International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 10, No. 12, 8 Dec. 2018
Cover page and Table of Contents: PDF (size: 858KB)
An Integrated Perceptron Kernel Classifier for Intrusion Detection System
Full Text (PDF, 858KB), PP.11-20
Views: 0 Downloads: 0
Author(s)
Ruby Sharma
1,*
Sandeep Chaurasia
1
Index Terms
Network Security, Intrusion Detection System, Fuzzy C-Means Clustering, Density based Distance Maximization approach, Ant Colony Optimization, Ensemble Classifier
Abstract
Because of the tremendous growth in the network based services as well as the sharing of sensitive data, the network security becomes a challenging task. The major risk in the network is the intrusion. Among various hardening system, intrusion detection system (IDS) plays a significant role in providing network security. Several traditional techniques are utilized for network security but still they lack in providing security. The major drawbacks of these network security algorithms are inaccurate classification results, increased false alarm rate, etc. to avoid these issues, an Integrated Perceptron Kernel Classifier is proposed in this work. The input raw data are preprocessed initially for the purpose of removing the noisy data as well as irrelevant data. Then the features form the preprocessed data are extracted by clustering it depending up on the Fuzzy C-Mean Clustering. Then the clustered features are extracted by employing the Density based Distance Maximization approach. After this the best features are selected using Modified Ant Colony Optimization by improving the convergence time. Finally the extracted best features are classified for identifying the network traffic as normal and abnormal by introducing an Integrated Perceptron Kernel Classifier. The performance of this framework is evaluated and compared with the existing classifiers such as SVM and PNN. The results prove the superiority of this framework with better classification accuracy.
Cite This Paper
Ruby Sharma, Sandeep Chaurasia, "An Integrated Perceptron Kernel Classifier for Intrusion Detection System", International Journal of Computer Network and Information Security(IJCNIS), Vol.10, No.12, pp.11-20, 2018. DOI:10.5815/ijcnis.2018.12.02
Reference
[1]B. Daya, "Network security: History, importance, and future," University of Florida Department of Electrical and Computer Engineering, vol. 4, 2013.
[2]K. Purohit, "Introduction to Computer Network with Security."
[3]M. V. Pawar and J. Anuradha, "Network security and types of attacks in network," Procedia Computer Science, vol. 48, pp. 503-506, 2015.
[4]H. A. M. Uppal, M. Javed, and M. Arshad, "An overview of intrusion detection system (IDS) along with its commonly used techniques and classifications," International Journal of Computer Science and Telecommunications, vol. 5, pp. 20-24, 2014.
[5]S. Elhag, A. Fernández, A. Bawakid, S. Alshomrani, and F. Herrera, "On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on intrusion detection systems," Expert Systems with Applications, vol. 42, pp. 193-202, 2015.
[6]R. A. R. Ashfaq, X.-Z. Wang, J. Z. Huang, H. Abbas, and Y.-L. He, "Fuzziness based semi-supervised learning approach for intrusion detection system," Information Sciences, vol. 378, pp. 484-497, 2017.
[7]A. A. Aburomman and M. B. I. Reaz, "A novel SVM-kNN-PSO ensemble method for intrusion detection system," Applied Soft Computing, vol. 38, pp. 360-372, 2016.
[8]E. Aghaei and G. Serpen, "Ensemble classifier for misuse detection using N-gram feature vectors through operating system call traces," International Journal of Hybrid Intelligent Systems, pp. 1-14, 2017.
[9]W.-C. Lin, S.-W. Ke, and C.-F. Tsai, "CANN: An intrusion detection system based on combining cluster centers and nearest neighbors," Knowledge-based systems, vol. 78, pp. 13-21, 2015.
[10]A. S. Eesa, Z. Orman, and A. M. A. Brifcani, "A new feature selection model based on ID3 and bees algorithm for intrusion detection system," Turkish Journal of Electrical Engineering & Computer Sciences, vol. 23, pp. 615-622, 2015.
[11]M. H. Aghdam and P. Kabiri, "Feature Selection for Intrusion Detection System Using Ant Colony Optimization," IJ Network Security, vol. 18, pp. 420-432, 2016.
[12]N. Pandeeswari and G. Kumar, "Anomaly detection system in cloud environment using fuzzy clustering based ANN," Mobile Networks and Applications, vol. 21, pp. 494-505, 2016.
[13]A. S. Eesa, Z. Orman, and A. M. A. Brifcani, "A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems," Expert Systems with Applications, vol. 42, pp. 2670-2679, 2015.
[14]F. Kuang, W. Xu, and S. Zhang, "A novel hybrid KPCA and SVM with GA model for intrusion detection," Applied Soft Computing, vol. 18, pp. 178-184, 2014.
[15]F. Kuang, S. Zhang, Z. Jin, and W. Xu, "A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection," Soft Computing, vol. 19, pp. 1187-1199, 2015.
[16]M. A. M. Hasan, M. Nasser, S. Ahmad, and K. I. Molla, "Feature selection for intrusion detection using random forest," Journal of information security, vol. 7, p. 129, 2016.
[17]I. Ahmad, "Feature selection using particle swarm optimization in intrusion detection," International Journal of Distributed Sensor Networks, vol. 11, p. 806954, 2015.
[18]B. Aslahi-Shahri, R. Rahmani, M. Chizari, A. Maralani, M. Eslami, M. Golkar, et al., "A hybrid method consisting of GA and SVM for intrusion detection system," Neural computing and applications, vol. 27, pp. 1669-1676, 2016.
[19]J. Kevric, S. Jukic, and A. Subasi, "An effective combining classifier approach using tree algorithms for network intrusion detection," Neural Computing and Applications, vol. 28, pp. 1051-1058, 2017.
[20]E. De la Hoz, E. De La Hoz, A. Ortiz, J. Ortega, and B. Prieto, "PCA filtering and probabilistic SOM for network intrusion detection," Neurocomputing, vol. 164, pp. 71-81, 2015.
[21]O. Al-Jarrah and A. Arafat, "Network intrusion detection system using neural network classification of attack behavior," Journal of Advances in Information Technology Vol, vol. 6, 2015.
[22]"http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html."
[23]"https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-IDS-Datasets/."