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2-layer classification model with correlated common feature selection for intrusion detection system in networks

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Abstract

The proliferation of wireless networks as a primary data transmission channel has brought about a surge in data volume but also raised security threats and privacy concerns. Intrusion Detection Systems (IDS) have proven effective in safeguarding data transmission, yet they struggle to detect minor or rare attacks that mimic normal traffic. To address this challenge, we propose a novel two-layer classification model integrating K-nearest neighbor (KNN) and support vector machines (SVMs). In the first layer, KNN classifies data into Normal, Major, and Minor Attack categories, while the second layer further distinguishes Major Attacks into DoS or Probe and Minor Attacks into U2R or R2. Our framework incorporates Common Correlated Feature Selection (CCFS) to optimize feature discrimination, partitioning training data into three groups. Furthermore, we explore data preprocessing techniques to enhance data interpretation and maintain statistical normalcy in traffic connection features. Our experimental analysis utilizes the NSL-KDD dataset, demonstrating that our approach significantly improves detection rates, particularly for Minor Attacks, achieving a remarkable 92.33% detection rate for U2R and 91.33% for R2L attacks. This represents a substantial 10% enhancement over existing methods, outperforming Multi-Layer Perceptron (MLP) by 13.23%, Random Forest (RF) by 10.11%, J48- Decision Tree (DT) by 9.23%, and Naïve Bayes (NB) by 9.42% and 8.17% in terms of detection rates. These results underscore the effectiveness of our approach in enhancing intrusion detection performance.

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Authors and Affiliations

  1. Department of Computer Science and Engineering (AI&ML), Institute of Aeronautical Engineering, Hyderabad, Telangana, 500043, India

    Sridhar Patthi

  2. Computer Science and Engineering Department, SRM University, Delhi-NCR, Sonipat, Haryana, India

    Sugandha Singh

  3. Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Hyderabad, Telangana, 501401, India

    Ila Chandana Kumari P

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  1. Sridhar Patthi
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  3. Ila Chandana Kumari P
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Patthi, S., Singh, S. & P, I.C.K. 2-layer classification model with correlated common feature selection for intrusion detection system in networks. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-023-17781-w

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