Classifying System Call Traces using Anomalous Detection

We used data mining techniques to detect intrusions among system call traces and have outlined our results. Recent work at the intersection of security and machine learning has lead to better understanding of anomalous intrusion detection. There is a need to more thoroughly understand how anomaly detection can be used because of its potential applications and advantages over current standard methods. In this thesis, we report on a new approach of anomalous detection using system call traces. Our goal is to be able to create a system that can accurately detect hacking attacks by analyzing the sequences of system calls the operating system is performing. We will look at how this data can be processed to achieve correct detection of intrusions on a system. In the end, we will outline ways in which system call traces can be leveraged as well as what we can do and learn from these results