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IDS Alert Priority Determination Based on Traffic Behavior

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11689))

Abstract

With the increase in the variety of devices connected to the Internet, each with their own vulnerabilities, we are currently observing an explosion of cyber attacks patterns. Furthermore, the overwhelming number of alerts from security sensors, such as intrusion detection systems (IDSs), makes it impossible to take appropriate countermeasures against attacks. A method to prioritize IDS alerts is therefore required for the next generation of security operation centers (SOCs). To this end, we have developed an IDS alert priority determination method that combines IDS alert information with traffic behavior and uses the difference in the distribution of traffic behavior to determine the priority of the alerts. We performed experiments with 2 million IDS alerts and 20 billion traffic flows in a real large-scale environment over two months and found that our method could identify 553 IDS alerts out of 2 million as high priority, which is a small enough number for SOC analysts to investigate them in detail.

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Notes

  1. 1.

    SQLMAP: http://sqlmap.org.

  2. 2.

    Shodan: https://www.shodan.io

    Rapid7: https://www.rapid7.com

    Shadowserver: https://shadowserver.org/wiki.

  3. 3.

    Signature name provided by Palo Alto Networks.

References

  1. Denning, D.E.: An intrusion-detection model. J. IEEE Trans. Softw. Eng. 13, 222–232 (1987)

    Article  Google Scholar 

  2. Lunt, T.F., Jagannathan, R., Lee, R., Whitehurst, A., Listgarten, S.: Knowledge-based intrusion detection. In: AI Systems in Government Conference, Washington, USA, pp. 102–107 (1989)

    Google Scholar 

  3. Garcia-Teodoro, P., Diaz-Verdejp, J., Macia-Fernandez, G., Vazquez, E.: Anomaly-based network intrusion detection: techniques, systems and challenges. J. Comput. Secur. 28, 18–28 (2009)

    Article  Google Scholar 

  4. Lv, Y., Xiang, S., Geng, J., Li, Y., Xia, C.: An alert correlation algorithm based on the sequence pattern mining. In: 2015 IEEE Advanced Technology, Electronic and Automation Control Conference, Chongqing, China, pp. 1146–1151 (2015)

    Google Scholar 

  5. Pei, J., Han, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.-C.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: 17th International Conference on Data Engineering, Heidelberg, Germany, pp. 215–224 (2001)

    Google Scholar 

  6. Wang, L., Liu, A., Jajodia, S.: Using attack graphs for correlating, hypothesizing, and predicting intrusion alerts. J. Comput. Commun. 29, 2917–2933 (2006)

    Article  Google Scholar 

  7. Yemini, S.A., Kliger, S., Mozes, E., Yemini, Y., Ohsie, D.: High speed and robust event correlation. J. IEEE Commun. Mag. 34, 82–90 (1996)

    Article  Google Scholar 

  8. Zan, X., Gao, F., Han, J., Sun, Y.: A hidden Markov model based framework for tracking and predicting of attack intention. In: 2009 International Conference on Multimedia Information Networking and Security, Hubei, China, pp. 498–501 (2009)

    Google Scholar 

  9. Zhicai, S., Yongxiang, X.: A novel hidden Markov model for detecting complicate network attacks. In: 2010 IEEE International Conference on Wireless Communications, Networking and Information Security, Beijing, China, pp. 312–315 (2010)

    Google Scholar 

  10. Steinder, M., Sethi, A.S.: Probabilistic fault localization in communication systems using belief networks. J. IEEE/ACM Trans. Netw. 12, 809–822 (2004)

    Article  Google Scholar 

  11. Shittu, R., Healing, A., Ghanea-Hercock, R., Bloomfield, R., Muttukrishnan, R.: OutMet: a new metric for prioritising intrusion alerts using correlation and outlier analysis. In: 39th Annual IEEE Conference on Local Computer Networks, Edmonton, Canada, pp. 322–330 (2014)

    Google Scholar 

  12. Njogu, H.W., Jiawei, L.: Using alert cluster to reduce IDS alerts. In: 2010 3rd International Conference on Computer Science and Information Technology, Chengdu, China, pp. 467–471 (2010)

    Google Scholar 

  13. Vaarandi, R., Podins, K.: Network IDS alert classification with frequent itemset mining and data clustering. In: 2010 International Conference on Network and Service Management, Niagara Falls, Canada, pp. 451–456 (2010)

    Google Scholar 

  14. GhasemiGol, M., Ghaemi-Bafghi, A.: A new alert correlation framework based on entropy. In: 3rd International eConference on Computer and Knowledge Engineering, Mashhad, Iran, pp. 184–189 (2013)

    Google Scholar 

  15. Gupta, D., Joshi, P.S., Bhattacharjee, A.K., Mundada, R.S.: IDS alerts classification using knowledge-based evaluation. In: 2012 Fourth International Conference on Communication Systems and Networks, Bangalore, India, pp. 1–8 (2012)

    Google Scholar 

  16. Mell, P., Scarfone, K., Romansky, S.: A Complete Guide to the Common Vulnerability Scoring System Version 2.0, National Infrastracture Advisory Council. https://ws680.nist.gov/publication/get_pdf.cfm?pub_id=51198. Accessed 15 Feb 2019

  17. The Global Internet Phenomena Report. https://www.sandvine.com/hubfs/downloads/phenomena/2018-phenomena-report.pdf. Accessed 15 Feb 2019

  18. Uncovering Hidden Threats within Encrypted Traffic. https://www.a10networks.com/sites/default/files/A10-EB-14106-EN.pdf. Accessed 15 Feb 2019

  19. Evangelos, S., Jiawei, H., Usama, M.F.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: The Second International Conference on Knowledge Discovery and Data Mining, Oregon, USA, pp. 226–231 (1996)

    Google Scholar 

  20. How many Alerts is Too Many to Handle?. https://www2.fireeye.com/StopTheNoise-IDC-Numbers-Game-Special-Report.html. Accessed 5 Jun 2019

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Correspondence to Shohei Hiruta .

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Hiruta, S., Ikeda, S., Shima, S., Takakura, H. (2019). IDS Alert Priority Determination Based on Traffic Behavior. In: Attrapadung, N., Yagi, T. (eds) Advances in Information and Computer Security. IWSEC 2019. Lecture Notes in Computer Science(), vol 11689. Springer, Cham. https://doi.org/10.1007/978-3-030-26834-3_11

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  • DOI: https://doi.org/10.1007/978-3-030-26834-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-26833-6

  • Online ISBN: 978-3-030-26834-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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