Accurate Intrusion Detection Based On Feature Optimization Using Plant Grow Algorithm
Keywords:
IDS, Feature Matrix, SVM, Accuracy, Precision, Recall, KDDCUP99, Machine Learning, features, Detection
Abstract
The process of features reduction enhanced the performance of the intrusion detection system. Nowadays used various features reduction algorithms are used for static as well as dynamic features reduction. The feature reduction technique behaves in dual mode. The reduction of features cannot have fixed how many features are reducing for the better detection process of intrusion. The process of features reduction used plant grow optimization algorithm and classification using support vector machine algorithm.
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References
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[3]. D. P. Gaikwad and Ravindra C. Thool "Intrusion Detection System Using Bagging Ensemble Method of Machine Learning", IEEE, 2015, Pp 291-295.
[4]. Shelly Xiaonan Wu and Wolfgang Banzhaf "The Use of Computational Intelligence in Intrusion Detection Systems: A Review", Applied Soft Computing, 2010, Pp 2-42.
[5]. Shi-Jinn Horng, Ming-Yang Su, Yuan-Hsin Chen, Tzong-Wann Kao, Rong-Jian Chen, Jui-Lin Lai and Citra Dwi Perkasa "A novel intrusion detection system based on hierarchical clustering and support vector machines", Elsevier, 2011, Pp 306-313.
[6]. Asaf Shabtai, Uri Kanonov and Yuval Elovici "Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method", The Journal of Systems and Software, 2010, Pp 1524–1537.
[7]. Monowar H. Bhuyan, D. K. Bhattacharyya and J. K. Kalita "Network Anomaly Detection: Methods, Systems and Tools", IEEE, 2014, Pp 303-336.
[8]. S. Revathi and Dr A. Malathi "A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection", IJERT., 2013, Pp 1848-1853.
[9]. Mahbod Tavallaee, Natalia Stakhanova and Ali A. Ghorbani "Towards Credible Evaluation of Anomaly-based Intrusion Detection Methods", IEEE, 2010, Pp 1-10.
[10]. Shaik Akbar, Dr.K.Nageswara Rao and Dr J.A.Chandulal "Intrusion Detection System Methodologies Based on Data Analysis", International Journal of Computer Applications, 2010, Pp 10-20.
[11]. Jayveer Singh and Manisha J. Nene "A Survey on Machine Learning Techniques for Intrusion Detection Systems", International Journal of Advanced Research in Computer and Communication Engineering, 2013, Pp 4349-4355.
[12]. A. M. Chandrashekhar and K. Raghuveer "Fortification of Hybrid Intrusion Detection System Using Variants of Neural Networks and Support Vector", IJNSA, 2013, Pp 71-90.
[13]. Álvaro Herrero, Martí Navarro, Emilio Corchado and Julián “RT-MOVICAB-IDS: Addressing Real-Time Intrusion Detection”, Elsevier, 2013, Pp 1-24.
[14]. Bharanidharan Shanmugam and NorbikBashah Idris "Hybrid Intrusion Detection Systems (HIDS) using Fuzzy Logic", Intrusion Detection Systems, 2011, Pp 1-21.
[15]. Kumar, Vikash, Ditipriya Sinha, Ayan Kumar Das, Subhash Chandra Pandey, and Radha Tamal Goswami. "An integrated rule-based intrusion detection system: Analysis on UNSW-NB15 data set and the real-time online dataset." Cluster Computing (2019): 1-22.
[16]. Anshul Chaturvedi and Vineet Richharia, "A Novel Method for Intrusion Detection Based on SARSA and Radial Bias Feed Forward Network (RBFFN)", international journal of computers & technology, vol 7, no 3.
[17]. Jain, Upendra "An Efficient intrusion detection based on Decision Tree Classifier using feature Reduction", International Journal of Scientific and Research Publications, Vol. 2, Jan. 2012.
[18]. E. Blanzieri and A. Bryl "A survey of learning-based techniques of email spam filtering" Artif. Intell. Rev., vol. 29, no. 1, pp. 63–92, 2008.
[19]. D. Sculley and G. Cormack "Filtering email spam in the presence of noisy user feedback" in Proc. 5th Email Anti-Spam Conf., 2008, pp. 1– 10.
[20]. HengjieLi, Jiankun Wang "Intrusion Detection System by Integrating PCNN and Online Robust SVM" IFIP International Conference on Network and Parallel Computing, 2007.pp 250-255.
[21]. V. Engen, J. Vincent, and K. Phalp "Enhancing network-based intrusion detection for imbalanced data" Int. J. Knowl.-Based Intell. Eng. Syst., vol. 12, no. 5–6, pp. 357–367,2008.
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[23]. K. Shafi, T. Kovacs, H. A. Abbass, and W. Zhu "Intrusion detection with evolutionary learning classifier systems" Nat. Comput., vol. 8, no. 1, pp. 3–27,2009.
[24]. Y. Yang and S. A. Elfayoumy "Anti-spam filtering using neural networks and Bayesian classifiers" in Proc. IEEE Int. Symp. Comput. Intell. Robot. Autom., 2007, pp.272–278.
[25]. Mohammad Behdad, Luigi Barone, Mohammed Bennamoun and Tim French "Nature-Inspired Techniques in the Context of Fraud Detection" in IEEE transactions on systems, man, and cybernetics part c: applications and reviews, vol. 42, no. 6, November 2012.
[26]. Mewada, Arvind, and Rupesh Kumar Dewang. "Deceptive reviewer detection by analyzing web data using HMM and similarity measures." Materials Today: Proceedings (2021).
Published
2021-08-25
How to Cite
Kumari, M., & Raghuvanshi, M. (2021). Accurate Intrusion Detection Based On Feature Optimization Using Plant Grow Algorithm. International Journal of Advanced Computer Technology, 10(4), 01-05. Retrieved from https://ijact.org/index.php/ijact/article/view/88
Section
Articles
