A Proposed Heuristic Optimization Algorithm for Detecting Network Attacks
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References
Aghdam, M. H., & Kabiri, P. (2016). Feature Selection for Intrusion Detection System Using Ant ColonyOptimization. International Journal of Network Security, 18(3), 420-432.
Ahmad, I., Abdulah, A. B., Alghamdi, A. S., Alnfajan, K., & Hussain, M. (2015). Feature Subset Selection for Network Intrusion Detection Mechanism Using Genetic Eigen Vectors. International Conference on Telecommunication Technology and Applications (CSIT), 5.
Brunswick, U. O. (2017). Retrieved from http://nsl.cs.unb.ca/NSL-KDD/
Dhanjibhai Serasiya, S., & Chaudhary, N. (2012). Simulation of Various Classifications Results using WEKA.
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, 1(3), 155-160.
Goldberg, D. (1989). Genetic Algorithms in Search, Optimization, and Machine learning. Addison Wesley.
Ibrahim, H. E., Badr, S. M., & Shaheen, M. A. (2012). Adaptive Layered Approach using Machine Learning Techniques with Gain Ratio for Intrusion Detection Systems. International Journal of Computer Applications (0975 – 8887), 56(7), 10-16.
Ibrahim, L. (2010). Anomaly network intrusion detection system based on distributed time-delay neural network(DTDNN). Journal of Engineering Science and Technology, 5(4), 457 – 471.
Kaushik, S. S., & Deshmukh, P. R. (2011). Detection of attacks in an intrusion detection system. International Journal of Computer Science and Information Technologies (IJCSIT), 2(3), 982-986.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proc. IEEE Int. Jt. Conf. neural networks, 4(6), 1942–1948.
Majeed, S. K., Hashem, S. H., & Gbashi, I. K. (2013). Propose HMNIDS Hybrid Multilevel Network Intrusion Detection System. IJCSI International Journal of Computer Science Issues, 10(5), 200-208.
Mirjalili, S., Hashim, S. Z. M., & Sardroudi, H. M. (2012). Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm. Appl. Math. Comput., 1125–11137.
Newton, I. (1729). In experimental philosophy particular propositions are inferred from the phenomena and afterwards rendered general by induction. Andrew Motte’s English translation.
Parvat, T. J., & Chandra, P. (2015). Modeling Intrusion Detection Systems with Machine Learning And Selected Attributes. Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2(6), 1553-1557.
Rahmani, M. (2008). Particle swarm optimization of artificial neural networks for autonomous robots.
Chalmers University of Technolog.
Sadek, R. A., Soliman, M. S., & Elsayed, H. S. (2013). Effective Anomaly Intrusion Detection System based on Neural Network with Indicator Variable and Rough set Reduction. IJCSI International Journal of Computer Science Issues, 10(6), 227-233.
Sharma, N., & Mukherjee, S. (2012). A Layered Appoach To Enhance Detection Of Novel Attacks In IDS.
International Journal of Advances in Engineering & Technology, 4(2), 444-455.
Tang, H., & Cao, Z. (2009). Machine Learning-based Intrusion Detection Algorithms. Journal of Computational Information Systems(5:6), 1825-1831.
Wahba, Y., El Salamouny, E., & El Taweel, G. (2015). IJCSI International Journal of Computer Science Issues, 12(3), 255-262.
Wang, D., Yeung, D. S., & Tsang, E. C. (2007). Weighted Mahalanobis Distance Kernels for Support Vector Machines. IEEE Transactions on Neural Networks, 18(5), 1453-1462.
Witten, I. H., Frank, E., & Hall, M. A. (2005). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Elsevier Inc.
Yu, J., Wang, S., & Xi, L. (2008). Evolving artificial neural networks using an improved PSO and DPSO.
Neurocomputing, 71(4-6), 1054–1060.
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