An Intrusion Detection Approach based on the Combination of Oversampling and Undersampling Algorithms

Year 2023, Volume: 7 Issue: 1, 125 - 138, 02.01.2024

Ahmet Okan Arık , G. Çiğdem Çavdaroğlu

https://doi.org/10.26650/acin.1222890

Abstract

The threat of network intrusion has become much more severe due to the increasing network flow. Therefore, network intrusion detection is one of the most concerned areas of network security. As demand for cybersecurity assurance increases, the requirement for intrusion detection systems to meet current threats is also growing. However, network-based intrusion detection systems have several shortcomings due to the structure of the systems, the nature of the network data, and uncertainty related to future data. The imbalanced class problem is also crucial since it significantly negatively affects classification performance. Although high performance has been achieved in deep learning-based methodologies in recent years, machine learning techniques may also provide high performance in network intrusion detection. This study suggests a new intrusion detection system called ROGONG-IDS (Robust Gradient Boosting - Intrusion Detection System) which has a unique two-stage resampling model to solve the imbalanced class problem that produces high accuracy on the UNSW-NB15 dataset using machine learning techniques. ROGONGIDS is based on gradient boosting. The system uses Synthetic Minority Over-Sampling Technique (SMOTE) and NearMiss-1 methods to handle the imbalanced class problem. The proposed model's performance on multi-class classification was tested with the UNSW-NB15, and then its robust structure was validated with the NSL-KDD dataset. ROGONG-IDS reached the highest attack detection rate and F1 score in the literature, with a 97.30% detection rate and 97.65% F1 score using the UNSW-NB15 dataset. ROGONG-IDS provides a robust, efficient intrusion detection system for the UNSW-NB15 dataset, which suffered from imbalanced class distribution. The proposed methodology outperforms state-of-the-art and intrusion detection methods.

Keywords

Machine Learning, Cyber Security, Intrusion Detection System, Imbalanced Data, Gradient Boosting

Örneklem Arttırma ve Örneklem Azaltma Algoritmalarının Kombinasyonuna Dayalı Bir Saldırı Tespit Yaklaşımı

Abstract

Artan ağ akışı nedeniyle ağa izinsiz giriş tehdidi çok daha şiddetli hale gelmiştir. Bu nedenle, ağ güvenliğinde en çok endişe duyulan alanlardan biri ağ saldırı tespitidir. Siber güvenlik güvencesine olan talep arttıkça mevcut tehditleri karşılamak için saldırı tespit sistemlerine olan gereksinim de artmaktadır. Bununla birlikte, ağ tabanlı saldırı tespit sistemlerinin, sistemlerin yapısı, ağ verilerinin doğası ve gelecekteki verilerle ilgili belirsizlik nedeniyle bazı eksiklikleri vardır. Dengesiz veri problemi de sınıflandırma performansını kötü etkilediği için çok önemlidir. Son yıllarda derin öğrenme tabanlı metodolojilerde yüksek performans elde edilmesine rağmen, makine öğrenme teknikleri de ağ saldırı tespitinde yüksek performans sağlayabilir. Bu çalışma, makine öğrenme tekniklerini kullanarak UNSW-NB15 veri setinde yüksek doğruluk üreten dengesiz sınıf problemini çözmek için benzersiz bir iki aşamalı yeniden örnekleme modeline sahip olan ROGONG-IDS (Robust Gradient Boosting - Saldırı Tespit sistemi) adlı yeni bir saldırı tespit sistemi önermektedir. ROGONG-IDS, gradyan artırmaya dayalıdır. Sistem, dengesiz sınıf problemini çözmek için Sentetik Azınlık Aşırı Örnekleme Tekniği (SMOTE) ve NearMiss-1 yöntemlerini kullanır. Önerilen modelin çok sınıflı sınıflandırma performansı UNSW-NB15 ile test edilmiş, güçlü yapısı NSL-KDD veri seti ile doğrulanmıştır. ROGONG-IDS, UNSW-NB15 veri setini kullanarak %97,30 tespit oranı ve %97,65 F1 skoru ile literatürdeki en yüksek saldırı tespit oranı ve F1 skoruna ulaşmıştır. ROGONG-IDS, dengesiz sınıf dağılımından muzdarip UNSW-NB15 veri kümesi için sağlam, verimli bir saldırı tespit sistemi sağlamaktadır. Önerilen metodoloji, literatürdeki en gelişmiş saldırı tespit metotlarından daha iyi performans göstermektedir.

Keywords

Makine Öğrenmesi, Siber Güvenlik, Saldırı Tespit Sistemi, Dengesiz Veri, Gradyan Artırma

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