Total views : 273

Nature Inspired Feature Selection Approach for Effective Intrusion Detection

Affiliations

  • Department of Computer Science Engineering, Shaheed Bhagat Singh State, Technical Campus, Ferozepur, India
  • Department of Computer Applications, Shaheed Bhagat Singh State, Technical Campus, Ferozepur, India

Abstract


Objectives: To reduce the dimensionality of network traffic dataset by selecting the relevant and irredundant features for accurate and quick intrusion detection. To achieve the target, we proposed a new feature selection approach based on the nature. Methods/Statistical Analysis: The proposed Modified CuttleFish Algorithm (MCFA) approach plays a crucial role in intrusion detection by selecting appropriate subset of most relevant features from huge amount of dataset. Griewank fitness function is used to calculate the fitness of the modified cuttlefish algorithm. Naive bayes classifier is employed at the generated subset of features from benchmark KDD 99 dataset in WEKA data mining tool Compare the results of proposed approach with the existing approaches of WEKA and Improved Cuttlefish Algorithm (ICFA), with different performance metrics. Findings: As per the outcomes are obtained by the WEKA Experimenter with the 9 feature selection approaches on KDD 99 10% training dataset, it has been observed that the Consistency Subset feature selection approach with Greedy stepwise search method gives higher accurate results than other approaches and from the literature survey has been found that the ICFA performs well, but still there is problem of low True positive (TP) rate and False negative (FN) rate. These problems are addressed by the proposed feature selection approach which outer perform best from others in accuracy rate (91.79%), True positive rate (0.947), false positive rate (0.025) and ROC area (0.9982) with the minimum amount of time at 19 relevant subset of features instead 41 features. As the consequence, the proposed approach is novel from existing approaches to increase the intrusion detection rate and discard the redundant and irrelevant subset of features. Application/Improvements: MCFA has improved intrusion detection rate by increasing the TP rate and decreasing the FP rate, so MCFA can be used for the real time applications of intrusion detection system.

Keywords

Accuracy, Dimensionality, Feature Selection Approach, FP Rate, Intrusion Detection, Modified Cuttlefish Algorithm, TP Rate.

Full Text:

 |  (PDF views: 292)

References


  • Denning D. An intrusion-detection model, Software Engineering, IEEE Transactions on SE, 1987; 13(2): 222-32.
  • Stoneburner G. Underlying technical models for information technology security, NIST Special Publication 800-33, National Institute of Standards and Technology.
  • Mukherjee S, Sharma N. Intrusion detection using naive bayes classier with feature reduction. Procedia Technology, 4:119-28. 2nd International Conference on Computer, Communication, Control and Information Technology ( C3IT-2012), 2012.
  • Saad A. An overview of hybrid soft computing techniques for classier design and feature selection. In: Hybrid Intelligent Systems, 2008. HIS '08. Eighth International Conference, 2008, p. 579-83.
  • Kumar G, Kumar K. An Information theoretic approach for feature selection. Security and Communication Networks, 2011; 5(2):178-85.
  • Skaruz J, Nowacki JP, Drabik A, Seredynski F, Bouvry P. Soft computing techniques for intrusion detection of sql-based attacks. In: Proceedings of the Second International Conference on Intelligent Information and Database Systems: Part I, ACIIDS'10, p. 33, Berlin, Heidelberg. Springer-Verlag, 2010.
  • Singh R, Kumar H, Singla R. Analysis of feature selection techniques for network trac dataset. In: Machine Intelligence and Research Advancement (ICMIRA), 2013. International Conference, 2013, p. 42- 6.
  • Eesa AS, Orman Z, Brifcani AMA. A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems. Expert Systems with Applications, 2015; 42(5):2670-79.
  • Lakshmi SV, Prabakaran TE. Performance Analysis of Multiple Classifiers on KDD Cup Dataset using WEKA Tool. Indian Journal of Science and Technology, 2015 Aug; 8(17):1-10.
  • Kaur R, Kumar G, Kumar K. A Novel Feature Selection Technique based on Improved Cuttlefish Algorithm (ICFA) for Intrusion Detection. Fourth International Conference on Advances in Information Technology and Mobile Communication, Bangalore, India, Narosa Publishers.
  • Eesa AS, Orman Z. Cuttlefish Algorithm—A Novel Bio-Inspired Optimization Algorithm. International Journal of Scientific and Engineering Research, 2013; 4(9):1978-86.
  • Kaur N, Singh W. Alcoholic Behavior Prediction through Comparative Analysis of J48 and Random Tree Classification Algorithms using WEKA. Indian Journal of Science and Technology, 2016 Aug; 9(32):1-7.
  • Kaur R, Sachdeva M, Kumar K. Study and Comparison of Feature Selection Approaches for Intrusion Detection. In: Proceedings of 4th International Conference on Advancements in Engineering & Technology (ICAET-2016), India. 2016 Mar .
  • Dash P, Pattnaik S, Rath B. Knowledge Discovery in Databases (KDD) as Tools for Developing Customer Relationship Management as External Uncertain Environment: A Case Study with Reference to State Bank of India. Indian Journal of Science and Technology, 2016 Jan; 9(4):1-11.
  • Bafna P, Pillai S, Pramod D. Quantifying Performance Appraisal Parameters: A Forward Feature Selection Approach. Indian Journal of Science and Technology, 2016 Jun; 9(21):1-7.
  • NSL-KDD. The NSL - KDD intrusion dataset. http://nsl.cs.unb.ca/NSL-KDD/ .
  • Data Mining Software in Java. http://www.cs.waikato.ac.nz/~ml/weka/.
  • Shaveta E, Bhandari A, Saluja KK. Applying genetic algorithm in intrusion detection system: A comprehensive review, 2014.
  • Tsai CF. Data pre-processing by genetic algorithms for bankruptcy prediction. IEEE International Conference on Industrial Engineering and Engineering Management, 2011, p. 1780-83.
  • Sang HV, Nam NH, Nhan ND. A Novel Credit Scoring Prediction Model based on Feature Selection Approach and Parallel Random Forest. Indian Journal of Science and Technology, 2016 May; 9(20):1-6.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.