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A focus on future cloud: machine learning-based cloud security

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Abstract

Recent days have seen an apparent shift in most of the organizations moving towards using cloud environment and various cloud-based services. In order to protect and safeguard the transactions made by organizations over cloud environment, it is highly essential to provide a secure and robust environmental solution across cloud space. Existing approaches such as linear regression and support vector machine have been tried to promote cyber-security in the market by performing static verification of cloud user behaviour in order to identify pre-defined threats. Due to their static nature, these security solutions are restricted in their functionality. When it comes to access control, the decision making involves performing a permit or block operation. Also, the earlier methods face difficulties in terms of data protection over the endpoints which are not managed by the cloud. In order to solve the above-said problems, this paper is focused on designing a novel security solution for cloud applications using machine learning (ML) approaches. The main objective of this paper is to shape the future generation of cloud security using one of the ML algorithms such as convolution neural network because CNN can provide automatic and responsive approaches to enhance security in cloud environment. Instead of focusing only on detecting and identifying sensitive data patterns, ML can provide solutions which incorporate holistic algorithms for secure enterprise data throughout all the cloud applications. The proposed ML algorithm is experimented, results are verified and performance is evaluated by comparing with the existing approaches.

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References

  1. https://www.analyticsvidhya.com/blog/2018/07/using-power-deep-learning-cyber-security/

  2. Tsai C, Hsu Y, Lin C, Lin W (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36(10):11994–12000

    Article  Google Scholar 

  3. Fernandes D, Soares L, Gomes J, Freire M, Inácio P (2014) Security issues in cloud environments: a survey. Int J Inf Secur 13(2):113–170

    Article  Google Scholar 

  4. Kandukuri B, Paturi V, Rakshit A (2009) Cloud security issues. In: IEEE international conference on services computing, pp 517–520

  5. Almulla S, Yeun C (2010) Cloud computing security management. In: 2nd international conference on ICESMA. IEEE, pp 1–7

  6. Palivela H, Chawande N, Wani A (2011) Development of server in cloud computing to solve issues related to security and backup. In: IEEE CCIS, pp 158–163

  7. Roshke S, Cheng F, Meinel C (2009) Intrusion detection in the cloud. In: Eighth IEEE international conference on dependable, autonomic and secure computing, pp 729–734

  8. Laura A, Moro R (2008) Support vector machines (SVM) as a technique for solvency analysis. DIW Berlin discussion paper

  9. Zhang X, Zhao Y (2013) Application of support vector machine to reliability analysis of engine systems. Telkomnika 11(7):3352–3560

    Google Scholar 

  10. Haykin S (2009) Neural networks: a comprehensive foundation, 2nd edn. Prentice Hall, Englewood Cliffs, NJ

    MATH  Google Scholar 

  11. Michalski R, Carbonell J, Mitchell T (2013) Machine learning: an artificial intelligence approach. Springer, Berlin

    MATH  Google Scholar 

  12. Breiman L, Friedman J, Olshen R, Stone P (1984) Classification and regressing trees. Wadsworth International Group, Belmont, CA

    MATH  Google Scholar 

  13. Stein G, Chen B, Wu A, Hua K (2005) Decision tree classifier for network intrusion detection with GA-based feature selection. In: Paper presented at the proceedings of the 43rd annual southeast regional conference, Kennesaw, GA

  14. Bhamare D, Salman T, Samaka M, Erbad A, Jain R (2016) Feasibility of supervised machine learning for cloud security. In: 2016 international conference on information science and security (ICISS), Pattaya, pp 1–5

  15. Ayodele T, Adeegbe D (2013) Cloud based emails boundaries and vulnerabilities. In: 2013 science and information conference, London, pp 912–914

  16. Karn RR, Kudva P, Elfadel IA (2019) Dynamic autoselection and autotuning of machine learning models for cloud network analytics. IEEE Trans Parallel Distrib Syst 30(5):1052–1064

    Article  Google Scholar 

  17. https://www.ixiacom.com/products/professional-services/test-as-a-service-taas

  18. Hong T, Choi C, Shin J (2017) CNN-based malicious user detection in social networks. Concurr Comput. https://doi.org/10.1002/cpe.4163

    Article  Google Scholar 

  19. Kuo W-C, Lin Y-P, Chang C-Y, Lin C-C, Lin H-H (2019) Malware detection method based on CNN. In: New trends in computer technologies and applications

  20. Habibi Lashkari A, Draper Gil G, Mamun M, Ghorbani A (2017) Characterization of Tor traffic using time based features, pp 253–262. https://doi.org/10.5220/0006105602530262

  21. Javaid A, Niyaz Q, Sun W, Alam M (2015) A deep learning approach for network intrusion detection system. EAI Endorsed Trans Secur Saf. https://doi.org/10.4108/eai.3-12-2015.2262516

    Article  Google Scholar 

  22. Nour M, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Military communications and information systems conference (MilCIS). IEEE

  23. Nour M, Slay J (2016) The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf Secur J Glob Perspect 25:1–14

    Article  Google Scholar 

  24. Sherif S, Traore I, Ghorbani A, Sayed B, Zhao D, Lu W, Felix J, Hakimian P (2011) Detecting P2P botnets through network behavior analysis and machine learning. In: Proceedings of 9th annual conference on privacy, security and trust (PST2011)

  25. Shiravi A, Shiravi H, Tavallaee M, Ghorbani A (2012) Toward developing a systematic approach to generate benchmark datasets for intrusion detection. Comput Secur 31(3):357–374

    Article  Google Scholar 

  26. Mchugh J (2000) Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln laboratory. ACM Trans Inf Syst Secur 3:262–294

    Article  Google Scholar 

  27. French Chapter of Honenynet. http://www.honeynet.org/chapters/france

  28. Szabó G, Orincsay D, Malomsoky S, Szabó I (2008) On the validation of traffic classification algorithms. In: Proceedings of the 9th international conference on passive and active network measurement, PAM. Springer, Berlin, pp 72–81

  29. LBNL Enterprise Trace Repository (2005) http://www.icir.org/enterprise-tracing

  30. Peddabachigari S, Abraham A, Thomas J (2004) Intrusion detection systems using decision trees and support vector machines. Int J Appl Sci Comput 11:118–134

    Google Scholar 

  31. Jin B, Wang Y, Liu Z, Xue J (2011) A trust model based on cloud model and Bayesian networks. Procedia Environ Sci 11(Part A):452–459

    Article  Google Scholar 

  32. Jordan A (2002) On discriminative vs. generative classifiers: a comparison of logistic regression and naïve Bayes. In: Advances in neural information processing systems, vol 14, p 841

  33. Modi C et al (2013) A survey of intrusion detection techniques in cloud. J Netw Comput Appl 36(1):42–57

    Article  Google Scholar 

  34. Waikato environment for knowledge analysis (WEKA) version 3.5.7. http://www.cs.waikato.ac.nz/ml/weka/. June 2008

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Authors and Affiliations

  1. B S Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

    E. K. Subramanian & Latha Tamilselvan

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  1. E. K. Subramanian
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  2. Latha Tamilselvan
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Correspondence to E. K. Subramanian.

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Subramanian, E.K., Tamilselvan, L. A focus on future cloud: machine learning-based cloud security. SOCA 13, 237–249 (2019). https://doi.org/10.1007/s11761-019-00270-0

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