Sistem Çağrısı Verilerinde Derin Öğrenme Mimarileri Kullanılarak Anomali Tespitinin Değerlendirilmesi: Performans ve Enerji Verimliliği

Abstract

Sistem çağrıları, işletim sistemi ile yazılım arasındaki etkileşimleri temsil eden kritik bir veri kaynağı görevi görmektedir. Bir veri kaynağındaki olağan dışı kalıpların belirlenmesi, sistem performansını iyileştirmek açısından önemli olan anomali tespiti olarak adlandırılmaktadır. Özellikle, derin öğrenme tabanlı yaklaşımlar sistem çağrılarında anomali tespitinin doğruluğunu artırırken, kullanılan derin öğrenme modellerinin enerji verimliliği göz ardı edilemez bir değerlendirme ölçütü olarak öne çıkmaktadır. Bu çalışmada, üç derin öğrenme modelinin (Tekrarlayan Sinir Ağı (RNN), Kapılı Tekrarlayan Birim (GRU) ve Uzun Kısa Süreli Bellek (LSTM)) sistem çağrısı verilerinde anomali tespiti için performansı ve enerji verimliliği karşılaştırılmıştır. Her model, Asgari-Azami Normalleştirmesi (Min-Max Normalization) ile ölçeklendirilmiş verilerle eğitilmiştir. Model performansı, Determinasyon Katsayısı (R²), Ortalama Kare Hatası (MSE) ve Ortalama Mutlak Hata (MAE) değerlendirme metrikleri kullanılarak ölçülmüştür. Enerji tüketimi tahmini ise Intel Power Gadget (IPG) ile gerçekleştirilmiştir. Bu karşılaştırmalı analiz, test edilen mimarilerin göreceli etkinliği hakkında ampirik içgörüler sunmaktadır. Bulgular, LSTM'nin anomali tespiti açısından daha iyi bir performans sergilediğini, ancak enerji verimliliği açısından RNN’nin daha avantajlı olduğunu göstermektedir.

Keywords

• Sistem çağrısı
• Anomali tespiti
• Enerji verimliliği
• Derin öğrenme

Evaluation of Anomaly Detection in System Call Data Using Deep Learning Architectures: Performance and Energy Efficiency

Abstract

Abstract: System calls serve as a critical data source that represents the interactions between the operating system and software. The identification of unusual patterns within a data source is referred to as anomaly detection, which is essential for improving system performance. Particularly, deep learning-based approaches enhance the accuracy of anomaly detection in system calls, while the energy efficiency of the employed deep learning models emerges as a crucial evaluation criterion that cannot be overlooked. In this study, the performance and energy efficiency of three deep learning models—Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM)—were compared for anomaly detection in system call data. Each model was trained using data scaled with Min-Max Normalization. Model performance was measured using the evaluation metrics Coefficient of Determination (R²), Mean Squared Error (MSE), and Mean Absolute Error (MAE). Energy consumption estimation was conducted using Intel Power Gadget (IPG). This comparative analysis provides empirical insights into the relative effectiveness of the tested architectures. The findings show that LSTM achieves better performance in anomaly detection, while RNN is more advantageous in terms of energy efficiency.

Keywords

• System call
• Anomaly detection
• Energy efficiency
• Deep learning

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