Skip to main content
SpringerLink
Log in
Book cover

International Conference on Advanced Informatics for Computing Research

ICAICR 2018: Advanced Informatics for Computing Research pp 358–378Cite as

A Comparative Analysis of Different Intrusion Detection Techniques in Cloud Computing

  • Conference paper
  • First Online:

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 956))

Abstract

Nowadays, the foremost optimal choice of every IT organization is cloud computing. Cloud computing technology is very flexible and scalable in nature. The prime concern in cloud computing is its security and privacy, because intruders are trying to breach it. The main reason for breaching is its open and distributed architecture. For detection of various attacks on cloud, the most common mechanism used is Intrusion Detection System (IDS). We have presented a comparative analysis of some existing cloud based intrusion detection systems and different methods of deploying the IDS are used for overcoming the security challenges. In spite of the fact that there are various existing literatures in this area of study, we endeavor to give more intricate picture of a thorough analysis. This paper shares an overview of different intrusions in cloud. The metrics, which are used for comparative analysis, are of various types like positioning, detection time, detection techniques, data source and attacks. The comparative analysis also shows the limitations of each technique that tells whether the cloud-computing environment is secure or not.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Modi, C., Patel, D., Borisaniya, B., Patel, H.: A survey of intrusion detection techniques in cloud. J. Netw. Comput. Appl. 36, 42–57 (2013)

    Article  Google Scholar 

  2. Almorsy, M., Grundy, J., Ibrahim, A.S.: Adaptable, model-driven security engineering for SaaS cloud-based applications. Automated Software Engineering 21(2), 187–224 (2014)

    Article  Google Scholar 

  3. Du, Y., Zhang, R., Li, M.: Research on a security mechanism for cloud computing based on virtualization. Telecommun. Syst. 53, 19–24 (2013)

    Article  Google Scholar 

  4. Edurado, F.B., Monge, R., Hashizume, K.: Building a security reference architecture for cloud systems. Requir. Eng. 21, 1–25 (2015)

    Google Scholar 

  5. He, J., Dong, M., Ota, K., Fan, M., Wang, G.: NetSecCC: a scalable and fault tolerant architecture for cloud computing security. Peer-to-Peer Netw. Appl. 9, 1–15 (2014)

    Google Scholar 

  6. Hu, P., Sung, C.W., Ho, S., Chan, T.H.: Optimal coding and allocation for perfect secrecy in multiple clouds. Inf. Forensics Secur. 11, 388–399 (2014)

    Article  Google Scholar 

  7. Lee, J., Cho, J., Seo, J., Shon, T., Won, D.: A novel approach to analyzing for detecting malicious network activity using a cloud computing testbed. Mob. Netw. Appl. 18, 122–128 (2012)

    Article  Google Scholar 

  8. Li, J., Li, Y.K., Chen, X., Lee, P.P.C., Lou, W.: A hybrid cloud approach for secure authorized deduplication. IEEE Trans. Parallel Distrib. Syst. 26, 1206–1216 (2014)

    Article  Google Scholar 

  9. Rahat, M., Shibli, M.A., Niazi, M.A.: Cloud identity management security issues and solutions: a taxonomy. Complex Adapt. Syst. Model. 2, 1–37 (2014)

    Article  Google Scholar 

  10. Rho, S., Chang, H., Kim, S., Lee, Y.S.: An efficient peer-to-peer distributed scheduling for cloud and grid computing. Peer-to-Peer Netw. Appl. 8, 863–871 (2014)

    Article  Google Scholar 

  11. Li, Q., Han, Q., Sun, L.: Collaborative recognition of queuing behavior on mobile phones. Mob. Comput. 15, 60–73 (2014)

    Google Scholar 

  12. Tak, G.K., Badge, N., Manwatkar, P., Rangnathan, A., Tapaswi, S.: Asynchronous anti phishing image captcha approach towards phishing. In: International Conference on Future Computer and Communication, vol. 3, pp. 694–698. IEEE (2010)

    Google Scholar 

  13. Malhotra, K., Gardner, S., Patz, R.: Implementation of elliptic-curve cryptography on mobile healthcare devices. IEEE (2007)

    Google Scholar 

  14. Jia, X., et al.: Efficient revocable id-based signature with cloud revocation server. IEEE Access 5, 2945–2954 (2017)

    Article  Google Scholar 

  15. Arjunan, K., Modi, C.N.: An enhanced intrusion detection framework for securing network layer of cloud computing. In: Asia Security and Privacy (ISEASP), 2017 ISEA. IEEE (2017)

    Google Scholar 

  16. Hamdi, O., Mbaye, M., Krief, F.: A cloud-based architecture for network attack signature learning. In: 2015 7th International Conference on New Technologies, Mobility and Security (NTMS). IEEE (2015)

    Google Scholar 

  17. Huang, S.-Y., Huang, Y., Suri, N.: Event pattern discovery on IDS traces of cloud services. In: 2014 IEEE Fourth International Conference on Big Data and Cloud Computing (BdCloud). IEEE (2014)

