Abstract
ADFA Linux data set (ADFA-LD) is released recently for substituting the existing benchmark data sets in the area of host-based anomaly detection which have lost most of their relevance to modern computer systems. ADFA-LD is composed of thousands of system call traces collected from a contemporary Linux local server, with six types of up-to-date cyber attack involved. Previously, we have conducted a preliminary analysis of ADFA-LD, and shown that the frequency-based algorithms can be realised at a cheaper computational cost in contrast with the short sequence-based algorithms, while achieving an acceptable performance. In this paper, we further exploit the potential of the frequency-based algorithms, in attempts to reduce the dimension of the frequency vectors and identify the optimal distance functions. Two typical frequency-based algorithms, i.e., k-nearest neighbour (kNN) and k-means clustering (kMC), are applied to validate the effectiveness and efficiency.
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References
Stavroulakis, P., Stamp, M.: Handbook of information and communication security. Springer (2010)
http://www.ll.mit.edu/mission/communications/cyber/CSTcorpora/ideval/data/
Forrest, S., Hofmeyr, S., Somayaji, A., Longstaff, T.A.: A sense of self for Unix processes. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy, pp. 120–128 (1996)
Kosoresow, A.P., Hofmeyer, S.A.: Intrusion detection via system call traces. IEEE Software 14, 35–42 (1997)
Forrest, S., Hofmeyr, S., Somayaji, A.: The Evolution of System-Call Monitoring. In: Annual Computer Security Applications Conference, ACSAC 2008, pp. 418–430 (2008)
Eskin, E., Wenke, L., Stolfo, S.J.: Modeling system calls for intrusion detection with dynamic window sizes. In: Proceedings of the DARPA Information Survivability Conference Exposition II, DISCEX 2001, pp. 165–175 (2001)
Hoang, X.D., Hu, J.: An efficient hidden Markov model training scheme for anomaly intrusion detection of server applications based on system calls. In: Proceedings of the 12th IEEE International Conference on Networks (ICON 2004), pp. 470–474 (2004)
Hoang, X.D., Hu, J., Bertok, P.: A program-based anomaly intrusion detection scheme using multiple detection engines and fuzzy inference. Journal of Network and Computer Applications 32, 1219–1228 (2009)
Creech, G., Hu, J.: Generation of a new IDS test dataset: Time to retire the KDD collection. In: 2013 IEEE Wireless Communications and Networking Conference (WCNC), pp. 4487–4492 (2013)
Creech, G., Hu, J.: A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguous and Discontiguous System Call Patterns. IEEE Transactions on Computers 63, 807–819 (2014)
Liao, Y., Vemuri, V.R.: Use of K-nearest neighbor classifier for intrusion detection. Computers & Security 21, 439–448 (2002)
Chen, W.-H., Hsu, S.-H., Shen, H.-P.: Application of SVM and ANN for intrusion detection. Computers & Operations Research 32, 2617–2634 (2005)
Sharma, A., Pujari, A.K., Paliwal, K.K.: Intrusion detection using text processing techniques with a kernel based similarity measure. Computers & Security 26, 488–495 (2007)
Xie, M., Hu, J.: Evaluating host-based anomaly detection systems: A preliminary analysis of ADFA-LD. In: 2013 6th International Congress on Image and Signal Processing (CISP), pp. 1711–1716 (2013)
Jolliffe, I.: Principal component analysis. Wiley Online Library (2005)
Xie, M., Han, S., Tian, B.: Highly Efficient Distance-Based Anomaly Detection through Univariate with PCA in Wireless Sensor Networks. In: 2011 IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 564–571 (2011)
Xie, M., Hu, J., Tian, B.: Histogram-Based Online Anomaly Detection in Hierarchical Wireless Sensor Networks. In: 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 751–759 (2012)
Xie, M., Hu, J., Han, S., Chen, H.-H.: Scalable Hypergrid k-NN-Based Online Anomaly Detection in Wireless Sensor Networks. IEEE Transactions on Parallel and Distributed Systems 24, 1661–1670 (2013)
Hu, J., Gingrich, D., Sentosa, A.: A k-Nearest Neighbor Approach for User Authentication through Biometric Keystroke Dynamics. In: IEEE International Conference on Communications, ICC 2008, pp. 1556–1560 (2008)
Hartigan, J.A., Wong, M.A.: Algorithm AS 136: A k-means clustering algorithm. Applied Statistics, 100–108 (1979)
Mahmood, A.N., Hu, J., Tari, Z., Leckie, C.: Critical infrastructure protection: Resource efficient sampling to improve detection of less frequent patterns in network traffic. Journal of Network and Computer Applications 33, 491–502 (2010)
Xi, K., Tang, Y., Hu, J.: Correlation keystroke verification scheme for user access control in cloud computing environment. The Computer Journal 54, 1632–1644 (2011)
Lloyd, S.: Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 129–137 (1982)
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Xie, M., Hu, J., Yu, X., Chang, E. (2014). Evaluating Host-Based Anomaly Detection Systems: Application of the Frequency-Based Algorithms to ADFA-LD. In: Au, M.H., Carminati, B., Kuo, CC.J. (eds) Network and System Security. NSS 2015. Lecture Notes in Computer Science, vol 8792. Springer, Cham. https://doi.org/10.1007/978-3-319-11698-3_44
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DOI: https://doi.org/10.1007/978-3-319-11698-3_44
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