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Security and Privacy at Cloud System

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Cloud Computing for Optimization: Foundations, Applications, and Challenges

Part of the book series: Studies in Big Data ((SBD,volume 39))

Abstract

This chapter is focused to provide security mechanism for complete cloud system by implementing encryption and intrusion detection system. Hybrid encryption is applied on data at cloud client level so that data in medium will be safe as well as data will be stored in cloud server in safe mode. Data in server will be accessible only to the authorized users which have the decryption key. Computation for decryption becomes challenging and difficult in case of hybrid encryption. The second phase of security will be applied in cloud server by implementing intrusion detection system which will detect the anomaly traffic towards server and block the unauthorized and unauthenticated traffic. Dimension reduction techniques are also focused in this chapter to make the efficient intrusion detection system.

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References

  1. J. Xiong, X. Liu, Z. Yao, J. Ma, Q. Li, K. Geng, S.P. Chen, A secure data self-destructing scheme in cloud computing. IEEE Trans. Cloud Comput. 2(4), 448–458 (2014)

    Article  Google Scholar 

  2. N. Sengupta, Designing encryption and IDS for cloud security. in The second International Conference on Internet of Things, Data and Cloud Computing (ICC 2017), Cambridge city, Churchill College, University of Cambridge, United Kingdom, 22–23 March 2017

    Google Scholar 

  3. M. Ficco, M. Rak, Stealthy Denial of service strategy in cloud computing. IEEE Trans. Cloud Comput. 3(1), 80–94 (2015)

    Article  Google Scholar 

  4. D. Talia, P. Trunfio, F. Marozzo, Data Analysis in the Cloud: Models, Techniques and Applications (Elsevier Science Publishers B.V. Amsterdam, The Netherlands, 2015). ISBN:0128028815 9780128028810

    Google Scholar 

  5. C.A. Chen, M. Won, R. Stoleru, G.G. Xie, Energy-efficient fault-tolerant data storage and processing in mobile cloud. IEEE Trans. Cloud Comput. 3(1), 28–41 (2015)

    Article  Google Scholar 

  6. T. Erl, R. Puttini, Z. Mahmood, Cloud Computing:Concepts, Technology and Architecture (Prentice Hall, Englewood Cliffs)

    Google Scholar 

  7. D. Zissis, D. Lekkas, Addressing cloud computing security issues. Futur. Gener. Comput. Syst. ELSEVIER Int. J. Grid Comput. eScience 28, 583–592 (2012)

    Article  Google Scholar 

  8. S. Thakur, G.J. Breslin, A robust reputation management mechanism in federated cloud. IEEE Trans. Cloud Comput. PP(99), 1 (2017)

    Article  Google Scholar 

  9. Z. Zhang, M. Dong, L. Zhu, Z. Guan, R. Chen, R. Xu, K. Ota, Achieving privacy-friendly storage and secure statistics for smart meter data on outsourced clouds. IEEE Trans. Cloud Comput. PP(99), 1 (2017)

    Google Scholar 

  10. J. Baek, H.Q. Vu, K.J. Liu, X. Huang, Y. Xiang, A secure cloud computing based framework for big data information management of smart grid. IEEE Trans. Cloud Comput. 3(2), 233–244 (2015)

    Article  Google Scholar 

  11. S. Jiang, X. Zhu, L. Guo, J. liu, Publicly verifiable boolean query over outsourced encrypted data. IEEE Trans. Cloud Comput. PP(99), 1 (2017)

    Google Scholar 

  12. M.K. SIM, Agent-based approaches for intelligent intercloud resource allocation. IEEE Trans. Cloud Comput. PP(99), 1 (2016)

    Article  Google Scholar 

  13. K. Hashizume, G.D. Rosado, F.E. Medina, B.E. Fernandez, An analysis of security issues for cloud computing. J. Internet Serv. Appl. 4, 5 (2013). https://doi.org/10.1186/1869-0238-4-5

    Article  Google Scholar 

  14. P.R. Padhy, R.M. Patra, C.S. Satapathy, Cloud computing: security issues and research challenges. IRACST Int. J. Comput. Sci. Inf. Technol. Secur. 1(2) (2011)