    Google Scholar 

  18. Mehmood, Y., et al.: Intrusion detection system in cloud computing: challenges and opportunities. In: 2013 2nd National Conference on Information Assurance (NCIA). IEEE (2013)

    Google Scholar 

  19. Kumawat, S., Sharma, A.K., Kumawat, A.: Intrusion detection and prevention system using K-learning classification in cloud. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE (2016)

    Google Scholar 

  20. Alarifi, S.S., Wolthusen, S.D.: Detecting anomalies in IaaS environments through virtual machine host system call analysis. In: 2012 International Conference for Internet Technology and Secured Transactions. IEEE (2012)

    Google Scholar 

  21. Fellin, C., Haney, M.: Preventing the mistraining of anomaly-based IDSs through ensemble systems. In: 2014 IEEE World Congress on Services (SERVICES). IEEE (2014)

    Google Scholar 

  22. Pan, Z., Pacheco, J., Hariri, S.: Anomaly behavior analysis for building automation systems. In: 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA). IEEE (2016)

    Google Scholar 

  23. Gupta, D., Gupta, S.: An efficient approach of trigger mechanism through IDS in cloud computing. In: 2017 4th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON). IEEE (2017)

    Google Scholar 

  24. Li, Z., Sun, W., Wang, L.: A neural network based distributed intrusion detection system on cloud platform. In: 2012 IEEE 2nd International Conference on Cloud Computing and Intelligent Systems (CCIS), vol. 1. IEEE (2012)

    Google Scholar 

  25. Alqahtani, S.M., John, R.: A comparative analysis of different classification techniques for cloud intrusion detection systems’ alerts and fuzzy classifiers. In: 2017 Computing Conference. IEEE (2017)

    Google Scholar 

  26. Alqahtani, S.M., John, R.: A comparative study of different fuzzy classifiers for cloud intrusion detection systems’ alerts. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (2016)

    Google Scholar 

  27. Sule, M.-J., et al.: Fuzzy logic approach to modelling trust in cloud computing. IET Cyber-Phys. Syst. Theory Appl. 2(2), 84–89 (2017)

    Article  Google Scholar 

  28. Kumar, R., Lal, S.P., Sharma, A.: Detecting denial of service attacks in the cloud. In: 2016 IEEE 14th International Conference on Dependable, Autonomic and Secure Computing, 14th International Conference on Pervasive Intelligence and Computing, 2nd International Conference on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE (2016)

    Google Scholar 

  29. Mukkavilli, S.K., Shetty, S.: Mining concept drifting network traffic in cloud computing environments. In: Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2012). IEEE Computer Society (2012)

    Google Scholar 

  30. Raja, M.C., Rabbani, M.M.A.: Combined analysis of support vector machine and principle component analysis for IDS. In: International Conference on Communication and Electronics Systems (ICCES). IEEE (2016)

    Google Scholar 

  31. Fan, M.-J., et al.: TTRSIS: a cloud computing platform for rice functional genomics research through a reverse genetics approach. In: 2011 IEEE 11th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE (2011)

    Google Scholar 

  32. Kannan, A., et al.: Genetic algorithm based feature selection algorithm for effective intrusion detection in cloud networks. In: Data Mining Workshops (ICDMW). IEEE (2012)

    Google Scholar 

  33. Yin, H., Gai, K., Wang, Z.: A classification algorithm based on ensemble feature selections for imbalanced-class dataset. In: IEEE International Conference on High Performance and Smart Computing (HPSC), 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), and IEEE International Conference on Intelligent Data and Security (IDS). IEEE (2016)

    Google Scholar 

  34. Justin, V., Marathe, N., Dongre, N.: Hybrid IDS using SVM classifier for detecting DoS attack in MANET application. In: 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), pp. 775–778. IEEE (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Lovely Professional University, Phagwara, India

    Aditya Bakshi

  2. Shri Mata Vaishno Devi University, Katra, India

    Sunanda

Authors
  1. Aditya Bakshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sunanda
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aditya Bakshi .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Maharshi Dayanand University, Rohtak, Haryana, India

    Ashish Kumar Luhach

  2. Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

  3. National Chung Cheng University, Minxiong Township, Chiayi County, Taiwan

    Pao-Ann Hsiung

  4. Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia

    Kamarul Bin Ghazali Hawari

  5. Saint Mary’s University, Halifax, NS, Canada

    Pawan Lingras

  6. Department of Computer Science and Engineering, Jaypee University of Information Technology, Kandaghat, India

    Pradeep Kumar Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bakshi, A., Sunanda (2019). A Comparative Analysis of Different Intrusion Detection Techniques in Cloud Computing. In: Luhach, A., Singh, D., Hsiung, PA., Hawari, K., Lingras, P., Singh, P. (eds) Advanced Informatics for Computing Research. ICAICR 2018. Communications in Computer and Information Science, vol 956. Springer, Singapore. https://doi.org/10.1007/978-981-13-3143-5_30

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-3143-5_30

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3142-8

  • Online ISBN: 978-981-13-3143-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation