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A Review of Machine Learning-Based Intrusion Detection Systems on the Cloud

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Security, Privacy and Data Analytics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 848))

Abstract

Organizations and individuals adopted cloud computing due to its scalability and flexibility. At the same time, it has become more vulnerable due to the interactions of various cloud stakeholders. One of the most popular security mechanisms for detecting various attacks on the cloud is the Intrusion Detection System (IDS). This paper presents an overview of different cloud intrusion detection techniques. Further, we highlight the use of IDS along with various Machine Learning (ML) techniques.

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References

  1. Smith J (2012) Cloud security incident reporting: framework for reporting about major cloud security incidents

    Google Scholar 

  2. Duncan A, Creese S, Goldsmith M (2015) An overview of insider attacks in cloud computing. Concurr Comput: Pract Exp 27(12):2964–2981

    Article  Google Scholar 

  3. Mishra P, Pilli ES, Varadharajant V, Tupakula U (2016) Black hat presentation demo vids: amazon. In: 2016 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 56–62

    Google Scholar 

  4. Fields T, Graham J (2016) Classifying network attack data using random forest. CATA, Dec

    Google Scholar 

  5. King ST, Chen PM (2006) Subvirt: implementing malware with virtual machines. In: 2006 IEEE symposium on security and privacy S & P’06). IEEE, pp 14–pp

    Google Scholar 

  6. Rutkowska J (2006) Subverting vistatm kernel for fun and profit. Black Hat Briefings

    Google Scholar 

  7. Bahram S, Jiang X, Wang Z, Grace M, Li J, Srinivasan D, Rhee J, Xu D (2010) Dksm: subverting virtual machine introspection for fun and profit. In: 2010 29th IEEE symposium on reliable distributed systems. IEEE, pp 82–91

    Google Scholar 

  8. Kene SG, Theng DP (2015) A review on intrusion detection techniques for cloud computing and security challenges. In: 2015 2nd international conference on electronics and communication systems (ICECS). IEEE, pp 227–232

    Google Scholar 

  9. Ainapure BS, Shah D, Rao AA (2017) Understanding perception of cache-based side-channel attack on cloud environment. In: Advances in intelligent systems and computing. Springer Singapore, pp 9–21, Aug 2017

    Google Scholar 

  10. Munir K, Palaniappan S (2012) Security threats/attacks present in cloud environment. IJCSNS 12(12):107

    Google Scholar 

  11. Modi CN, Patel D (2013) A novel hybrid-network intrusion detection system (h-nids) in cloud computing. In: 2013 IEEE symposium on computational intelligence in cyber security (CICS). IEEE, pp 23–30

    Google Scholar 

  12. Xiao Y, Zhang X, Zhang Y, Teodorescu R (2016) One bit flips, one cloud flops: cross-vm row hammer attacks and privilege escalation. In: 25th USENIX security symposium (USENIX Security 16)

    Google Scholar 

  13. Barbabra (2002) Applications of data mining in computer security

    Google Scholar 

  14. Mehmood Y, Shibli MA, Habiba U, Masood R (2013) Intrusion detection system in cloud computing: challenges and opportunities. In: 2013 2nd national conference on information assurance (NCIA). IEEE, pp 59–66

    Google Scholar 

  15. Garcia-Teodoro P, Diaz-Verdejo J, Maciá-Fernández G, Vázquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur 28(1–2):18–28

    Google Scholar 

  16. Scarfone K, Mell P (2007) Guide to intrusion detection and prevention systems (IDPS). NIST Spec Publ 800(2007):94

    Google Scholar 

  17. Denning DE (1987) An intrusion-detection model. IEEE Trans Softw Sng 2:222–232

    Article  Google Scholar 

  18. Ye N, Emran SM, Li X, Chen Q (2001) Statistical process control for computer intrusion detection. In: Proceedings DARPA information survivability conference and exposition II. DISCEX’01, vol 1. IEEE, pp 3–14

    Google Scholar 

  19. Barbara D, Wu N, Jajodia S (2001) Detecting novel network intrusions using bayes estimators. In: Proceedings of the 2001 SIAM international conference on data mining. SIAM, pp 1–17

    Google Scholar 

  20. Samuel AL (1967) Some studies in machine learning using the game of checkers. II’recent progress. IBM J Res Dev 11(6):601–617

    Article  Google Scholar 

  21. Sommer R, Paxson V (2010) Outside the closed world: On using machine learning for network intrusion detection. In: 2010 IEEE symposium on security and privacy, pp 305–316

    Google Scholar 

  22. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41

    Google Scholar 

  23. Kumar Singh Gautam R, Doegar EA (2018) An ensemble approach for intrusion detection system using machine learning algorithms. In: 2018 8th international conference on cloud computing, data science engineering (Confluence), pp 14–15

    Google Scholar 

  24. Kuang F, Xu W, Zhang S (2014) A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Comput 18:178–184

    Article  Google Scholar 

  25. Salo F, Injadat M, Nassif AB, Shami A, Essex A (2018) Data mining techniques in intrusion detection systems: a systematic literature review. IEEE Access 6:56046–56058