    Google Scholar 

  15. P. Schoo, V. Fusenig, V. Souza, M. Melo, P. Murray, H. Debar, H. Medhioub, D. Zeghlache, Challenges for cloud networking security. HP Laboratories

    Google Scholar 

  16. H. Liu, B. He, X. Liao, H. Jin, Towards declarative and data-centric virtual machine image management in IaaS clouds. IEEE Trans. Cloud Comput. PP(99), 1 (2017)

    Google Scholar 

  17. P. Jamshidi, A. Ahmad, C. Pahl, Cloud migration research: a systematic review. IEEE Trans. Cloud Comput. 1(2), 142–157 (2013)

    Article  Google Scholar 

  18. Y. Zhang, X. Liao, H. Jin, G. Min, Resisting skew-accumulation for time-stepped applications in the cloud via exploiting parallelism. IEEE Trans. Cloud Comput. 3(1), 54–65 (2015)

    Article  Google Scholar 

  19. K. Hashizume, G.D. Rosado, F.E. Medina, B.E. Fernandez, An analysis of security issues for cloud computing. J. Internet Serv. Appl. 4(5) (2013). https://doi.org/10.1186/1869-0238-4-5

    Article  Google Scholar 

  20. N. vurukonda, T.B. Rao, A study on data storage security issues in cloud computing. in 2nd International Conference on Intelligent Computing, Communication & Convergence (ICCC-2016), vol. 92 (Elsevier Procedia Computer Science, 2016), pp. 128–135

    Article  Google Scholar 

  21. N.P. Smart, Algorithms, key size and parameters report, European Union Agency for Network and Information Security, 2014

    Google Scholar 

  22. T. Olufon, E.-A.C. Campbell, S. Hole, K. Radhakrishnan, A. Sedigh, Mitigating external threats in wireless local area networks. Int. J. Commun. Netw. Inf. Secur. (IJCNIS) 6(3), 200 (2014)

    Google Scholar 

  23. F. Ayoub, K. Singh, Cryptographic techniques and network security. IEE Proc. F Commun. Radar Signal Process. 131(7) (2008)

    Article  Google Scholar 

  24. U. Somani, K. Lakhani, M. Mundra, Implementing digital signature with rsa encryption algorithm to enhance the data security of cloud in cloud computing. in 1st International Conference on Parallel, Distributed and Grid Computing (PDGC - 2010) (2010)

    Google Scholar 

  25. T. Cusick, P. Stanica, Cryptographic Boolean Functions and Applications (Elsevier). eBook ISBN: 9780128111307

    Google Scholar 

  26. M. Abdalla, X. Boyen, C. Chevalier, D. Pointcheval, Distributed public-key cryptography from weak secrets, in Public Key Cryptography PKC 2009, ed. by S. Jarecki, G. Tsudik. PKC 2009. Lecture Notes in Computer Science, vol. 5443 (Springer, Berlin, 2009)

    Chapter  Google Scholar 

  27. U. Somani, K. Lakhani, M. Mundra, Implementing digital signature with rsa encryption algorithm to enhance the data security of cloud in cloud computing. in 1st International Conference on Parallel, Distributed and Grid Computing (PDGC - 2010) (2010)

    Google Scholar 

  28. N. Sengupta, Intrusion detection system for cloud computing. in Middle East and North Africa Conference for Public Administration Research, Bahrain, 23–24 April 2014

    Google Scholar 

  29. D.E. Denning, An intrusion-detection model. IEEE Trans. Softw. Eng. SE–13(2), 222–232 (1987)

    Article  Google Scholar 

  30. Aydin, EhsanAmiri E., Keshavarz, H., Mohamadi, E., Moradzadeh, H. : Intrusion detection systems in manet: a review. in Elsevier, International Conference on Innovation, Management and Technology Research, Malaysia, 22–23 Sept 2013

    Google Scholar 

  31. S. Roschke, C. Feng, C. Meinel, Intrusion detection in the cloud, dependable, autonomic and secure computing, in DASC09, Eighth IEEE International Conference (2009), pp. 729–734. E-ISBN: 978-1-4244-5421-1

    Google Scholar 

  32. Y. Mehmood, U. Habiba, M.A. Shibli, R. Masood, Intrusion detection system in cloud computing: challenges and opportunities. in 2nd National Conference on Information Assurance (NCIA) (2013)

