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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Nunn, I., White, T.: The application of antigenic search techniques to time series forecasting. In: GECCO, pp. 353–360 (June 2005)
Wilson, W.O., Birkin, P., Aickelin, U.: Motif detection inspired by immune memory. In: ICARIS 2007. Proceedings of the 6th International Conference on Artificial Immune Systems, Santos, Brazil. LNCS, Springer, Heidelberg (2007)
de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)
Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: The 2nd workshop on temporal data mining, at the 8th ACM SIGKDD international conference on knowledge discovery and data mining (July 2002)
Guan, X., Uberbacher, E.C.: A fast look up algorithm for detecting repetitive DNA sequences. In: Pacific symposium on biocomputing, Hawaii IEEE Tran. Control Systems Tech. (December 1996)
Keogh, E., Smyth, P.: A probabilistic approach to fast pattern matching in time series databases. In: Proceedings of the third international conference of knowledge discovery and data mining, pp. 20–24 (1997)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time series databases. In: Proceedings of the SIGMOD conference, pp. 419–429 (1994)
Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: SIGKDD (August 2003)
Lin, J., Keogh, E., Lonardi, S.: Visualizing and discovering non trivial patterns in large time series databases. Information visualization 4(2), 61–82 (2005)
Tanaka, Y., Uehara, K.: Discover motifs in multi-dimensional time series using the principal component analysis and the MDL principle. In: 3rd international conference on machine learning and data mining in pattern recognition, Leipzig, Germany, pp. 252–265 (2003)
Fu, T.C., Chung, F.L., Ng, V., Luk, R.: Pattern discovery from stock market time series using self organizing maps. In: Workshop notes of KDD 2001 workshop on temporal data mining, San francisco, CA, pp. 27–37 (2001)
Forrest, S., Hofmeyr, S.A., Somayaji, A., Longstaff, T.A.: A sense of self for UNIX processes. In: IEEE Symposium on Security and Privacy, pp. 120–128. IEEE Computer Society Press, Oakland, CA (1996)
Sekar, R., Bowen, T., Segal, M.: On preventing intrusions by process behavior monitoring. In: Proceedings of the Workshop on Intrusion Detection and Network Monitoring, pp. 29–40. USENIX Association, Berkeley, CA (1999)
Warrender, C., Forrest, S., Pearlmutter, B.: Detecting intrusions using system calls: Alternative data models. In: Proceedings of the 1999 Conference on Security and Privacy (S&P-99), pp. 133–145. IEEE Press, Los Alamitos (1999)
Tandon, G., Chan, P., Mitra, D.: Morpheus: Motif oriented representations to purge hostile events from unlabeled sequences. In: Proceedings of the 2004 ACM workshop on Visualization and data mining for computer security, pp. 16–25. ACM Press, New York (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wilson, W.O., Feyereisl, J., Aickelin, U. (2007). Detecting Motifs in System Call Sequences. In: Kim, S., Yung, M., Lee, HW. (eds) Information Security Applications. WISA 2007. Lecture Notes in Computer Science, vol 4867. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77535-5_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-77535-5_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77534-8
Online ISBN: 978-3-540-77535-5
eBook Packages: Computer ScienceComputer Science (R0)