    Article  Google Scholar 

  26. Tao P, Sun Z, Sun Z (2018) An improved intrusion detection algorithm based on GA and SVM. IEEE Access 6:13624–13631

    Article  Google Scholar 

  27. Alavikia Z, Ghasemi A (2018) Overload control in the network domain of lte/lte-a based machine type communications. Wirel Netw 24(1):1–16

    Article  Google Scholar 

  28. Jan SU, Ahmed S, Shakhov V, Koo I (2019) Toward a lightweight intrusion detection system for the internet of things. IEEE Access 7:42450–42471

    Article  Google Scholar 

  29. Alshammari A, Aldribi A (2021) Apply machine learning techniques to detect malicious network traffic in cloud computing. J Big Data 8(1):1–24

    Article  Google Scholar 

  30. Jaber AN, Rehman SU (2020) FCM-SVM based intrusion detection system for cloud computing environment. Clust Comput 23:1–11

    Article  Google Scholar 

  31. Mishra P, Khurana K, Gupta S, Sharma MK (2019) Vmanalyzer: malware semantic analysis using integrated CNN and bi-directional lSTM for detecting VM-level attacks in cloud. In: 2019 twelfth international conference on contemporary computing (IC3). IEEE, pp 1–6

    Google Scholar 

  32. Chiba Z, Abghour N, Moussaid K, El Omri A, Rida M (2019) A clever approach to develop an efficient deep neural network based ids for cloud environments using a self-adaptive genetic algorithm. In: 2019 international conference on advanced communication technologies and networking (CommNet). IEEE, pp 1–9

    Google Scholar 

  33. Hajimirzaei B, Navimipour NJ (2019) Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm. ICT Exp 5(1):56–59

    Article  Google Scholar 

  34. Mishra P, Pilli ES, Varadharajant V, Tupakula U (2016) Nvcloudids: a security architecture to detect intrusions at network and virtualization layer in cloud environment. In: 2016 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 56–62

    Google Scholar 

  35. Patil R, Dudeja H, Modi C (2020) Designing in-VM-assisted lightweight agent-based malware detection framework for securing virtual machines in cloud computing. Int J Inf Secur 19(2):147–162

    Article  Google Scholar 

  36. Ghosh P, Mitra R (2015) Proposed GA-BFSS and logistic regression based intrusion detection system. In: Proceedings of the 2015 third international conference on computer, communication, control and information technology (C3IT), pp 1–6

    Google Scholar 

  37. Mishra P, Pilli ES, Varadharajan V, Tupakula U (2016) Efficient approaches for intrusion detection in cloud environment. In: 2016 international conference on computing, communication and automation (ICCCA), pp 1211–1216

    Google Scholar 

  38. Besharati E, Naderan M, Namjoo E (2019) LR-hids: logistic regression host-based intrusion detection system for cloud environments. J Ambient Intell Hum Comput 10(9):3669–3692

    Article  Google Scholar 

  39. Balamurugan V, Saravanan R (2019) Enhanced intrusion detection and prevention system on cloud environment using hybrid classification and OTS generation. Clust Comput 22(6):13027–13039

    Article  Google Scholar 

  40. Khan MA, Kim J (2020) Toward developing efficient conv-ae-based intrusion detection system using heterogeneous dataset. Electronics 9(11)

    Google Scholar 

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Authors and Affiliations

  1. Sardar Vallabhbhai National Institute of Technology, Surat, India

    Nishtha Srivastava, Ashish Chaudhari, Nidhi Joraviya, Bhavesh N. Gohil & Udai Pratap Rao

  2. University of New Brunswick, Fredericton, Canada

    Suprio Ray

Authors
  1. Nishtha Srivastava
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  2. Ashish Chaudhari
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  3. Nidhi Joraviya
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  4. Bhavesh N. Gohil
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  5. Suprio Ray
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  6. Udai Pratap Rao
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Corresponding author

Correspondence to Nishtha Srivastava .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

    Udai Pratap Rao

  2. Department of Computer Science and Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India

    Sankita J. Patel

  3. Reliance Jio Platforms Ltd., Bangalore, Karnataka, India

    Pethuru Raj

  4. Department of Computer Science, University of Milan, Milan, Milano, Italy

    Andrea Visconti

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Srivastava, N., Chaudhari, A., Joraviya, N., Gohil, B.N., Ray, S., Rao, U.P. (2022). A Review of Machine Learning-Based Intrusion Detection Systems on the Cloud. In: Rao, U.P., Patel, S.J., Raj, P., Visconti, A. (eds) Security, Privacy and Data Analytics. Lecture Notes in Electrical Engineering, vol 848. Springer, Singapore. https://doi.org/10.1007/978-981-16-9089-1_25

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  • DOI: https://doi.org/10.1007/978-981-16-9089-1_25

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9088-4

  • Online ISBN: 978-981-16-9089-1

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