems and methods are provided for intrusion detection, specifically, identifying masquerade attacks in large scale, multiuser systems, which improves the scoring systems over conventional masquerade detection systems by adopting distinct alignment parameters for each user. For example, the use of DDSGA may result in a masquerade intrusion detection hit ratio of approximately 88.4% with a small false positive rate of approximately 1.7%. DDSGA may also improve the masquerade intrusion detection hit ratio by about 21.9% over convention masquerade detection techniques and lower the Maxion-Townsend cost by approximately 22.5%. It will also improve the computational overhead.