Fixed Neuro Fuzzy Classification Technique For Intrusion Detection Systems
[Full Text]
AUTHOR(S)
Dr A M Viswa Bharathy, Dr R Bhavani
KEYWORDS
ABSTRACT
Over the years, design and development of intrusion detection systems have gone to new heights. A lot of algorithms and techniques have been developed and tested for the better security of our internetwork systems. Stand-alone efficient approaches, hybrid techniques and frameworks are given by many researchers and scientists towards the enhancement of intrusion detection andprevention systems. In this paper, we study various intrusion detection approaches with their pros and cons and conclude statistically with the proposed Fixed Neuro Fuzzy Classification (FNFC), a new technique for intrusion detection algorithm.
REFERENCES
[1] Farid, DM, Harbi, N &Rahman, MZ 2010, ‘Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection’, International Journal of Network Security & its Applications, vol. 2, no. 2, pp. 12-25.
[2] Peddabachigari, S, Abraham, A, Grosan, C & Thomas, J 2007, ‘Modeling Intrusion Detection System using Hybrid Intelligent Systems’, Journal of Network and Computer Applications, vol. 30, no. 1, pp. 114-132.
[3] Yasami, Y &Mozaffari, SP 2010, ‘A Novel Unsupervised Classification Approach for Network Anomaly Detection by K-Means Clustering and ID3 Decision Tree Learning Methods’, The Journal of Supercomputing, vol. 53, no. 1, pp. 231-245.
[4] Tang, DH & Cao, Z 2009, ‘Machine Learning-based Intrusion Detection Algorithms’, Journal of Computational Information Systems, vol. 5, no. 6, pp. 1825-1831.
[5] Agarwal, B & Mittal, N 2012, ‘Hybrid Approach for Detection of Anomaly Network Traffic using Data Mining Techniques’, Procedia Technology, vol. 6, no. 1, pp. 996-1003.
[6] Ravale, U, Marathe, N &Padiya, P 2015, ‘Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function’, Procedia Computer Science, vol. 45, no. 1, pp. 428 – 435.
[7] Ghanem, TF, Elkilani, WS & Abdul-Kader, HM 2014, ‘A Hybrid Approach for Efficient Anomaly Detection using Metaheuristic Methods’, Journal of Advanced Research, vol. 6, no. 1, pp. 609-619.
[8] Sangeetha, K, Periasamy, PS &Prakash, S 2015, ‘Identification of Network Intrusion with Efficient Genetic Algorithm using Bayesian Classifier’, Proceedings of the International Conference on Computer Communication and Informatics, pp. 1-4.
[9] Bamakan, SMH, Amiri, B & Shi, MMY 2015, ‘A New Intrusion Detection Approach Using PSO based Multiple Criteria Linear Programming’, Procedia Computer Science, vol. 55, no. 1, pp. 231-237.
[10] Ikram, ST &Cherukuri, AK 2016, ‘Intrusion Detection Model using Fusion of Chi-Square Feature Selection and Multi Class SVM’, Journal of King Saud University–Computer and Information Sciences (in press).
[11] Dhanachandra, N, Manglem, K &Chanu, YJ 2015, ‘Image Segmentation using K-means Clustering Algorithm and Subtractive Clustering Algorithm’, Procedia Computer Science, vol. 54, no. 1, pp. 764-771.
[12] Cepheli, O, Buyukcorak, S & Kurt, GK 2016, ‘Hybrid Intrusion Detection System for DDoS Attacks’, Journal of Electrical and Computer Engineering, vol. 2016, Article ID 1075648.
[13] Ahmad, I 2015, ‘Feature Selection Using Particle Swarm Optimization in Intrusion Detection’, International Journal of Distributed Sensor Networks, vol. 11, no. 10, A. 806954.
[14] Soleimani, H &Kannan, G 2015, ‘A Hybrid Particle Swarm Optimization and Genetic Algorithm for Closed-Loop Supply Chain Network Design in Large-Scale Networks’, Applied Mathematical Modelling, vol. 39, no. 14, pp. 3990-4012.
[15] Idris, I, Selamat, A, Nguyen, NT, Omatu, S, Krejcar, O, Kuca, K &Penhaker, M 2015, ‘A Combined Negative Selection Algorithm–Particle Swarm Optimization for an Email Spam Detection System’, Engineering Applications of Artificial Intelligence, vol. 39, no. 1, pp. 33-44.
[16] De, A, Mamanduru, VKR, Gunasekaran, A, Subramanian, N &Tiwari, MK 2016, ‘Composite Particle Algorithm for Sustainable Integrated Dynamic Ship Routing and Scheduling Optimization’, Computers & Industrial Engineering, vol. 96, no. 1, pp. 201-215.
[17] Eddaly, M, Jarboui, B &Siarry, P 2016, ‘Combinatorial Particle Swarm Optimization for Solving Blocking Flowshop Scheduling Problem’, Journal of Computational Design and Engineering, vol. 3, no. 4, pp. 295–311.
[18] Moustafa, N, Elhosseini, M, Taha, TH & Salem, M 2016, ‘Fragmented Protein Sequence Alignment using Two-Layer Particle Swarm Optimization (FTLPSO)’, Journal of King Saud University-Science, (in press).
[19] Bamakan, SMH, Wang, H, Yingjie, T & Shi, Y 2016, ‘An Effective Intrusion Detection Framework based on MCLP/SVM Optimized by Time-Varying Chaos Particle Swarm Optimization’, Neurocomputing, vol. 199, no. C, pp. 90-102 .
[20] Al-Yaseen, WL, Othman, ZA &Nazri, MZA 2015, ‘Hybrid Modified K-Means with C4.5 for Intrusion Detection Systems in Multiagent Systems’, The Scientific World Journal, A. 294761.
[21] Eesa, AS, Orman, Z &Brifcani, AMA 2015, ‘A Novel Feature-Selection Approach Based on the Cuttlefish Optimization Algorithm for Intrusion Detection Systems’, Expert Systems with Applications, vol. 42, no. 1, pp. 2670-2679.
[22] ViswaBharathy, AM, Basha, AM 2017, ‘A Multi-Class Classification MCLP Model with Particle Swarm Optimization for Network Intrusion Detection’, Sadhana: Academy Proceedings in Engineering Science, vol. 42, no. 5, pp. 631-640.
[23] ViswaBharathy, AM, Basha, AM 2016, ‘A Hybrid Intrusion Detection System Cascading Support Vector Machine and Fuzzy Logic’, World Applied Sciences Journal, vol. 35, no. 1, pp. 104-109.
[24] ViswaBharathy, AM, Basha, AM 2016, ‘A Hybrid Network Intrusion Detection Technique using Variable Multiplicative K-Means with Self-Organising PSO’, Middle East Journal of Scientific research, vol. 24, no. 12, pp. 3812-3819.
[25] ViswaBharathy, AM, Basha, AM, 2016, ‘A Detailed Review on Intrusion Detection Systems in Mobile Ad-Hoc Networks Based on Attack Classification and its Detection Technique’ in International Journal of Innovative Research in Science and Technology, Vol. 2, no. 9, pp. 228-231.
|