Abstract
Network intrusions have become a big challenge to current network environment. Thus, network intrusion detection systems (NIDSs) are being widely deployed in various networks aiming to detect different kinds of network attacks (e.g., Trojan, worms). However, in real settings, a large number of alarms can be generated during the detection procedure, which greatly decrease the effectiveness of these intrusion detection systems. To mitigate this problem, we advocate that constructing an alarm filter is a promising solution. In this paper, we design and develop an intelligent alarm filter to help filter out NIDS alarms by means of knowledge-based alert verification. In particular, our proposed method of knowledge-based alert verification employs a rating mechanism in terms of expert knowledge to classify incoming NIDS alarms. We implemented and evaluated this intelligent knowledge-based alarm filter in a network environment. The experimental results show that the developed alarm filter can accurately filter out a number of NIDS alarms and achieve a better outcome.
This is a preview of subscription content, log in via an institution to check access.
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
Axelsson, S.: The Base-Rate Fallacy and the Difficulty of Intrusion Detection. ACM Transactions on Information and System Security, 186–205 (August 2000)
Symantec Corp., Internet Security Threat Report, vol. 16, http://www.symantec.com/business/threatreport/index.jsp (accessed on May 26, 2012)
Paxson, V.: Bro: A System for Detecting Network Intruders in Real-Time. Computer Networks 31(23-24), 2435–2463 (1999)
Scarfone, K., Mell, P.: Guide to Intrusion Detection and Prevention Systems (IDPS), pp. 800–894. NIST Special Publication (2007)
Vigna, G., Kemmerer, R.A.: NetSTAT: a Network-based Intrusion Detection Approach. In: Proceedings of Annual Computer Security Applications Conference (ACSAC), pp. 25–34. IEEE Press, New York (1998)
Roesch, M.: Snort: Lightweight Intrusion Detection for Networks. In: Proceedings of 13th Large Installation System Administration Conference (LISA), pp. 229–238. USENIX Association Berkeley, CA (1999)
Valdes, A., Anderson, D.: Statistical Methods for Computer Usage Anomaly Detection Using NIDES. Technical report, SRI International (January 1995)
Ghosh, A.K., Wanken, J., Charron, F.: Detecting Anomalous and Unknown Intrusions Against Programs. In: Proceedings of Annual Computer Security Applications Conference (ACSAC), pp. 259–267 (1998)
Ptacek, T.H., Newsham, T.N.: Insertion, Evation, and Denial of Service: Eluding Network Intrusion Detection. Technical Report, Secure Networks (January 1998)
McHugh, J.: Testing Intrusion Detection Systems: a Critique of the 1998 and 1999 Darpa Intrusion Detection System Evaluations as Performed by Lincoln Laboratory. ACM Transactions on Information System Security, 262–294 (2000)
Lippmann, R.P., Fried, D.J., Graf, I., Haines, J.W., Kendall, K.R., McClung, D., Weber, D., Webster, S.E., Wyschogrod, D., Cunningham, R.K., Zissman, M.A.: Evaluating Intrusion Detection Systems: the 1998 DARPA off-line Intrusion Detection Evaluation. In: Proceedings of DARPA Information Survivability Conference and Exposition, pp. 12–26 (2000)
Meng, Y., Kwok, L.-F.: Adaptive False Alarm Filter Using Machine Learning in Intrusion Detection. In: Wang, Y., Li, T. (eds.) Practical Applications of Intelligent Systems. AISC, vol. 124, pp. 573–584. Springer, Heidelberg (2011)
Snort-The Open Source Network Intrusion Detection System, http://www.snort.org/ (accessed on April 25, 2012)
Sommer, R., Paxson, V.: Outside the Closed World: On using Machine Learning for Network Intrusion Detection. In: Proceedings of IEEE Symposium on Security and Privacy, pp. 305–316 (2010)
Kruegel, C., Robertson, W.: Alert Verification: Determining the Success of Intrusion Attempts. In: Proceedings of Workshop on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA), pp. 25–38 (July 2004)
Zhou, J., Carlson, A.J., Bishop, M.: Verify Results of Network Intrusion Alerts Using Light-weight Protocol Analysis. In: Proceedings of Annual Computer Security Applications Conference (ACSAC), pp. 117–126 (December 2005)
Mu, C., Huang, H., Tian, S.: Intrusion Detection Alert Verification Based on Multi-level Fuzzy Comprehensive Evaluation. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-M., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 9–16. Springer, Heidelberg (2005)
Wireshark, Homepage, http://www.wireshark.org (accessed on April 10, 2012)
Pietraszek, T.: Using Adaptive Alert Classification to Reduce False Positives in Intrusion Detection. In: Jonsson, E., Valdes, A., Almgren, M. (eds.) RAID 2004. LNCS, vol. 3224, pp. 102–124. Springer, Heidelberg (2004)
Kruegel, C., Robertson, W., Vigna, G.: Using Alert Verification to Identify Successful Intrusion Attempts. Journal of Practice in Information Processing and Communication 27(4), 220–228 (2004)
Law, K.H., Kwok, L.-F.: IDS False Alarm Filtering Using KNN Classifier. In: Lim, C.H., Yung, M. (eds.) WISA 2004. LNCS, vol. 3325, pp. 114–121. Springer, Heidelberg (2005)
Meng, Y., Li, W.: Constructing Context-based Non-Critical Alarm Filter in Intrusion Detection. In: Proceedings of International Conference on Internet Monitoring and Protection (ICIMP), pp. 75–81 (2012)
Alharby, A., Imai, H.: IDS False Alarm Reduction Using Continuous and Discontinuous Patterns. In: Ioannidis, J., Keromytis, A.D., Yung, M. (eds.) ACNS 2005. LNCS, vol. 3531, pp. 192–205. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meng, Y., Li, W., Kwok, Lf. (2012). Intelligent Alarm Filter Using Knowledge-Based Alert Verification in Network Intrusion Detection. In: Chen, L., Felfernig, A., Liu, J., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2012. Lecture Notes in Computer Science(), vol 7661. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34624-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-34624-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34623-1
Online ISBN: 978-3-642-34624-8
eBook Packages: Computer ScienceComputer Science (R0)