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

AMAROUCHE, SIHAM; KÜÇÜK, KEREM

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

SIHAM AMAROUCHE, (Kocaeli Üniversitesi, Bilgisayar Mühendisliği Bölümü, Kocaeli, Türkiye)

Kerem KÜÇÜK (Kocaeli Üniversitesi, Yazılım Mühendisliği Bölümü, Kocaeli, Türkiye)

Journal of Naval Sciences and Engineering

8 3

Yıl: 2022 Cilt: 18 Sayı: 2 Sayfa Aralığı: 333 - 361 Metin Dili: İngilizce İndeks Tarihi: 08-12-2022

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

Öz:

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.

Anahtar Kelime:

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

Öz:

Günümüz teknoloji dünyasında, Nesnelerin İnterneti (IoT) sistemleri için izinsiz giriş tespiti önemli bir konudur. IoT'de kablosuz ağlara bağlı küçük cihazların kullanımının artmasıyla birlikte veri miktarı ihtiyacı da hızla artmaktadır. Bu veriler saldırılara karşı savunmasız olabilmektedir. Bu nedenle IoT sistem çözümlerinin gizliliğini, kullanılabilirliğini ve güvenilirliğini sağlamak için bu verilerin güvenceye alınması gereklidir. Yapay zekânın (AI) otonom biçimde kullanarak saldırıların tespit edilmesi, ağ saldırı tespit sistemlerinde (NIDS) daha uygun bir yöntem haline gelmiştir. Bu çalışmada, bu tespit sistemlerinin performansını iyileştirmek ve doğruluğu artırmak için yeni tespit tekniği önerilmektedir. IoT sistemleri için farklı saldırı türlerini tespit etmek için farklı makine öğrenimi (ML) ve derin öğrenme (DL) yöntemleri birlikte sunulmaktadır. Ayrıca, IoT sistem ortamındaki anomaliyi tanımlamanın en iyi yolunu bulmak için deneyler sunulmaktadır. Bununla birlikte farklı AI modelleri arasında karşılaştırmalar yapılmaktadır. Deneyler için, UNSW-NB15 veri seti kullanılmıştır.

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

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APA	AMAROUCHE S, KÜÇÜK K (2022). MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. , 333 - 361.
Chicago	AMAROUCHE SIHAM,KÜÇÜK KEREM MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. (2022): 333 - 361.
MLA	AMAROUCHE SIHAM,KÜÇÜK KEREM MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. , 2022, ss.333 - 361.
AMA	AMAROUCHE S,KÜÇÜK K MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. . 2022; 333 - 361.
Vancouver	AMAROUCHE S,KÜÇÜK K MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. . 2022; 333 - 361.
IEEE	AMAROUCHE S,KÜÇÜK K "MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS." , ss.333 - 361, 2022.
ISNAD	AMAROUCHE, SIHAM - KÜÇÜK, KEREM. "MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS". (2022), 333-361.

APA	AMAROUCHE S, KÜÇÜK K (2022). MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. Journal of Naval Sciences and Engineering, 18(2), 333 - 361.
Chicago	AMAROUCHE SIHAM,KÜÇÜK KEREM MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. Journal of Naval Sciences and Engineering 18, no.2 (2022): 333 - 361.
MLA	AMAROUCHE SIHAM,KÜÇÜK KEREM MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. Journal of Naval Sciences and Engineering, vol.18, no.2, 2022, ss.333 - 361.
AMA	AMAROUCHE S,KÜÇÜK K MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. Journal of Naval Sciences and Engineering. 2022; 18(2): 333 - 361.
Vancouver	AMAROUCHE S,KÜÇÜK K MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS. Journal of Naval Sciences and Engineering. 2022; 18(2): 333 - 361.
IEEE	AMAROUCHE S,KÜÇÜK K "MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS." Journal of Naval Sciences and Engineering, 18, ss.333 - 361, 2022.
ISNAD	AMAROUCHE, SIHAM - KÜÇÜK, KEREM. "MACHINE AND DEEP LEARNING-BASED INTRUSION DETECTION AND COMPARISON IN INTERNET OF THINGS". Journal of Naval Sciences and Engineering 18/2 (2022), 333-361.