Abstract
Distributed Denial of Service (DDoS) plays a significant role in threatening the cloud-based services. DDoS is a kind of attack which targets the CPU, bandwidth and other resources and makes them unavailable to benign users. The DDoS attack has an enormous impact on multi-tenant cloud network than the traditional network due to the cloud features like virtualization, load balancing, resource scaling and migrations. These features spread attack effects in the whole cloud network, which introduces the collateral damages to the non-target stakeholders. Some of these stakeholders are co-hosted virtual machines (VMs), host physical server, co-hosted physical server, cloud service providers and users, etc. Therefore, there is a need for a method that can reduce such collateral damages. In this work, we focus on reducing VM level collateral damages caused to the co-hosted VMs residing with the victim VM on the same host. The proposed architecture consists of: (i) a request awareness based module to reduce VM level collateral damages, (ii) to obtain the request awareness, a novel Cuckoo Search based IDentification of Request (CS-IDR) method using bivariate flight is also proposed. The CS-IDR method helps in taking the request-aware decision, which eventually reduces VM level collateral damages. The result also shows that the proposed method minimizes the CPU usage, RAM usage, power consumption, overall load, and incurred cost caused due to DDoS attack on non-target co-hosted VM, and hence reduces such collateral damages.
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References
Aldossary, S., Allen, W.: Data security, privacy, availability and integrity in cloud computing: issues and current solutions. Int. J. Adv. Comput. Sci. Appl. 7(4), 485–498 (2016)
Somani, G., Gaur, M.S., Sanghi, D., Conti, M., Rajarajan, M., Buyya, R.: Combating DDoS attacks in the cloud: requirements, trends, and future directions. IEEE Cloud Comput. 4(1), 22–32 (2017)
Zlomisli, V., Fertalj, K., Sruk, V.: Denial of service attacks, defences and research challenges. Clust. Comput. 20(1), 661–671 (2017)
Gupta, B.B., Badve, O.P.: Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a cloud computing environment. Neural Comput. Appl. 28(12), 3655–3682 (2017)
Shaar, F., Efe, A.: DDoS attacks and impacts on various cloud computing components. Int. J. Inf. Secur. Sci. 7, 26–48 (2018)
Somani, G., Gaur, M.S., Sanghi, D., Conti, M.: DDoS attacks in cloud computing: collateral damage to non-targets. Comput. Netw. 109, 157–171 (2016)
Chen, Y., Hwang, K.:Collaborative change detection of DDoS attacks on community and ISP networks. International Symposium on Collaborative Technologies and Systems (CTS’06), Las Vegas, NV, 2006, pp. 401-410 (2006)
Zhang, H., Gu, Z., Liu, C., Jie, T.:Detecting VoIP-specific denial-of-service using change-point method. In: 11th International Conference on Advanced Communication Technology, Phoenix Park, 2009, pp. 1059–1064 (2009)
Feinstein, L., Schnackenberg, D., Balupari, R., Kindred, D.: Statistical approaches to DDoS attack detection and response. In: Proceedings DARPA Information Survivability Conference and Exposition, Washington, DC, USA, vol 1 pp. 303–314 (2003)
Moore, D., Shannon, C., Brown, D.J., Voelker, G.M., Savage, S.: Inferring internet denial-of-service activity. ACM Trans. Comput. Syst. 24(2), 115–139 (2006)
Yu, S., Thapngam, T., Liu, J., Wei, S., Zhou, W.: Discriminating DDoS flows from flash crowds using information distance. In: Third International Conference on Network and System Security, Gold Coast, QLD, 2009, pp. 351–356 (2009)
Hamdi, M., Boudriga, N.: Detecting Denial-of-Service attacks using the wavelet transform. Comput. Commun. 30(16), 3203–3213 (2007)
Carl, G., Brooks, R.R., Rai, S.: Wavelet based denial-of-service detection. Comput. Secur. 25(8), 600–615 (2006)
Lombardi, F., Di Pietro, R.: Secure virtualization for cloud computing. J. Netw. Comput Appl. 34(4), 1113–1122 (2011)
Somani, G., Gaur, M.S., Sanghi, D., Conti, M., Buyya, R.: Service resizing for quick DDoS mitigation in cloud computing environment. Ann. Telecommun. 72(5–6), 237–252 (2017)
Somani, G., Gaur, M.S., Sanghi, D., Conti, M., Rajarajan, M.: DDoS victim service containment to minimize the internal collateral damages in cloud computing. Comput. Electr. Eng. 59, 165–179 (2017)
Somani, G., Gaur, M.S., Sanghi, D., Conti, M., Rajarajan, M.: Scale inside-out: rapid mitigation of cloud DDoS attacks. IEEE Trans. Dependable Secure Comput. 15(6), 959–973 (2017)
Saxena, R., Dey, S.: DDoS attack prevention using collaborative approach for cloud computing. Clust. Comput. 23, 1329–1344 (2020)
Hezavehi, S.M., Rahmani, R.: An anomaly-based framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments. Clust Comput. 23, 2609–2627 (2020)
Verma, P., Tapaswi, S., Godfrey, W.W.: An adaptive threshold-based attribute selection to classify requests under DDoS attack in cloud-based systems. Arab. J. Sci. Eng. 45, 2813–2834 (2020)
Kesavamoorthy, R., Soundar, K.R.: Swarm intelligence based autonomous DDoS attack detection and defense using multi agent system. Clust. Comput. 22(4), 9469–9476 (2019)
Kim, H., Kim, J., Kim, Y., Kim, I., Kim, K.J.: Design of network threat detection and classification based on machine learning on cloud computing. Clust. Comput. 22, 1–10, (2018)
Wang, C., Yao, H., Liu, Z.: An efficient DDoS detection based on SU-Genetic feature selection. Clust. Comput. 22, 1–11 (2018).
