Abstract
Malware is every malicious code that has the potential to harm any computer or network. The amount of malware is increasing faster every year and poses a serious security threat. Hence, malware detection has become a critical topic in computer security. Currently, signature-based detection is the most extended method within commercial antivirus. Although this method is still used on most popular commercial computer antivirus software, it can only achieve detection once the virus has already caused damage and it is registered. Therefore, it fails to detect new variations of known malware. In this paper, we propose a new method to detect variants of known malware families. This method is based on the frequency of appearance of opcode sequences. Furthermore, we describe a method to mine the relevance of each opcode and, thereby, weigh each opcode sequence frequency. We show that this method provides an effective way to detect variants of known malware families.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Karsperky-Labs: Kaspersky Security Bulletin: Statistics 2008 (2009)
Christodorescu, M., Jha, S.: Static analysis of executables to detect malicious patterns. In: Proceedings of the 12th USENIX Security Symposium, February 2003, pp. 169–186 (2003)
Morley, P.: Processing virus collections. In: Proceedings of the 2001 Virus Bulletin Conference (VB 2001), Virus Bulletin, pp. 129–134 (2001)
Bilar, D.: Opcodes as predictor for malware. International Journal of Electronic Security and Digital Forensics 1(2), 156–168 (2007)
VX heavens (2009), http://vx.netlux.org/ (Last accessed: September 29, 2009)
NewBasic - An x86 Assembler/Disassembler for DOS, http://www.frontiernet.net/~fys/newbasic.htm (Last accessed: September 29, 2009)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1226–1238 (2005)
McGill, M., Salton, G.: Introduction to modern information retrieval. McGraw-Hill, New York (1983)
Tata, S., Patel, J.: Estimating the Selectivity of tf-idf based Cosine Similarity Predicates. SIGMOD Record 36(2), 75–80 (2007)
Carrera, E., Erdélyi, G.: Digital genome mapping–advanced binary malware analysis. In: Virus Bulletin Conference, pp. 187–197 (2004)
Ashcraft, K., Engler, D.: Using programmer-written compiler extensions to catch security holes. In: Proceedings of the 23rd IEEE Symposium on Security and Privacy, pp. 143–159 (2002)
Schultz, M., Eskin, E., Zadok, F., Stolfo, S.: Data mining methods for detection of new malicious executables. In: Proceedings of the 22nd IEEE Symposium on Security and Privacy, pp. 38–49 (2001)
Kolter, J.Z., Maloof, M.A.: Learning to detect malicious executables in the wild. In: Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD), pp. 470–478. ACM, New York (2004)
Santos, I., Penya, Y., Devesa, J., Bringas, P.: N-Grams-based file signatures for malware detection. In: Proceedings of the 11th International Conference on Enterprise Information Systems (ICEIS), Volume AIDSS, pp. 317–320 (2009)
Christodorescu, M., Jha, S., Seshia, S., Song, D., Bryant, R.: Semantics-aware malware detection. In: Proceedings of the 2005 IEEE Symposium on Security and Privacy, pp. 32–46 (2005)
Cavallaro, L., Saxena, P., Sekar, R.: On the limits of information flow techniques for malware analysis and containment. In: Zamboni, D. (ed.) DIMVA 2008. LNCS, vol. 5137, pp. 143–163. Springer, Heidelberg (2008)
Bayer, U., Moser, A., Kruegel, C., Kirda, E.: Dynamic analysis of malicious code. Journal in Computer Virology 2(1), 67–77 (2006)
King, S., Chen, P.: SubVirt: Implementing malware with virtual machines. In: 2006 IEEE Symposium on Security and Privacy, pp. 314–327 (2006)
Willems, C., Holz, T., Freiling, F.: Toward automated dynamic malware analysis using cwsandbox. IEEE Security & Privacy 5(2), 32–39 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Santos, I. et al. (2010). Idea: Opcode-Sequence-Based Malware Detection. In: Massacci, F., Wallach, D., Zannone, N. (eds) Engineering Secure Software and Systems. ESSoS 2010. Lecture Notes in Computer Science, vol 5965. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11747-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-11747-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11746-6
Online ISBN: 978-3-642-11747-3
eBook Packages: Computer ScienceComputer Science (R0)