Abstract
Cloud computing has become an innovative technology. Recent advances in hardware and software have put tremendous pressure on administrators, who manage these resources to provide an uninterrupted service. System administrators should be familiar with cloud-server monitoring and network tools. The main focus of the present research is the design of a model that prevents distributed denial-of-service attacks based on host-based intrusion detection protection systems over hypervisor environments. The prevention model uses principal component analysis and linear discriminant analysis with a hybrid, nature-inspired metaheuristic algorithm called Ant Lion optimisation for feature selection and artificial neural networks to classify and configure the cloud server. The current results represent a feasible outcome for a good intrusion detection and prevention framework for DDoS-cloud computing systems based on statistics and predicted techniques.
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
Subashini, S., Kavitha, V.: A survey on security issues in service delivery models of cloud computing. J. Netw. Comput. Appl. 34, 1–11 (2011)
Beloglazov, A., Abawajy, J., Buyya, R.: Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Gener. Comput. Syst. 28, 755–768 (2012)
Dorbala, S.Y., Kishore, R., Hubballi, N.: An experience report on scalable implementation of ddos attack detection. In: International Conference on Advanced Information Systems Engineering, pp. 518–529. Springer (2015)
Gérard, K., Hamaide, M., Sadre, R., Van Roy, P., Bilal, M.: Collaborative intrusion detection system for small computers
Anusha, K., Sathiyamoorthy, E.: OMAMIDS: ontology based multi-agent model intrusion detection system for detecting web service attacks. J. Appl. Secur. Res. 11, 489–508 (2016)
Akamai (2017)
Ullrich, J., Zseby, T., Fabini, J., Weippl, E.: Network-based secret communication in clouds: a survey. IEEE Commun. Surv. Tutor. 19, 1112–1144 (2017)
Suryawanshi, M.S.M., Kolhe, M.V.L.: Big data analytics: requirements and characteristics. Int. J. 4, 46–51 (2017)
Zargar, S.T., Joshi, J., Tipper, D.: A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Commun. Surv. Tutor. 15, 2046–2069 (2013)
Sun, D., Zhang, J., Fan, W., Wang, T., Liu, C., Huang, W.: SPLM: security protection of live virtual machine migration in cloud computing. In: Proceedings of the 4th ACM International Workshop on Security in Cloud Computing, pp. 2–9. ACM (2016)
Gupta, B., Badve, O.P.: Taxonomy of DoS and DDoS attacks and desirable defense mechanism in a cloud computing environment. In: Neural Computing and Applications, pp. 1–28 (2016)
Naik, N., Jenkins, P.: An analysis of open standard identity protocols in cloud computing security paradigm. 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), pp. 428–431. IEEE (2016)
Patel, A., Taghavi, M., Bakhtiyari, K., JúNior, J.C.: An intrusion detection and prevention system in cloud computing: a systematic review. J. Netw. Comput. Appl. 36, 25–41 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Jaber, A.N., Zolkipli, M.F., Shakir, H.A., Jassim, M.R. (2018). Host Based Intrusion Detection and Prevention Model Against DDoS Attack in Cloud Computing. In: Xhafa, F., Caballé, S., Barolli, L. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-319-69835-9_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-69835-9_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-69834-2
Online ISBN: 978-3-319-69835-9
eBook Packages: EngineeringEngineering (R0)