Vidal, J.M., Orozco, A.L.S., Villalba, L.J.G.: Adaptive artificial immune networks for mitigating DoS flooding attacks. Swarm Evol. Comput. 38, 94–108 (2018)
Garg, S., Batra, S.: Fuzzified cuckoo based clustering technique for network anomaly detection. Comput. Electr. Eng. 71, 798–817 (2017)
Velliangiri, S., Premalatha, J.: Intrusion detection of distributed denial of service attack in cloud. Clust. Comput. 22(5), 10615–10623 (2019)
Velliangiri, S., Pandey, H.M.: Fuzzy-Taylor-elephant herd optimization inspired Deep Belief Network for DDoS attack detection and comparison with state-of-the-arts algorithms. Future Gener. Comput Syst. 110, 80–90 (2020)
Prasad, K.M., Reddy, A.R., Rao, K.V.: BIFAD: Bio-inspired anomaly based HTTP-flood attack detection. Wirel. Pers. Commun. 97(1), 281–308 (2017)
Buyya, R., Ranjan, R., Calheiros, R.N.: Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: challenges and opportunities. In: 2009 International Conference on High Performance Computing and Simulation, pp. 1–11 (2009)
Shehab, M., Khader, A.T., Al-Betar, M.A.: A survey on applications and variants of the cuckoo search algorithm. Appl. Soft Comput. 61, 1041–1059 (2017)
Yang, X.S., Deb, S.: Cuckoo search via Levy flights. In: World Congress on Nature and Biologically Inspired Computing, pp. 210–214 (2009)
Yang, X.S., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)
Mareli, M., Twala, B.: An adaptive cuckoo search algorithm for optimisation. Appl. Comput. Inform. 14(2), 107–115 (2017)
Zheng, H., Zhou, Y.Q.: A novel Cuckoo Search optimization algorithm base on Gauss distribution. J. Comput. Inf. Syst. 8, 4193–4200 (2012)
Zaw, M.M., Mon, E.E.: Web document clustering using Gauss distribution based cuckoo search clustering algorithm. Int. J. Sci. Eng. Technol. Res. 3(13), 2945–2949 (2014)
Thang, N.T.: Economic emission load dispatch with multiple fuel options using Hopfield Lagrange Network. Int. J. Adv. Sci. Technol. 57, 9–24 (2013)
Nguyen, T.T., Vo, D.N., Dinh, B.H.: Cuckoo search algorithm using different distributions for short-term hydrothermal scheduling with reservoir volume constraint. Int. J. Electr. Eng. Inform. 8(1), 76–92 (2016)
Tusiy, S.I., Shawkat, N., Ahmed, M., Panday, B., Sakib, N.: Comparative analysis on improved Cuckoo search algorithm and artificial Bee colony algorithm on continuous optimization problems. Int. J. Adv. Res. Artif. Intell. 4(2), 14–19 (2015)
Tuba, M., Subotic, M., Stanarevic, N.:Modified cuckoo search algorithm for unconstrained optimization problems. In: Proceedings of the 5th European conference on European Computing Conference on World Scientific and Engineering Academy and Society (WSEAS), pp. 263–268 (2011)
NSL-KDD Dataset. http://www.unb.ca/cic/datasets/nsl.html
Phyu, T.Z., Oo, N.N.: Performance comparison of feature selection methods. MATEC Web Conf. (2016). https://doi.org/10.1051/matecconf/20164206002
Jin, C., De-Lin, L., Fen-Xiang, M.: An improved ID3 decision tree algorithm. In: 4th International Conference on Computer Science and Education, pp. 127–130 (2009)
Lecture Notes on Bivariate Distribution. University of Washington, Department of Statistics. https://www.cl.cam.ac.uk/teaching/0708/Probabilty/prob10.pdf
Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A., Buyya, R.: CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithm. Softw. Pract. Exp. 41(1), 23–50 (2011)
Beloglazov, A., Buyya, R.: Managing overloaded hosts for dynamic consolidation of virtual machines in cloud data centers under quality of service constraints. IEEE Trans. Parallel Distrib. Syst. 24(7), 1366–1379 (2013)
Jena, U.K., Das, P.K., Kabat, M.R.: Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. J. King Saud Univ. Comput. Inf. Sci. (2020) https://doi.org/10.1016/j.jksuci.2020.01.012
Al-Haidari, F., Sqalli, M., Salah, K.: Evaluation of the impact of EDoS attacks against cloud computing services. Arab. J. Sci. Eng. 40(3), 773–785 (2015)
Amazon. Amazon EC2 Pricing (2017). https://aws.amazon.com/ec2/pricing/on-demand/
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Verma, P., Tapaswi, S. & Godfrey, W.W. A request aware module using CS-IDR to reduce VM level collateral damages caused by DDoS attack in cloud environment. Cluster Comput 24, 1917–1933 (2021). https://doi.org/10.1007/s10586-021-03234-2
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DOI: https://doi.org/10.1007/s10586-021-03234-2