Abstract
Distributed Denial of Service (DDoS) attacks are a serious threat to Internet security. A lot of research effort focuses on having detection and prevention methods on the victim server side or source side. The Bloom filter is a space-efficient data structure used to support pattern matching problems. The filter is utilised in network applications for deep packet inspection of headers and contents and also looks for predefined strings to detect irregularities. In intrusion detection systems, the accuracy of pattern matching algorithms is crucial for dependable detection of matching pairs, and its complexity usually poses a critical performance bottleneck. In this paper, we will propose a novel Dual Counting Bloom Filter (DCBF) data structure to decrease false detection of matching packets applicable for the \(\textit{SACK}^2\) algorithm. A theoretical evaluation will determine the false rate probability of detection and requirements for increased memory. The proposed approach significantly reduces the false rate compared to previously published results. The results indicate that the increased complexity of the DCBF does not affect efficient implementation of hardware for embedded systems that are resource constrained. The experimental evaluation was performed using extensive simulations based on real Internet traces of a wide area network link, and it was subsequently proved that DCBF significantly reduces the false rate.
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Cisco. (2016). Annual Security Report 2016. http://www.cisco.com/c/dam/assets/offers/pdfs/cisco-asr-2016.pdf. Accessed Jan 2016.
Zhang, G., Fischer-Hübner, S., & Ehlert, S. (2010). Blocking attacks on SIP VoIP proxies caused by external processing. Telecommunication Systems, 45(1), 61–76.
Sun, C., Fan, J., & Liu, B. (2007). A robust scheme to detect SYN flooding attacks. In Second International Conference on Communications and Networking (pp. 397–401).
Li, L., & Lee, G. (2005). DDoS attack detection and wavelets. Telecommunication Systems, 28(3–4), 435–451.
Zlomislić, V., Fertalj, K., & Sruk, V. (2017). Denial of service attacks, defences and research challenges. Cluster Computing The Journal of Networks, Software Tools and Applications, 20(1), 1–11.
DDoS Attacks in Q4 2015. Kaspersky Lab Report. https://securelist.com/analysis/quarterly-malware-reports/73414/kaspersky-ddos-intelligence-report-for-q4-2015/. Accessed Jan 2016.
Markku, A., Aura, T., & Särelä, M. (2014). Denial-of-service attacks in Bloom-filter-based forwarding. IEEE/ACM Transactions on Networking (TON), 22(5), 1463–1476.
Mehdi, M. A., & Amphawan, A. (2012). Review of syn-flooding attack detection mechanism. International Journal of Distributed & Parallel Systems, 3(1), 99–117.
Scarfone, K., & Mell, P. (2010). Guide to intrusion detection and prevention systems (IDPS) (NIST SP 800-94). Washington, DC: Computer Security Resource Center, National Institute of Standards and Technology, U.S. Department of Commerce.
Wang, G., Xu, M., & Huan, X. (2012). Design and implementation of an embedded router with packet filtering. In Proceedings—2012 IEEE Symposium on Electrical and Electronics Engineering, EEESYM 2012 (pp. 285–288).
Mittal, A., Shrivastava, A. K., & Manoria, M. (2011). A review of DDOS attack and its countermeasures in TCP based networks. International Journal of Computer Science & Engineering Survey (IJCSES), 2(4), 177–187.
Ma, X., & Chen, Y. (2014). DDoS detection method based on chaos analysis of network traffic entropy. IEEE Communications Letters, 18(1), 114–117.
Broder, A., & Mitzenmacher, M. (2003). Network application of Bloom filters: A survey. Internet Mathematics, 1(4), 485–509.
Sun, C., Hu, C., Tang, Yi, & Liu, B. (2009). More accurate and fast SYN flood detection. In Proceedings of 18th International Conference on Computer Communications and Networks (pp. 1–6).
Farkaz, F., & Halasz, S. (2006). Embedded fuzzy controller for industrial applications. Acta Polytechnica Hungarica, 3(2), 41–63.
Xia, Z., Lu, S., Li, J., & Tang, J. (2010). Enhancing DDoS flood attack detection via intelligent fuzzy logic. Informatica (Slovenia) An International Journal of Computing and Informatics, 34(4), 497–507.
Kawahara, R., Ishibashi, K., Mori, T., Kamiyama, N., Harada, S., & Asano, S. (2007). Detection accuracy of network anomalies using sampled flow statistics. In Global Telecommunications Conference 2007, GLOBE-COM ’07 (pp. 1959–1964). IEEE.
Kanwal, G., & Rshma, C. (2011). Detection of DDoS attack using data mining. International Journal of Computing and Business Research (IJCBR), 2(1), 1–10.
