Survey and Performance Analysis of Machine Learning Based Security Threats Detection Approaches in Cloud Computing

Authors

  • Rajesh Keshavrao Sadavarte  Assistant Professor and Head, Netaji Subhashchandra Bose College, Nanded, Maharashtra, India
  • Dr. G. D. Kurundkar  Assistant Professor, Computer Science Department, Shri. GuruBuddhiswami Mahavidyalaya, Purna District Parbhani, Maharashtra, India

DOI:

https://doi.org/10.32628/CSEIT217538

Keywords:

Cloud Computing, Machine Learning, Cloud Security

Abstract

Cloud computing is gaining a lot of attention, however, security is a major obstacle to its widespread adoption. Users of cloud services are always afraid of data loss, security threats and availability problems. Recently, machine learning-based methods of threat detection are gaining popularity in the literature with the advent of machine learning techniques. Therefore, the study and analysis of threat detection and prevention strategies are a necessity for cloud protection. With the help of the detection of threats, we can determine and inform the normal and inappropriate activities of users. Therefore, there is a need to develop an effective threat detection system using machine learning techniques in the cloud computing environment. In this paper, we present the survey and comparative analysis of the effectiveness of machine learning-based methods for detecting the threat in a cloud computing environment. The performance assessment of these methods is performed using tests performed on the UNSW-NB15 dataset. In this work, we analyse machine learning models that include Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Random Forests (RF) and the K-Nearest neighbour (KNN). Additionally, we have used the most important performance indicators, namely, accuracy, precision, recall and F1 score to test the effectiveness of several methods.

