Network Intrusion Detection System (NIDS) in Cloud Environment based on Hidden Naïve Bayes Multiclass Classifier
DOI:
https://doi.org/10.23851/mjs.v28i2.508Keywords:
High Dimensional Datasets, Dimensionality reduction, SVD, Vector Space ModelAbstract
Cloud Environment is next generation internet based computing system that supplies customiza-ble services to the end user to work or access to the various cloud applications. In order to provide security and decrease the damage of information system, network and computer system it is im-portant to provide intrusion detection system (IDS. Now Cloud environment are under threads from network intrusions, as one of most prevalent and offensive means Denial of Service (DoS) attacks that cause dangerous impact on cloud computing systems. This paper propose Hidden naïve Bayes (HNB) Classifier to handle DoS attacks which is a data mining (DM) model used to relaxes the conditional independence assumption of Naïve Bayes classifier (NB), proposed sys-tem used HNB Classifier supported with discretization and feature selection where select the best feature enhance the performance of the system and reduce consuming time. To evaluate the per-formance of proposal system, KDD 99 CUP and NSL KDD Datasets has been used. The experi-mental results show that the HNB classifier improves the performance of NIDS in terms of accu-racy and detecting DoS attacks, where the accuracy of detect DoS is 100% in three test KDD cup 99 dataset by used only 12 feature that selected by use gain ratio while in NSL KDD Dataset the accuracy of detect DoS attack is 90 % in three Experimental NSL KDD dataset by select 10 fea-ture only.Downloads
References
Vidhya. V, "A Review of DOS Attacks in Cloud Computing," IOSR Journal of Computer Engineering, vol.16, no.5, 2014.
Singh S. and Srivastava R, "Intrusion Detection Using Data Mining Technique," International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol.2, no.4, 2013..
Bandgar M., dhurve K., Jadhav S., Kayastha V. and Parvat T. J., "Intrusion Detection System using Hidden Markov Model (HMM)," OSR Journal of Computer Engineering (IOSR-JCE), vol.10, no.3, 2013.
Madni H. A., Javed M. and M. J. Arshad, "An Overview of Intrusion Detection System (IDS) along with its Commonly Used Techniques and Classifications," International Journal of Computer Science and Telecommunications, vol.5, no.2, 2014.
Arora S. and Bawa R. K., "A Review on Intrusion Detection System to Protect Cloud Data," International Journal of Innovations & Advancement in Computer Science, vol.3, no.5, 2014.
Abhaya, Kumar K., Jha3 R. and Afroz S., "Data Mining Techniques for Intrusion Detection: A Review," International Journal of Advanced Research in Computer and Communication Engineering, vol.3, no.6, 2014.
Kanagalakshmi. R and Raj N. V., "Network Intrusion Detection Using Hidden Naive Bayes Multiclass Classifier Model," International Journal of Science, Technology & Management, vol.3, no.12, 2014.
Koc L. and Carswell A. D., "Network Intrusion Detection Using a HNB Binary Classifier," in UKSIM-AMSS International Conference on Modelling and Simulation, 2015.
Ghosh P., Debnath C., Metia D. and Dr. Dutta R., "An Efficient Hybrid Multilevel Intrusion Detection System in Cloud Environment," IOSR Journal of Computer Engineering, vol.6, no.4, 2014.
Ibrahim H. E., Badr S. M. and Shaheen M. A., "Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems," International Journal of Computer Applications, vol.56, no.7, 2012.
Mukherjeea S., Sharmaa N., "Intrusion Detection using Naive Bayes Classifier with Feature Reduction," Elsevier Ltd., 2012.
Koc L., Thomas A. Mazzuchi and Sarkani S., "A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier," Elsevier Ltd., 2012.
Padmakumari P., Surendra K., Sowmya M. and Sravya M., "Effective Intrusion Detection System for Cloud Architecture," ARPN Journal of Engineering and Applied Sciences, vol.9, no.11, 2014.
Ibrahim L. M., Basheer D. T. and Mahmod M. S., "A Comparison Study For Intrusion Database (KDD99, NSL-KDD) Based On Self Organization Map (SOM) Artificial Neural Network," Journal of Engineering Science and Technology, vol.8, no.1, 2013.
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