Prathibha, R. C., & Rejimol Robinson, R. R. (2014). A comparative study of defense mechanisms against SYN flooding attack. International Journal of Computer Applications, 98(1), 16–21.
Fall, R. K., & Stevens, R. W. (2012). TCP/IP illustrated, volume 1: The protocols. Addison-Wesley Professional Computing Series. New York: Pearson Education.
Sun, C., Fan, J., Shi, L., & Liu, B. (2007). A novel router-based scheme to mitigate SYN flooding DDoS attacks. In IEEE INFOCOM (Poster), Anchorage, Alaska, USA
Kompella, R., Singh, S., & Varghese, G. (2007). On scalable attack detection in the network. IEEE/ACM Transactions on Networking, 15(1), 14–25.
Chen, W., Yeung, D. Y. (2006). Defending against TCP SYN flooding attacks under different types of IP spoofing. In International Conference on Mobile Communications and Learning Technologies (ICNICONSMCL06) (pp. 38–42).
Chen, W., & Yeung, D. Y. (2006). Throttling spoofed SYN flooding traffic at the source. Telecommunication Systems, 33(1), 47–65.
Nashat, D., Juang, X., & Horiguchi, S. (2008). Router based detection for low-rate agents of DDoS attack. In 2008 International Conference on High Performance Switching and Routing (pp. 177–182).
Ling, Y., Gu, Y., & Wei, G. (2009). Detect SYN flooding attack in edge routers. International Journal of Security and its Applications, 3(1), 31–45.
Sun, C., Hu, C., & Liu, B. (2013). \(\mathit{SACK}^2\): Effective SYN flood detection against skillful spoofs. IET Information Security, 6(3), 149–156.
Halagan, T., Kovacik, T., Truchly, P., & Binder, A. (2015). Syn flood attack detection and type distinguishing mechanism based on Counting Bloom Filter. In Information and Communication Technology: Third IFIP TC 5/8 International Conference, ICT-EurAsia 2015, and 9th IFIP WG 8.9 Working Conference, CONFENIS 2015, Held as Part of WCC 2015, Daejeon, Korea, 4–7 Oct 2015, Proceedings (pp. 30–39). Springer, New York.
Alzahrani, A. B., Vassilakis, G. V., & Reed, J. M. (2014). Selecting Bloom-filter header lengths for secure information centric networking. In 2014 9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP) (pp. 628–633). IEEE.
Alzahrani, B., Vassilakis, V., Alreshoodi, M., Alarfaj, F., & Alhindi, A. (2016). Proactive detection of DDOS attacks in Publish-Subscribe networks. International Journal of Network Security & Its Applications (IJNSA), 8(4), 1–15.
Blustein, J., & El-Maazawi, A. (2002). Bloom filters—A tutorial, analysis, and survey. Faculty of Computer Science, Dalhousie University. https://www.cs.dal.ca/sites/default/files/technical_reports/CS-2002-10.pdf. Accessed Jan 2016.
Ramakrishna, M. V., Fu, E., & Bahcekapili, E. (1997). Efficient hardware hashing functions for high performance computers. IEEE Transactions on Computers, 46(12), 1378–1381.
Ramakrishna, M. V., Fu, E., & Bahcekapili, E. (1994). A performance study of hashing functions for hardware applications. In Proceedings of International Conference on Computing and Information (pp. 1621–1636).
Harwayne-Gidansky, J., Stefan, D., & Dalal, I. (2009). FPGA-based SoC for real-time network intrusion detection using Counting Bloom Filters. In IEEE Southeastcon 2009 (pp. 452–458).
Tabataba, F.S., & Hashemi, M.R. (2011). Improving false positive in Bloom filter. In 2011 19th Iranian Conference on Electrical Engineering (pp. 1–5).
Rottenstreich, O., Kanizo, Y., & Keslassy, I. (2014). The variable increment counting Bloom filter. IEEE/ACM Transactions on Networking, 22(4), 1092–1105.
Särelä, M., Rothenberg, C. E., Aura, T., Zahemszky, A., Nikander, P., & Ott, J. (2011). Forwarding anomalies in Bloom filter-based multicast. In INFOCOM, 2011 Proceedings IEEE (pp. 2399–2407).
Fan, L., Cao, P., Almeida, J., & Broder, A. Z. (2000). Summary cache: A scalable wide-area web cache sharing protocol. IEEE/ACM Transactions on Networking (TON), 8(3), 281–293.
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The authors would like to thank Central Informatics Support staff at Zagreb University of Applied Sciences for gathering the data.
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Dodig, I., Sruk, V. & Cafuta, D. Reducing false rate packet recognition using Dual Counting Bloom Filter. Telecommun Syst 68, 67–78 (2018). https://doi.org/10.1007/s11235-017-0375-3
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DOI: https://doi.org/10.1007/s11235-017-0375-3