MAKİNE VE DERİN ÖĞRENME YÖNTEMLERİ İLE NESNELERİN İNTERNETİ İÇİN SALDIRI TESPİTİNİN KARŞILAŞTIRILMASI

Günümüz teknoloji dünyasında, nesnelerin interneti (IoT) sistemleri için izinsiz giriş tespiti önemli bir konudur. IoT'de kablosuz ağlara bağlı küçük cihazların kullanımının artmasıyla birlikte veri miktarı da hızla artıyor. Bu veriler saldırılara karşı savunmasız olabilir, bu nedenle IoT sistemlerinin sistemin gizliliğini, kullanılabilirliğini ve güvenilirliğini artırmak için bu verileri güvenceye alması gerekir. Yapay zekayı (AI) otonom olarak kullanarak saldırıları tespit etme ilerlemesi, ağ saldırı tespit sistemlerinde (NIDS) daha uygun bir yöntem haline geldi. Bu yazıda, NIDS'de performansı iyileştirmek ve doğruluğu artırmak için yeni tespit tekniği öneriyoruz. IoT sistemleri için farklı saldırı türlerini tespit etmek için farklı makine öğrenimi (ML) ve derin öğrenme (DL) yöntemleri sunuyoruz. Ayrıca, IoT sistem ortamındaki anomaliyi tanımlamanın en iyi yolunu bulmak için deneyler sunuyoruz, farklı AI modelleri arasında karşılaştırmalar yapıyoruz. Deney, açık veri tabanı UNSW-NB15 ile değerlendirilmiştir.

Anahtar Kelimeler:

Derin Öğrenme, IoT Güvenliği, Makine Öğrenmesi, Saldırı Tespiti, Siber Güvenlik.

MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS

In today’s technology world, intrusion detection is important topic for the Internet of Things (IoT) systems. With the growth of using tiny devices connected to wireless networks in IoT, the amount of data is growing rapidly. This data may be vulnerable to attacks so that IoT systems need to secure it for increasing the system’s confidentiality, availability, and reliability. The progress of detecting attacks using artificial intelligence (AI) autonomously has become a more convenient method in network intrusion detection systems (NIDS). In this article, we propose new detecting technique to improve performance and increase accuracy in NIDS. We present different machine learning (ML) and deep learning (DL) methods to detect the different type of attacks for IoT systems. We also provide the experiments to find out the best way to identify the anomaly in IoT system environment, take comparisons between different AI models. The experiment was evaluated with the open database UNSW-NB15.

Keywords:

Deep Learning, IoT Security, Machine Learning, Intrusion Detection, Cyber Security.,

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