Paper Info
Title
Detecting motifs in system call sequences
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 system call language for measuring similarity between patterns of such calls is also suggested.
Year
Venue
Keywords
2010
WISA'07 Proceedings of the 8th international conference on Information security applications
low level system call,algorithm search,system call sequence,detecting motif,operating system,motif tracking algorithm,compress data,large system call data,higher level system call,time series data,small number,limited number,secure computation,artificial intelligent,evolutionary computing
DocType
Volume
ISSN
Journal
abs/1002.0432
Proceedings of the 8th International Workshop on Information Security Applications (WISA2007), Lecture Notes in Computer Science, Jeju, Korea
ISBN
Citations 
PageRank 
3-540-77534-X
4
0.43
References 
Authors
12
3
Name
Order
Citations
PageRank
William O. Wilson1395.95
Jan Feyereisl213110.20
Uwe Aickelin31679153.63