Adaptive Model to Detect Anomaly and Real-Time Attacks in Cloud Environment Using Data Mining Algorithm

doi:10.23940/ijpe.21.10.p7.889899

Abstract

Abstract: The potential benefits and enhancement of services have made cloud computing an attractive field in the current era. Cloud computing has various benefits that has grabbed a focal point among researchers. Cloud service providers pose numerous security challenges and are highly susceptible to attacks. In the context of cloud computing, the anomalies and insiders attacks will deactivate the service providers, which results in the malfunctioning of the entire system. Traditional defense systems in the network are not efficient in handling insider attacks and intrusion. In this work, the anomaly identification technique is developed to identify the attack incidence, and the proposed approach uses the fuzzy min-max neural network (FMM-NN). The classification accuracy is enhanced by the effective identification of features using a neural network. The performance investigation and outcome of the FMM-NN identifies and classifies the real-time attacks in the cloud environment with high identification accuracy.

Key words: cloud computing, neural network, attacks, detection of intrusion, cloud security

D. Sakthivel and B. Radha. Adaptive Model to Detect Anomaly and Real-Time Attacks in Cloud Environment Using Data Mining Algorithm [J]. Int J Performability Eng, 2021, 17(10): 889-899.

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References

1. Mastelic, T. and Brandic, I.Recent trends in energy-efficient cloud computing.IEEE Cloud Computing, 2(1), pp.40-47, 2015.
2. Moghaddam F.F., Ahmadi M., Sarvari S., Eslami M., andGolkar A.Cloud computing challenges and opportunities: A survey. In2015 1st International Conference on Telematics and Future Generation Networks (TAFGEN), IEEE. pp.34-38, 2015.
3. Zhan Z.H., Liu X.F., Gong Y.J., Zhang J., Chung H.S.H., and Li, Y. Cloud computing resource scheduling and a survey of its evolutionary approaches.ACM Computing Surveys (CSUR), 47(4), pp.1-33, 2015.
4. Rani B.K., Rani B.P., andBabu A.V.Cloud computing and inter-clouds-types, topologies and research issues.Procedia Computer Science, 50, pp.24-29, 2015.
5. Sakthivel, D. and Radha, B.A Study on Security issues and challenges in Cloud IaaS. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 6(3), pp.909-914, November 2018.
6. Ramachandra G., Iftikhar M., andKhan F.A.A comprehensive survey on security in cloud computing.Procedia Computer Science, 110, pp.465-472, 2017.
7. Khan, N. and Al-Yasiri, A. Identifying cloud security threats to strengthen cloud computing adoption framework.Procedia Computer Science, 94, pp.485-490, 2016.
8. Astri L.Y.A study literature of critical success factors of cloud computing in organizations.Procedia Computer Science, 59, pp.188-194, 2015.
9. Virupakshar K.B., Asundi M., Channal K., Shettar P., Patil S., andNarayan D.G.Distributed denial of service (DDoS) attacks detection system for OpenStack-based private cloud.Procedia Computer Science, 167, pp.2297-2307, 2020.
10. Babu, B.M. and Bhanu, M.S.Prevention of insider attacks by integrating behavior analysis with risk based access control model to protect cloud. Procedia Computer Science, 54, pp.157-166. 2015.
11. Yadav R.M.Effective analysis of malware detection in cloud computing.Computers & Security, 83, pp.14-21, 2019.
12. Rajendran P.K., Muthukumar B., andNagarajan G.Hybrid intrusion detection system for private cloud: a systematic approach.Procedia Computer Science, 48, pp.325-329, 2015.
13. Idhammad M., Afdel K., andBelouch M.Distributed intrusion detection system for cloud environments based on data mining techniques.Procedia Computer Science, 127, pp.35-41, 2018.
14. Bamakan S.M.H., Amiri, B., Mirzabagheri, M., and Shi, Y. A new intrusion detection approach using PSO based multiple criteria linear programming.Procedia Computer Science, 55, pp.231-237, 2015.
15. Khan, N. and Al-Yasiri, A. Identifying cloud security threats to strengthen cloud computing adoption framework.Procedia Computer Science, 94, pp.485-490, 2016.
16. Sun H.Improved BP algorithm intrusion detection model based on KVM. In2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), IEEE, pp.442-445, 2016.
17. Arjunan, K. and Modi, C.N.An enhanced intrusion detection framework for securing network layer of cloud computing. In2017 ISEA Asia Security and Privacy (ISEASP), IEEE, pp.1-10, 2017.
18. Zekri M.,El Kafhali, S., Aboutabit, N., and Saadi, Y. DDoS attack detection using machine learning techniques in cloud computing environments. In2017 3rd international conference of cloud computing technologies and applications (CloudTech), IEEE, pp.1-7, 2017.
19. Schueller Q., Basu K., Younas M., Patel M., andBall F.A hierarchical intrusion detection system using support vector machine for SDN network in cloud data center. In 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, pp.1-6. 2018.
20. Idhammad M., Afdel K., andBelouch M.Distributed intrusion detection system for cloud environments based on data mining techniques.Procedia Computer Science, 127, pp.35-41, 2018.
21. Javadpour, A., Abharian, S.K., and Wang, G. Feature selection and intrusion detection in cloud environment based on machine learning algorithms. In 2017 IEEE international symposium on parallel and distributed processing with applications and 2017 IEEE international conference on ubiquitous computing and communications (ISPA/IUCC), IEEE, pp.1417-1421, 2017.
22. Hajimirzaei, B. and Navimipour, N.J.Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm.ICT Express, 5(1), pp.56-59, 2019.
23. Almasoudy F.H.,Al-Yaseen, W.L., and Idrees, A.K. Differential evolution wrapper feature selection for intrusion detection system.Procedia Computer Science, 167, pp.1230-1239, 2020.
24. Sun P., Liu P., Li Q., Liu C., Lu X., Hao R., andChen J.DL-IDS: extracting features using CNN-LSTM hybrid network for intrusion detection system.Security and Communication Networks, 2020.
25. Simpson P.K.Fuzzy Min—MaX Neural NetWorks—Part 1: Classiﬁcation.IEEE Trans. on Neural Networks, 3(5), pp.776-786, 1992.

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