Analysis on Detecting Masquerade Attack using DDSGA: A Data-Driven semi-global Alignment Approach for Detecting Masquerade Attack

A masquerade aggressor impersonates a legal user to utilize the user services and privileges. The semi-global alignment algorithmic rule (SGA) is one among the foremost effective and efficient techniques to observe these attacks however it's not reached yet the accuracy and performance needed by massive scale, multiuser systems. To improve each the effectiveness and also the performances of this algorithmic rule, we propose the Data-Driven Semi-Global Alignment, DDSGA approach. From the protection effectiveness read purpose, DDSGA improves the rating systems by adopting distinct alignment parameters for every user. Moreover, it tolerates little mutations in user command sequences by permitting little changes within the low-level illustration of the commands practicality. It conjointly adapts to changes within the user behaviour by updating the signature of a user in line with its current behaviour. To optimize the runtime overhead, DDSGA minimizes the alignment overhead and parallelizes the detection and also the update.