    Google Scholar 

  33. A. Patel, M. Taghavia, K. Bakhtiyaria, J.C. Jnior, An intrusion detection and prevention system in cloud computing: a systematic review. J. Netw. Comput. Appl. 36(1), 2541 p (2013)

    Article  Google Scholar 

  34. X. Ren, Intrusion detection method using protocol classification and rough set based support vector machine. Comput. Inf. Sci. 2(4), 100–108 (2009)

    Google Scholar 

  35. Z. Yu, J.J.P. Tsai, Intrusion Detection, A Machine Learning Approach, vol. 3 (Imperial College Press, London, 2011). ISBN-13: 978-1848164475

    Google Scholar 

  36. R.P. Patil, Y. Sharma, M. Kshirasagar, Performance analysis of intrusion detection systems implemented using hybrid machine learning techniques. Int. J. Comput. Appl. (0975–8887) 133(8) (2016)

    Google Scholar 

  37. A.M. Aydin, H.A. Zaim, G.K. Ceylan, A hybrid intrusion detection system design for computer network security. Comput. Electr. Eng. 35, 517–526 (2009). Elsevier

    Article  Google Scholar 

  38. D. Anderson, T. Lunt, H. Javitz, A. Tamaru, A. Valdes. Safeguard final report: Detecting unusual program behavior using the NIDES statistical component, Technical report, Computer Science Laboratory, SRI International, Menlo Park, CA, 1993

    Google Scholar 

  39. Lianying, L. Fengyu, A Swarm-intelligence-based intrusion detection technique. IJCSNS Int. J. Comput. Sci. Netw. Secur. 6(7B) (2006)

    Google Scholar 

  40. S. Owais, V. Snasel, P. Kromer, A. Abraham, Survey: Using genetic algorithm approach in intrusion detection systems techniques. in CISIM 2008 (IEEE, 2008), pp. 300–307

    Google Scholar 

  41. P. LaRoche, N.A. ZincirHeywood, 802.11 network intrusion detection using genetic programming. in Proceeding GECCO ’05 Proceedings of the 2005 workshops on Genetic and evolutionary computation (2005), pp. 170–171

    Google Scholar 

  42. Y. LIN, Y. ZHANG, OU Yang-Jia, : The design and implementation of host-based intrusion detection system. Third International Symposium on Intelligent Information Technology and Security Informatics, 2010

    Google Scholar 

  43. R.W. Swiniarski, Rough sets methods in feature reduction and classification. Int. J. Appl. Math. Comput. Sci. 11(3), 565–582 (2001)

    MathSciNet  MATH  Google Scholar 

  44. Y. Zhao, F. Luo, S.K.M. Wong, Y.Y. Yao, A general definition of an attribute reduct. in Proceedings of Rough Sets and Knowledge Technology, Second International Conference, RSKT 2007, LNAI 4481 (2007), pp. 101–108

    Google Scholar 

  45. L. Cuijuan, L. Yuanyuan, Q. Qin Yankai, Research on anomaly intrusion detection based on rough set attribute reduction. in The 2nd International Conference on Computer Application and System Modeling (Atlantis Press, Paris, France, 2012)

    Google Scholar 

  46. M. Sammany, T. Medhat, Dimensionality reduction using rough set approach for two neural networks-based applications. in Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (Springer, Berlin, 2007), pp. 639–647

    Google Scholar 

  47. R. Jensen, Q. Shen, Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches (Wiley-IEEE Press, Oxford, 2008)

    Book  Google Scholar 

  48. J.R. Anaraki, M. Eftekhari, Improving Fuzzy-rough quick reduct for feature selection. in IEEE 19th Iranian Conference on Electrical Engineering (ICEE) (2011), pp. 1–6

    Google Scholar 

  49. P.E. Ephzibah, B. Sarojini, E.J. Sheela, A study on the analysis of genetic algorithms with various classification techniques for feature selection. Int. J. Comput. Appl. 8(8), 33 (2010)

    Google Scholar 

  50. B. Zadrozny, Learning and evaluating classifiers under sample selection bias. in International Conference on Machine Learning ICML’04 (2004)

