İlişkilendirme Kuralı Madenciliği Algoritmasını Kullanarak BGP Anomali Tespiti

Year 2022, Issue: 42, 134 - 139, 31.10.2022

Mubarak Altamimi Zafer Albayrak Muhammet Çakmak Ahmet Nusret Özalp

https://doi.org/10.31590/ejosat.1186700

Abstract

Anomali, ağ güvenliğini etkileyen olağan dışı durumun ortaya çıkmasıdır. Bir ağdaki olağandışı durumun algılanması gereksinimi, ağdan anormal akışı algılayan ve kaldıran Anomali tespitidir. Sınır Ağ Geçidi Protokolü (BGP), yönlendirme ve erişilebilirlik bilgilerini paylaşmak için otonom sistemlerle iletişim kurmak için kullanılan en yaygın harici Ağ Geçidi Protokolüdür. Bu protokolün anormal davranışı, yetersiz tedarik, kötü niyetli saldırılar, trafik veya ekipman sorunları ve ağ operatörü hataları gibi çeşitli faktörlerden kaynaklanabilir. BGP güven varsayımı üzerine inşa edilmiştir ve sonuç olarak yıllar içinde birçok kez saldırıya uğramıştır. Code Red I, BGP ağını hedef alan ve işleyişinde anormallikler üreten iyi bilinen bir saldırı tespitinde kullanılan verisetidir. Veriseti içindeki saldırılar türleri, ağ trafiği verilerini kullanarak modelin eğitimi için veri kümesi olarak kullanılmıştır. Bu çalışmanın amacı, bir süre boyunca BGP'de bir anormalliği tetikleyen olayları tespit etmek ve aynı zamanda eğitim veri seti modelini kullanarak bu zaman aralığı boyunca BGP'den bir anormalliği tespit etmektir. İzinsiz Giriş Tespit Sisteminde (IDS) BGP anomali tespiti için gerçek birliktelik kuralı madenciliği sunuyoruz.

Keywords

BGP Anormali Tespiti, Veri madenciliği, Birliktelik Kuralları, Veri kümesi, Code Red I, Apriori Algoritması.

BGP Anomaly Detection Using Association Rule Mining Algorithm

Abstract

An anomaly is the occurrence of an exception that affects network security. The requirement for abnormality detection in a network is Anomaly detection, which detects and removes anomalous flow from the network. The Border Gateway Protocol (BGP) is the most common external Gateway Protocol used to communicate with autonomous systems to share routing and reachability information. This protocol's abnormal behavior may be caused by a variety of factors, including inadequate provisioning, malicious attacks, traffic or equipment issues, and network operator mistakes. BGP was built on the assumption of trust, and as a result, it has been hacked numerous times over the years. Code Red I is one well-known assault that targets BGP networking and produce abnormalities in its operation. These attacks were utilized as the dataset for training the model using network traffic data. The goal of this study is to detect the events that triggered an anomaly in the BGP during a time, as well as to detect an anomaly from the BGP throughout that time interval using the training dataset model. We present real association rule mining for BGP anomaly detection in the Intrusion Detection System (IDS).

Keywords

BGP Anomalies Detection, Datamining, Association Rules, Dataset, Code Red I, Apriori Algorithm

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