Saldırı Tespiti İçin Nelder-Mead Algoritması ile Optimize Ağırlıklı Oylama Topluluk Öğrenmesi

Year 2024, , 2139 - 2158, 23.10.2024

Mustafa Burak Ürün , Yusuf Sönmez

https://doi.org/10.29130/dubited.1440640

Abstract

İnternet kullanımı ve veri aktarım hızlarındaki artış çok sayıda anomaliye yol açmıştır. Bu nedenle, anomali tabanlı saldırı tespit sistemleri (IDS), bilinmeyen siber saldırıları, özellikle de imza tabanlı IDS’lerin tespit edemediği sıfırıncı gün saldırılarını belirleme yetenekleri nedeniyle siber güvenlikte çok önemlidir. Bu çalışmada, KNN, XGBoost ve Rastgele Orman temel modelleri ile ağırlıklı bir yumuşak oylama sistemi kullanarak saldırı tespiti için bir topluluk sınıflandırması önerilmektedir. Temel modellerin ağırlıkları, genel topluluk performansını iyileştirmek için Nelder-Mead simpleks yöntemi kullanılarak optimize edilmiştir. Çalışmamızda, Nelder-Mead algoritması ve özellik seçimi kullanılarak optimize edilen yumuşak oylama sınıflandırıcı seviyesi ağırlıklarını kullanan sağlam bir saldırı tespit çerçevesi öneriyoruz. Sistemin performansı KDD99 ve UNSW-NB15 veri setleri kullanılarak değerlendirilmiş ve önerilen yaklaşımın doğruluk açısından mevcut diğer yöntemleri aştığını ve daha az özellik ile karşılaştırılabilir sonuçlar sağladığı tespit edilmiştir. Önerilen sistem ve hiperparametre optimizasyon tekniği, göreceli etkinlik ve verimliliğini belirlemek için diğer siber tehdit tespit ve sınırlama sistemleriyle karşılaştırılmıştır.

Keywords

Saldırı Tespit Sistemleri, Topluluk Öğrenme, Yumuşak Oylama, Hiperparametre Optimizasyonu, Nelder-Mead Algoritması

Nelder-Mead Optimized Weighted Voting Ensemble Learning for Network Intrusion Detection

Abstract

The rise in internet usage and data transfer rates has led to numerous anomalies. Hence, anomaly-based intrusion detection systems (IDS) are essential in cybersecurity because of their ability to identify unknown cyber-attacks, especially zero-day attacks that signature-based IDS cannot detect. This study proposes an ensemble classification for intrusion detection using a weighted soft voting system with KNN, XGBoost, and Random Forest base models. The base model weights are optimized using the Nelder-Mead simplex method to improve the overall ensemble performance. We propose a robust intrusion detection framework that uses soft-voting classifier-level weights optimized using the Nelder-Mead algorithm and feature selection. We evaluated the system's performance using the KDD99 and UNSW-NB15 datasets, which demonstrated that the proposed approach exceeded other existing methods in respect of accuracy and provided comparable results with fewer features. The proposed system and its hyperparameter optimization technique were compared with other cyber threat detection and mitigation systems to determine their relative effectiveness and efficiency.

Keywords

Intrusion Detection Systems, Ensemble Learning, Soft Voting, Hyperparameter Optimization, Nelder-Mead Algorithm

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