References

  1. Paul, R. Jain, M. Samaka, J. Pan, “Application Delivery in Multi-Cloud Environments using Software Defined Networking”, Computer Networks Special Issue on cloud networking and communications, February 2014, pp. 166-186.
  2. Xu, S. Chen, H. Zhang, and T. Wu, ‘‘Incremental k-NN SVM method in intrusion detection,’’ in Proc. 8th IEEE Int. Conf. Softw. Eng. Service Sci. (ICSESS), Nov. 2017, pp. 712–717, doi: 10.1109/ICSESS.2017.8343013.
  3. Moreno-Vozmediano, R. S. Montero, E. Huedo, and I. M. Llorente, ‘‘Efficient resource provisioning for elastic cloud services based on machine learning techniques,’’ J. Cloud Comput., vol. 8, no. 1, p. 5, Dec. 2019, doi: 10.1186/s13677-019-0128-9.
  4. Nour, J. Slay, “UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set),” Military Communications and Information Systems Conference (MilCIS), IEEE, 2015.
  5. Nour, J. Slay, “The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set,” Information Security Journal: A Global Perspective, 2016, pp.1-14.
  6. R. Chandre, P. N. Mahalle, and G. R. Shinde, “Deep Learning and Machine Learning Techniques for Intrusion Detection and Prevention in Wireless Sensor Networks: Comparative Study and Performance Analysis”, Lecture Notes in Networks and Systems 82, https://doi.org/10.1007/978-981-13-9574-1_5
  7. BADER ALOUFFI, MUHAMMAD HASNAIN, HASHEM ALYAMI, MUHAMMAD AYAZ, ABDULLAH ALHARBI, WAEL ALOSAIMI, “Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies”, IEEE Access, 2021.
  8. Rathore S , Park J H, “Semi-supervised learning based distributed attack detection framework for IoT”, Appl. Soft Comput. 2018;72:79–89 .
  9. Mobilio M, Orrù M, Riganelli O, Tundo A, Mariani L., “Anomaly detection as-a-service”, In: 2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW). IEEE; 2019. p. 193–9.
  10. Moustafa N, Creech G, Sitnikova E, Keshk M., “Collaborative anomaly detection framework for handling big data of cloud computing”, In: 2017 military communications and information systems conference (MilCIS). IEEE; 2017. p. 1–6.
  11. Myint, H. O., & Meesad, P., “Incremental Learning Algorithm based on Support Vector Machine with Mahalanobis distance (ISVMM) for Intrusion Prevention”, 978-1-4244-33889/09/$25.00 ©2009 IEEE, (2009).
  12. Farnaaz, N., & Jabbar, M. A., “Random forest modelling for network intrusion detection system”, Procedia Computer Science, 89, 213–217 (Elsevier), (2016).
  13. Al-Qatf, M., Lasheng, Y., Alhabib, M., & Al-Sabahi, K. (2018), “Deep learning approach combining sparse auto encoder with SVM for network intrusion detection”, IEEE Access. https:// doi.org/10.1109/ACCESS.2018.2869577.
  14. Peddabachigari, S., Abraham, A., & Thomas, J. (2016), “Intrusion detection systems using decision trees and support vector machines”, International Journal of Advanced Networking and Applications, 07(04), 2828–2834. ISSN: 0975-0290.
  15. Panda, M., & Patra, M. R., “Network intrusion detection using Naïve Bayes”, IJCSNS International Journal of Computer Science and Network Security, 7(12), (2007, December).
  16. Peel D, McLachlan G J, “Robust mixture modelling using the t distribution”, Stat Comput. 2000;10(4):339–48.
  17. Van, N. T., Thinh, T. N., & Sach, L. T., “An anomaly-based network intrusion detection system using deep learning”, In 2017 International Conference on System Science and Engineering (ICSSE).
  18. Yang, Y., Zheng, K., Wu, C., Niu, X., Yang, Y., “Building an Effective Intrusion Detection System Using the Modified Density Peak Clustering Algorithm and Deep Belief Networks”, Appl. Sci. 9, 238 (2019).
  19. Osanaiye O, Cai H, Choo KKR, Dehghantanha A, Xu Z. Dlodlo M, “Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing”, EURASIP J Wirel Commun Netw. 2016;1:130–130 (2016), https://doi.org/10.1186/s13638-016-0623-3.
  20. Kwon D, Kim H, Kim J, Suh SC, Kim I, Kim K J, “A survey of deep learning-based network anomaly detection”, Cluster Comput. 2019;22(1):949–61.
  21. Aldribi A, Traoré I, Moa B, Nwamuo O., “Hypervisor-based cloud intrusion detection through online multivariate statistical change tracking”, Comput Secur. 2020;88:101646–101646. https://doi.org/10.1016/j.cose.2019.101646.
  22. Nisioti A, Mylonas A, Yoo PD, Katos V. ,“From intrusion detection to attacker attribution: a comprehensive survey of unsupervised methods”, IEEE Commun Surv Tutor. 2018;20(4):3369–88. https://doi.org/10.1109/comst.2018.2854724.
  23. Garg S, Kaur K, Kumar N, Kaddoum G, Zomaya AY, Ranjan R., “A hybrid deep learning-based model for anomaly detection in cloud datacenter networks”, IEEE Trans Netw Serv Manage. 2019;16(3):924–35. https://doi.org/10.1109/tnsm.2019.2927886.
  24. Fernández G C, Xu S, “A case study on using deep learning for network intrusion detection”, In: MILCOM 2019–2019, IEEE Military Communications Conference (MILCOM). IEEE; 2019. p. 1-6.
  25. Nicholas Lee, Shih Yin Ooi and Ying Han Pang, “ A Sequential Approach to Network Intrusion Detection”, Lecture Notes in Electrical Engineering 603, https://doi.org/10.1007/978-981-15-0058-9_2
  26. Kishor Kumar Gulla, P. Viswanath, Suresh Babu Veluru, and R. Raja Kumar, “ Machine Learning Based Intrusion Detection Techniques”, Handbook of Computer Networks and Cyber Security, https://doi.org/10.1007/978-3-030-22277-2_35
  27. Zhang J, “Anomaly detecting and ranking of the cloud computing platform by multi-view learning”, Multimedia Tools Appl. 2019;78:30923–42.
  28. Barbhuiya S, Papazachos Z, Kilpatrick P, Nikolopoulos DS, “RADS: Real-time anomaly detection system for cloud data centres”, 2018, arXiv preprint arXiv:1811.04481.
  29. Peng K, Leung VCM, Zheng L, Wang S, Huang C, Lin T, “Intrusion detection system based on decision tree over big data in fog environment”, Wireless Commun Mob Comput. 2018;2018:1–10. https://doi.org/10.1155/2018/4680867.
  30. Sapna S. Kaushik, Dr. Prof. P. R. Deshmukh, “Detection of Attacks in an Intrusion Detection System”, International Journal of Computer Science and Information Technologies, Vol. 2 (3), 2011, 982-986.
  31. Manna A, Alkasassbeh M., “Detecting network anomalies using machine learning and SNMP-MIB dataset with IP group”, In: 2019 2nd International Conference on new Trends in Computing Sciences (ICTCS). IEEE; 2019. p. 1–5.
  32. Gopal Singh Kushwah, Virender Ranga, “Optimized extreme learning machine for detecting DDoS attacks in cloud computing”, In computers & security 105 (2021) 102260. https://doi.org/10.1016/j.cose.2021.102260
  33. Kashif Naseer Qureshi, Gwanggil Jeon, Francesco Piccialli, “Anomaly detection and trust authority in artificial intelligence and cloud computing”, In Computer Networks 184 (2021) 107647. https://doi.org/10.1016/j.comnet.2020.107647
  34. Fargana J. Abdullayeva, “Advanced Persistent Threat attack detection method in cloud computing based on autoencoder and softmax regression algorithm”, In Array 10 (2021) 100067. https://doi.org/10.1016/j.array.2021.100067
  35. Krishnaveni, S. Prabakaran, “Ensemble approach for network threat detection and classification on cloud computing”, Concurrency Computat Pract Exper. 2019;e5272, https://doi.org/10.1002/cpe.5272
  36. ALI BOU NASSIF, MANAR ABU TALIB, QASSIM NASIR, HALAH ALBADANI, FATIMA MOHAMAD DAKALBAB, “Machine Learning for Cloud Security: A Systematic Review”, In IEEE Access, 2021.
  37. Mohamed Amine Ferrag, Leandros Maglaras, Sotiris Moschoyiannis, Helge Janicke, “Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study”, Journal of Information Security and Applications, 50 (2020) 102419.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2021-10-30

Issue

Volume 7, Issue 6, November-December-2021

Section

Research Articles

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How to Cite

[1]
Rajesh Keshavrao Sadavarte, Dr. G. D. Kurundkar, " Survey and Performance Analysis of Machine Learning Based Security Threats Detection Approaches in Cloud Computing" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 7, Issue 6, pp.49-58, November-December-2021. Available at doi : https://doi.org/10.32628/CSEIT217538