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Kazemitabar, S., Taheri, R., Kheradmandian, G. (2019). A Novel Technique for Improvement of Intrusion Detection via Combining Random Forrest and Genetic Algorithm. Electronic and Cyber Defense, 10(3), 287-296.
seyyed Javad Kazemitabar; Reyhaneh Taheri; Ghorban Kheradmandian. "A Novel Technique for Improvement of Intrusion Detection via Combining Random Forrest and Genetic Algorithm". Electronic and Cyber Defense, 10, 3, 2019, 287-296.
Kazemitabar, S., Taheri, R., Kheradmandian, G. (2019). 'A Novel Technique for Improvement of Intrusion Detection via Combining Random Forrest and Genetic Algorithm', Electronic and Cyber Defense, 10(3), pp. 287-296.
Kazemitabar, S., Taheri, R., Kheradmandian, G. A Novel Technique for Improvement of Intrusion Detection via Combining Random Forrest and Genetic Algorithm. Electronic and Cyber Defense, 2019; 10(3): 287-296.

A Novel Technique for Improvement of Intrusion Detection via Combining Random Forrest and Genetic Algorithm

علوم و فناوریهای پدافند نوین
Article 9, Volume 10, Issue 3 - Serial Number 37, July 2019, Pages 287-296    PDF (887.64 K)
Document Type: Original Article
Authors
seyyed Javad Kazemitabar* 1; Reyhaneh Taheri2; Ghorban Kheradmandian3
1Babol Noshirvani University of Technology
2Mazandaran University of Science and Technology
3Tosan Intelligent Data Miners
Receive Date: 23 July 2018,  Revise Date: 22 October 2018,  Accept Date: 23 December 2018 
Abstract
As computer networks grow, so attacks and intrusions to these networks are increased. In order to have a fully secure computer network, one needs ‘intrusion detection systems’ (IDS) on top of firewalls. The goal of using an IDS is to supervise the abnormal activities and differentiate between normal and abnormal activities in a host system or in a network. An efficient IDS has high detection rate while keeping a low false alarm rate. In this paper, a new approach to classify KDD-Cup-99 data set using a combination of random forest method and genetic algorithm is presented. The purpose is to increase the speed of learning and test phases while improving the accuracy. Random forest is an ensemble learning method based on decision trees. Due to its relatively simple structure and good performance, it is used in many supervised learning applications.  However, like all tree based machine learning algorithms, having too many categorical features, can be a problem both for the speed and accuracy. This is exactly the case with the problem in hand, i.e. intrusion detection; many of the features are in the form of categorical data. For example, in R language, the maximum number of definable categorical features for random forest is 53. The contribution of this work is resolving this issue with the aid of Genetic Algorithm (GA). In this research information gain as a measure of importance is defined and the number of features is reduced based on genetic algorithm.
Keywords
Signature-Based Intrusion Detection; Data Mining; Genetic Algorithm; Random Forest Algorithm
References

[1]     Cumming, I. G.; Wong, F. H. "Digital Processing of Synthetic Aperture Radar Data”; Artech House: London, 2005.

[2]     Brown, J.; Anwar, M.; Dozier, G. “An Evolutionary General Regression Neural Network Classifier for Intrusion Detection”; Proc. 25th Int. Conf. Comput. Commun. and Network, Waikoloa, USA, 2016.

[3]     Stolfo, S. J.; Fan, W.; Lee, W.; Prodromidis, A.; Chan, P. K. “Cost-Based Modeling for Fraud and Intrusion Detection: Results from the JAM Project”; Proc. Int. DARPA Information Survivability Conference and Exposition2000,  2, 1130.

  [4]     Tavallaee, M.; Bagheri, E.; Lu, W.; Ghorbani, A. “A Detailed Analysis of the KDD CUP 99 Data Set”; Second IEEE Symposium on Computational Intelligence for  Security and Defense Applications, 2009.

[5]     Kaushik, S. S.; Deshmukh, P. R. “Detection of  Attacks in an Intrusion Detection System”; Int. J. Comput. Sci.  Information Technol.2011,  2982-986.

[6]     McHugh, J. “Testing Intrusion Detection Systems:  A Critique of the 1998 and 1999 Darpa  Intrusion Detection System Evaluations as Performed by Lincoln Laboratory”; ACM Trans. Inform. Syst. Secur. 2000,  3, 262-294.

[7]     Okafor, A. “Entropy Based Techniques with Applications in Data Mining”; Dissertation, University of Florida, 2005.

[8]     Lin, L.; Zuo, R.; Yang, S.; Zhang, Z. “SVM Ensemble for Anomaly Detection Based on Rotation Forest”; IEEE 3th Int. Conf. Intelligent Control and Information Processing,2012.

[9]     Malik, A.; Shahzad, W.; Khan, F. “Binary PSO  and Random Forests Algorithm for Probe Attacks  Detection in a Network”; IEEECongress on Evolutionary Computation2011, 662-668.

[10]  Bukhtoyarov, V.; Zhukov, V. “Ensemble-Distributed Approach in Classification Problem Solution for Intrusion Detection Systems”; Proc. Int. Conf. Intelligent Data Engineering and Automated Learning 2014,  255-265.

[11]  Bahri, E.; Harbi, N.; Huu, H. “Approach Based Ensemble Methods for Better and Faster Intrusion Detection“; Comput. Intell. Secur. Inform. Syst. 2011, 17-24.

[12]  Pervez, M.; Farid, D. “Feature Selection and Intrusion Classification in NSL-KDD Cup 99 Dataset Employing SVMS”; 8th Int. Conf. Software, Knowledge Information Management and Applications 2014, 1-6.

[13]  Najafi, M.; Rafeh, R. “A New Light Weight Intrusion Detection Algorithm for Computer Networks”; Adv. Defence Sci. Technol. 2017, 10, 191-200.

[14]  Mitchell, T. M. “Machine Learning”; Mc-Graw-Hill Companies, Inc. ISBN 0070428077, 1997.

[15]  Stein, G.;  Chen, B.; Wu, A.; Hua, K. “Decision Tree Classifier for Network Intrusion Detection with GA-Based Feature Selection”; Proceedings of the 43rd annual Southeast regional conference 2005, 2, 136-141.

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