Anomaly Detection Using New Tracing Tricks on Program Executions and Analysis of System Data

Now the security of information and applications is getting abnormal attention in the public. Because the millions of expenditure spending to combat on continuous threats. The threats (anomalies) are widely occurred at programming scope by exploitation of coding and other side is at application scope due to bad structure of development. Today various machine learning techniques are applied over application level behavior to discriminate the anomalies, but not much work is done in coding exploits. So in this paper, we have given some rich extension work to detect wide range of anomalies at coding exploits. Here, we used some standard tracing tricks and tools available in Linux platform, which describe how to observe the behavior of program execution's outcomes and model the necessary information collected from system as part of active learning. The experimental work done on various codes of artificial programs, Linux commands and also compared their performance on artificial datasets collected while program normal runs.