Araçsal Ağ Mimarisi İçin Makine Öğrenmesi Tabanlı Saldırı Tespit Sistemi Üzerine Kapsamlı Bir Araştırma

Abstract

Günümüzde iletişim teknolojilerinin gelişmesi birçok farklı alanda değişimlere neden olmaktadır. Bu alanlardan biri de VANET (Araç Alan Ağı) uygulama alanıdır. VANET alanında kullanım alanlarının artmasıyla birlikte VANET ağ güvenliğinin sağlanması daha kritik hale gelmiştir. VANET ağlarına yapılan saldırıları tespit etmek için birçok farklı sistem geliştirilmiştir. Makine öğrenimi tabanlı sistemler, bu saldırı tespit sistemlerinin (STS- Intrusion Detection Systems IDS) geliştirilmesinde en yaygın kullanılan yöntemlerden biridir. Bu makalede literatürde son dönemde yapılan makine öğrenmesi tabanlı VANET IDS üzerine araştırmalar yapılmıştır. Öncelikle VANET mimarisi ve güvenlik gereksinimleri sunulmuş, ardından kapsamlı bir literatür özeti verilmiş ve farklı parametreler üzerinden karşılaştırmalar yapılmıştır. Sonuç olarak, saldırı tespit sistemlerinde birçok farklı makine öğrenmesi modelinin kullanıldığı ve yüksek performanslı tespit gerçekleştirdiği tespit edilmiştir. STS'nin performansında kullanılan makine öğrenmesi algoritmasının yanı sıra birçok farklı parametrenin de performansta kritik rol oynadığı gösterilmiştir. Makale, literatür karşılaştırması (makine öğrenme modeli, simülasyon araçları, veri seti, makine öğrenme algoritması ve performans kriterleri gibi kriterler dikkate alınarak) sayesinde saldırı tespit sistemlerinin performansını artıracak kazanımlarla bu alanda yapılacak yeni çalışmalara rehberlik etmeyi amaçlamaktadır.

Keywords

• IDS
• Machine Learning
• Security
• Vehicular networks

A Comprehensive Survey On Machine Learning-Based Intrusion Detection System for Vehicular Area Network Architectures

Abstract

Today, the development of communication technologies causes changes in many different areas. One of these areas is VANET (Vehicular Area Network) application area. With the increase in usage areas in the VANET field, ensuring VANET network security has become more critical. Many different systems have been developed to detect attacks on VANET networks. Machine learning-based systems are one of the most widely used methods in developing these intrusion detection systems (IDS). In this article, research on machine learning-based VANET IDS, which has been done recently in the literature, has been carried out. First, VANET architecture and security requirements are presented, then a comprehensive literature summary is given, and comparisons are made on different parameters. As a result, it has been determined that many different machine learning models are used in IDSs and perform high-performance detection. In addition to the machine learning algorithm used in the performance of IDSs, it has been shown that many different parameters play a critical role in the performance. The paper aims to guide new studies in this field with the gains that will increase the performance of intrusion detection systems because of the literature comparison (considering criteria such as machine learning model, simulation tools, dataset, machine learning algorithm, and performance criteria).

Keywords

• IDS
• Machine learning
• Security
• Vehicular networks

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