Dayanıklı Temel Bileşenler Analizi ile Anomali Tespiti Üzerine Bir Uygulama

Year 2024, Volume: 19 Issue: 1, 107 - 112, 28.03.2024

Kübra Bağcı Genel , H. Eray Çelik

https://doi.org/10.55525/tjst.1293057

Abstract

Dijital sistemlere yönelik artan sayıda tehdit ve saldırılar sebebi ile güvenli bir ağ ortamı sağlamak önemli bir problemdir. Anomali tespiti gibi yöntemlerin uygulanması, herhangi bir anomal trafik hacminin tespit edilmesine yardımcı olabilmektedir. Dayanıklı istatistiksel yöntemler de dahil olmak üzere ağ anomalilerini tespit etmek için çeşitli istatistiksel ve makine öğrenmesi yaklaşımları kullanılmaktadır. Dayanıklı yöntemler, anormal trafik modellerini belirlemeye ve bunları normal trafikten doğru bir şekilde ayırmaya yardımcı iyi bir araçtır. Bu çalışmada, ağ anomalilerini tespit etmek için kemometri ve genetik literatüründe yaygın kullanımıyla bilinen ROBPCA adlı dayanıklı bir Temel Bileşen Analizi (PCA) yöntemi kullanılmış ve PCAGRID adlı başka bir dayanıklı PCA yöntemi ile karşılaştırılmıştır. Bu yöntemlerin anomali tespit performansları, iyi bilinen bir trafik matrisine sentetik trafik hacmi enjekte edilerek değerlendirilmiştir. Uygulama sonuçlarına göre anomali tespitinde ROBPCA yöntemi daha iyi performans sağladığı görülmüştür.

Keywords

Anomali tespiti, aykırı değer, temel bileşenler analizi, dayanıklı istatistik

An Application of Robust Principal Component Analysis Methods for Anomaly Detection

Abstract

Ensuring a secure network environment is crucial, especially with the increasing number of threats and attacks on digital systems. Implementing effective security measures, such as anomaly detection can help detect any abnormal traffic patterns. Several statistical and machine learning approaches are used to detect network anomalies including robust statistical methods. Robust methods can help identify abnormal traffic patterns and distinguish them from normal traffic accurately. In this study, a robust Principal Component Analysis (PCA) method called ROBPCA which is known for its extensive use in the literature of chemometrics and genetics is utilized for detecting network anomalies and compared with another robust PCA method called PCAGRID. The anomaly detection performances of these methods are evaluated by injecting synthetic traffic volume into a well-known traffic matrix. According to the application results, when the normal subspace is contaminated with large anomalies the ROBPCA method provides much better performance in detecting anomalies.

Keywords

Anomaly detection, outlier, principal component analysis, robust statistics

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