Detecting Motifs in System Call Sequences

TL;DR

This paper introduces the Motif Tracking Algorithm, an immune-inspired method for discovering unknown repeating patterns in time series data, demonstrated on Linux system call sequences for security profiling.

Contribution

The paper presents a novel, data-independent motif detection algorithm that effectively summarizes system call sequences for security analysis.

Findings

Successfully identified motifs in large system call datasets

Generated process profiles from discovered motifs

Highlighted potential for security applications

Abstract

The search for patterns or motifs in data represents an area of key interest to many researchers. In this paper we present the Motif Tracking Algorithm, a novel immune inspired pattern identification tool that is able to identify unknown motifs which repeat within time series data. The power of the algorithm is derived from its use of a small number of parameters with minimal assumptions. The algorithm searches from a completely neutral perspective that is independent of the data being analysed, and the underlying motifs. In this paper the motif tracking algorithm is applied to the search for patterns within sequences of low level system calls between the Linux kernel and the operating system's user space. The MTA is able to compress data found in large system call data sets to a limited number of motifs which summarise that data. The motifs provide a resource from which a profile of executed processes can be built. The potential for these profiles and new implications for security research are highlighted. A higher level call system language for measuring similarity between patterns of such calls is also suggested.