Abstract
To prevent cyber-attacks, cloud-based systems mainly depend upon different types of intrusion detection systems (IDS). Most of the approaches have high detection rate for known attacks. But in case of unknown attacks or new attacks, these intrusion detection system increases false alarm rate. Another problem is that the reduction of false alarm rate increases the computational complexities in case of genetic algorithm-based IDS and ANN-based IDS. For instance, to tackle challenges like zero-day attack, the only way is to rely upon a robust data-driven approach for security in cloud. Actually in cloud huge amount of data are processed for various activities. It is very difficult to correlate events over such huge amount of data. To improve the abilities of monitoring and fast decision-making, context management is used for correlating events and inferring contexts and evidences. In this paper, a new data-driven framework has been proposed which utilizes ontology and knowledge base to detect cyber-attack with intrusion detection system in cloud.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Li, C. (ed.): Handbook of Research on Computational Forensics, Digital Crime, and Investigation: Methods and Solutions. IGI Global (2010)
Qiu, J., Wu, Q., Ding, G., Xu, Y., Feng, S.: A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. 67 (2016)
Shahul Kshirsagar, S.Y.: Intrusion detection systems: a survey and analysis of classification techniques. Int. J. Scienti. Res. Eng. Technol. IJRSET 3(4), 742–747 (2014)
Dharmapurikar, S., Krishnamurthy, P., Sproull, T., Lockwood, J.: Deep packet inspection using parallel bloom filters. In: Proceedings of 11th Symposium on High Performance Interconnects, pp. 44–51. IEEE (2003)
Lee, I., Jeong, S., Yeo, S., Moon, J.: A novel method for SQL injection attack detection based on removing SQL query attribute values. Math. Comput. Model. 55(1–2), 58–68 (2012)
Chen, W.-H., Hsu, S.-H., Shen, H.-P.: Application of SVM and ANN for intrusion detection. Comput. Oper. Res. 32(10), 2617–2634 (2005)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1988)
Dotcenko, S., Vladyko, A., Letenko, I.: A fuzzy logic-based information security management for software-defined networks. In: 16th International Conference on Advanced Communication Technology, pp. 167–171 (2014)
Shafer, G.: A mathematical theory of evidence turns 40. Int. J. Approxi. Reason. 79, 7–25, 2016. 40 years of Research on Dempster-Shafer theory
Yazar, Z.: A qualitative risk analysis and management tool—CRAMM. SANS InfoSec Reading Room White Paper 11, 12–32 (2002)
Acknowledgments
This research was supported by Information Security Education and Awareness (ISEA) Project II funded by Ministry of Electronics and Information Technology (MeitY), Govt. of India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Badde, S., Kumar, V., Chatterjee, K., Sinha, D. (2021). Cyber Attack Detection Framework for Cloud Computing. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_23
Download citation
DOI: https://doi.org/10.1007/978-981-15-5679-1_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5678-4
Online ISBN: 978-981-15-5679-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)