Journals / Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik / 2018 / Cilt: 19 - Sayı: 1

FEATURE SELECTION AND COMPARISON OF CLASSIFICATION ALGORITHMS FOR INTRUSION DETECTION

Pages
206–218
Publication Date
2022-10-11
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Then, the results of classification algorithmswere compared with these new feature sets according to the performance of the algorithms.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The increase in the frequency of use of the Internet causes the attacks on computer networks to increase.
📊
Key Findings & Data:Such phenomena alsoincrease the importance of intrusion detection systems.
💡
Conclusion & Impact:Then, the results of classification algorithmswere compared with these new feature sets according to the performance of the algorithms.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

The increase in the frequency of use of the Internet causes the attacks on computer networks to increase. Such phenomena alsoincrease the importance of intrusion detection systems. In this paper, KDD Cup 99 dataset is used for the classification of thenetwork attacks. Four different classification algorithms were used, and the results were compared. These algorithms weremultilayer perceptron network, decision trees, fuzzy unordered rule induction algorithm (FURIA) and support vector machines.The most successful algorithm in this dataset found as FURIA. As the second part of this study, the most important feature setswere found by correlation-based feature selection and best first search algorithm. Then, the results of classification algorithmswere compared with these new feature sets according to the performance of the algorithms.

🔖

Cite This Article

GÜNDÜZ, S. Y., ÇETER, M. N. (2022). FEATURE SELECTION AND COMPARISON OF CLASSIFICATION ALGORITHMS FOR INTRUSION DETECTION. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi :A-Uygulamalı Bilimler ve Mühendislik, Cilt: 19 - Sayı: 1, 206–218. https://ajindex.com/articles/438229

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (13)

  1. Hall MA. Correlation-based Feature Selection for Machine Learning. Department of Computer Science; The University of Waikato, 1999.
  2. Witten IH, Frank E, Hall MA. Data Mining Practical Machine Leanrning Tools and Techniques. Morgan Kouffman. Tavallaee M, Bagheri E, Lu W. A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications; 2009;
  3. Ottawa, ON, Canada. Lin SW, Ying KC, Lee CY, Lee ZJ. An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Applied Soft Computing 2012; 12: 3285-3290. University of California. (1999, October 8). KDD Cup 1999 Data. Available: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html Vapnik VN. Statistical learning theory. New York: Wiley, 1998.
  4. Cortes C, Vapnik V. Support-Vector Networks. Machine Learning 1995; 20: 273-297.
  5. Huhn J, Hullermeier E. FURIA: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery 2009; 19: 293-319.
  6. Han J, Kamber M. Data mining: concepts and techniques. San Francisco: Morgan Kaufmann Publishers, 2001. Haykin SS. Neural networks: a comprehensive foundation. 2nd ed. Upper Saddle River, N.J.: Prentice Hall, 1999. Alamleh AH. Network Intrusion Classification Using Data Mining Techniques. Masters MSc, Zarqa University, Jordan, 2015.
  7. Belavagi MC, Muniyal B. Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. In: Twelfth International Multi-Conference on Information Processing; 2016;
  8. Bangalore, India; pp. 117-123.
  9. Ravale U, Marathe N, Padiya P. Feature Selection Based Hybrid Anomaly Intrusion Detection System Using K Means and RBF Kernel Function. In: International Conference on Advanced Computing Technologies and Applications; 2015, pp. 428-435.
  10. Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A. Feature selection and classification in multiple class datasets: An application to KDD Cup 99 dataset. Expert Systems with Applications 2011; 38: 5947-5957.
  11. Mukherjee S, Sharma N. Intrusion Detection using Naive Bayes Classifier with Feature Reduction. In: 2nd International Conference on Computer, Communication, Control and Information Technology; 2012; vol. 4, pp. 119-128.
  12. Koc L, Mazzuchi TA, Sarkani S. A network intrusion detection system based on a Hidden Naive Bayes multiclass classifier. Expert Systems with Applications 2012; 39: 13492-13500.
  13. Abadeh MS, Mohamadi H, Habibi J. Design and analysis of genetic fuzzy syst