Document Type: Original Research Paper

Authors

Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Abstract

Abstract— In order to provide complete security in a computer system and to prevent intrusion, intrusion detection systems (IDS) are required to detect if an attacker crosses the firewall, antivirus, and other security devices. Data and options to deal with it. In this paper, we are trying to provide a model for combining types of attacks on public data using combined methods of genetic algorithm and neural network. The goal is to make the designed model act as a measure of system attack and combine optimization algorithms to create the ultimate accuracy and reliability for the proposed model and reduce the error rate. To do this, we used a feedback neural network, and by examining the worker, it can be argued that this research with the new approach reduces errors in the classification.with the rapid development of communication and information technology and its applications, especially in computer networks, there is a new competition in information security and network security.

Keywords

Main Subjects

[1] H.T. Elshoush, I.M. Osman ,2011 . "Alert correlation in collaborative intelligent intrusion detection systems : A survey", applied soft computing, Elsevier, pp. 221-238
[2] Li, Yinhui, et al , 2012. "An efficient intrusion detection system based on support vector machines and gradually feature removal method." Expert Systems with Applications , pp. 424-430.
[3] Panda, Mrutyunjaya, Ajith Abraham, and Manas Ranjan Patra , 2012. "A Hybrid Intelligent Approach for Network Intrusion Detection." Procedia Engineering,pp.1-9.‏
[4] P.Srinivasu, and S. Avadhani , 2012. "Genetic Algorithm based Weight Extraction Algorithm for Artificial Neural Network Classifier in Intrusion Detection."Procedia‌ Engineering‌,‌pp.144-153.‏
[5] Horng, Shi-Jinn, et al , 2011. "A novel intrusion detection system based on hierarchical clustering and support vector machines." Expert systems with Applications , pp. 306-313.
[6] Ozge Cepheli,Saliha Buyukcorak and Gunes Karabulut Kurt, 2016 ." Hybrid Intrusion Detection System for DDOS Attacks. " Journal of Electrical and Computer Engineering,Article ID1075648,8 pages.
[7] M. Li, S. Pan, Y. Zhang, and X. Cai ,2016. “Classifying networked text data with positive and unlabeled examples,” Pattern Recognition Letters, vol. 77, pp. 1–7.
[8] Y. Gong, S. Mabo, C. Chen , 2009 . "Intrusion detection system combining misuse detection and anomaly detection using genetic network programming", ICROS-SICE international joint conference,pp. 3463-3467.
[9] J. Yang, X. Chen, X._Xiang,_J. Wan, 2010 ." HIDS-DT: An effective hybrid intrusion detection system based on decision tree", IEEE international conference on communications and mobile computing, pp. 70-75.
[10] H.T. Elshoush, I.M. Osman, 2011 ." Reducing false positives through fuzzy alert correlation in collaborative intelligent intrusion detection systems : A review", IEEE international conference on fuzzy systems , pp. 1-8
[11] J.Zhang, and X.Chen , 2012."Research on Intrusion Detection of Database based on Rough Set"‌ , International Conference on Solid State Devices and Materials Science, Physics Procedia ,pp.1637-1641.
[12] Z.Muda, et al, 2011."Intrusion detection based on K-Means clustering and Naïve Bayes classification." Information Technology in Asia (CITA 11), 7th International Conference on. IEEE.
[13] Neelam Dwivedi and Aprna Tripathi , 2015." Event Correlation for Intrusion Detection Systems. " IEEE International Conference on Computation & Communication Technology .