Denetleyici Alan Ağının Güvenliğinin Sağlanması için Derin Öğrenme Tabanlı Saldırı Tespit Sistemleri Üzerine Bir Derleme

Year 2021, Issue: 27, 1038 - 1049, 30.11.2021

Zinnet Duygu Akşehir , Sedat Akleylek

https://doi.org/10.31590/ejosat.974582

Abstract

Nesnelerin interneti fikrinin otomotiv alanına girmesi ile birlikte araçların interneti kavramı ortaya çıkmıştır. Araçların interneti hem araç içi ağ iletişimini hem de araçların diğer nesnelerle olan iletişimini kapsamaktadır. Araç içi ağ iletişimi, araç içi çeşitli işlevleri sağlayan Elektronik Kontrol Birimleri arasındaki güvenilir bir iletişimi sağlamakta olup araç içi ağlar arasında en yaygın kullanılanı denetleyici alan ağlarıdır. Denetleyici alan ağı, araç içi ağ için güvenli bir iletişim ortamı sunarken siber saldırılara karşı savunmasızdır. Bu derleme çalışmasında araç içi denetleyici alan ağının güvenliğinin sağlanması için derin öğrenme yöntemini kullanan saldırı tespit sistemleri üzerine odaklanılmıştır. Bu kapsamda veritabanları üzerinde sistematik bir literatür taraması gerçekleştirilerek literatüre yön veren çalışmalar belirlenmiştir. Belirlenen çalışmalar kullanılan yöntem, veri kümesi, seçilen öznitelik ve odaklanılan saldırı bakımından detaylı bir şekilde incelenmiştir. Ayrıca incelenen çalışmalarda önerilen saldırı tespit modelinin performansının nasıl değerlendirildiği ifade edilmekle birlikte önerilen modelin diğer yöntemlerle yapılan karşılaştırmalar detaylandırılmıştır.

Keywords

Nesnelerin interneti (IoT), Araçların interneti (IoV), Araç içi ağ (IVN), Güvenlik, Denetleyici alan ağı (CAN), Saldırı tespit sistemi (IDS), Derin öğrenme

A Review on Deep Learning Based Intrusion Detection Systems for Ensuring Security of Controller Area Network

Abstract

The concept of the Internet of vehicles emerges with the definition of the Internet of things idea into the automotive field. The internet of vehicles covers both in-vehicle network and the communication of vehicles with other things. The in-vehicle network provides reliable communication between Electronic Control Units providing various in-vehicle functions, and the most widely used among in-vehicle networks is the controller area networks. The controller area network is vulnerable to cyber-attacks while providing a secure communication environment for the in-vehicle network. This survey paper focuses on intrusion detection systems that use deep learning to secure the in-vehicle controller area network. In this context, systematic literature research is conducted on scientific/academical databases, and papers are determined that has an effect on the literature. The studies are examined in detail in terms of the used method, dataset, selected attribute, and focused attack. In addition, the previous studies are compared with the others in detail.

Keywords

Internet of things (IoT), Internet of vehicles (IoV), In-vehicle network (IVN), Security, Controller area network (CAN), Intrusion detection system (IDS), Deep learning

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