    Google Scholar 

  51. Y.X. Wang, G. Whitwell, M.J. Garibaldi, The application of a simulated annealing fuzzy clustering algorithm for cancer diagnosis. in Proceedings of IEEE 4th International Conference on Intelligent Systems Design and Application, Budapest, Hungary, 26–28 August 2004, pp. 467–472

    Google Scholar 

  52. R.C. Chen, K.F. Cheng, C.F. Hsieh, Using rough set and support vector machine for network intrusion detection. Int. J. Netw. Secur. Appl. (IJNSA) 1(1) (2009)

    Google Scholar 

  53. Z. Pawlak, Rough Sets Theoretical Aspects of Reasoning about Data, vol. 229 (Kluwer Academic Publishers, Boston, 1991)

    MATH  Google Scholar 

  54. Z. Pawlak, Rough set theory and its applications to data analysis. Cybern. Syst. 29, 661–688 (1998)

    Article  Google Scholar 

  55. N. Sengupta, J. Sen, J. Sil, M. Saha, Designing of on line intrusion detection system using rough set theory and Q learning algorithm. Neurocomputing (Elsevier Journal) 111, 161–168 (2013)

    Article  Google Scholar 

  56. Y. Yao, Discernibility matrix simplification for constructing attribute reducts. Inf. Sci. 179(5), 867–882 (2009)

    Article  MathSciNet  Google Scholar 

  57. Y. Zhao, Y. Yao, F. Luo, Data analysis based on discernibility and indiscernibility. Inf. Sci. 177, 4959–4976 (2007). Elsevier Inc

    Article  Google Scholar 

  58. A. Skowron, C. Rauszer, The discernibility matrices and functions in information systems, in Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, ed. by R. Slowinski (Springer, Dordrecht, 1991), pp. 331–362

    Google Scholar 

  59. H. Li, Q. Zhang, J. Deng, B.Z. Xu, A preference-based multiobjective evolutionary approach for sparse optimization. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–16 (2017)

    Google Scholar 

  60. J. Faigi, A.G. Hollinger, Autonomous data collection using a self-organizing map. IEEE Trans. Neural Netw. Learn. Syst. PP(99), 1–13 (2017)

    Google Scholar 

  61. A.N. Alias, M.H.N. Radzi, Fingerprint classification using support vector machine. in 2016 Fifth ICT International Student Project Conference (ICT-ISPC), 27–28 May 2016

    Google Scholar 

  62. S.K.M. Varma, K.K.N. Rao, K.K. Raju, S.P.G. Varma, Pixel-based classification using support vector machine classifier. in 2016 IEEE 6th International Conference on Advanced Computing (IACC), 27–28 Feb 2016

    Google Scholar 

  63. H.A. Sung, S. Mukkamala, Identifying important features for intrusion detection using support vector machines and neural networks. in Proceedings of International Symposium on Applications and the Internet (SAINT 2003) (2003), pp. 209–217

    Google Scholar 

  64. S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, K.R.K. Murthy, A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Trans. Neural Netw. 11, 124–136 (2000)

    Article  Google Scholar 

  65. C. Cortes, V.N. Vapnik, Support vector network. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  66. Nsl-kdd data set for network-based intrusion detection systems (2009), http://nsl.cs.unb.ca/NSL-KDD/

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Author information

Authors and Affiliations

  1. Department of IT, University College of Bahrain, P O Box 55040, Manama, Bahrain

    Nandita Sengupta

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  1. Nandita Sengupta
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Correspondence to Nandita Sengupta .

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Editors and Affiliations

  1. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Bhabani Shankar Prasad Mishra

  2. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Himansu Das

  3. Department of Information and Communication Technology , Fakir Mohan University, Balasore, Odisha, India

    Satchidananda Dehuri

  4. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Alok Kumar Jagadev

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Sengupta, N. (2018). Security and Privacy at Cloud System. In: Mishra, B., Das, H., Dehuri, S., Jagadev, A. (eds) Cloud Computing for Optimization: Foundations, Applications, and Challenges. Studies in Big Data, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-319-73676-1_9

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  • DOI: https://doi.org/10.1007/978-3-319-73676-1_9

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  • Online ISBN: 978-3-319-73676-1

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