IIoT Sensör Verilerinin Güvenliği için Derin Öğrenme ve Ethereum Blok Zinciri'nin İnteraktif Kullanımı

Yıl 2024, , 369 - 384, 29.11.2024

Emrullah Şahin , Naciye Nur Arslan , Fırat Aydemir

https://doi.org/10.35193/bseufbd.1381786

Öz

Endüstriyel Nesnelerin İnterneti (IIoT), birden fazla cihazın ve sensörün bir ağ üzerinden birbirleriyle iletişim kurduğu bir yapıyı ifade eder. İnternetle bağlantılı cihazların sayısı arttıkça, bu cihazlara yönelik saldırıların sayısı da artar. Bu nedenle, fabrikalarda veya işyerlerinde verileri güvence altına almak ve olası tehditlere karşı önlem almak önemli hale gelmiştir. Bu çalışmada, IIoT sensörlerinden toplanan verilerin ağ trafiğine bakılarak saldırı altında olup olmadığını belirlemek için derin öğrenme tabanlı bir mimari kullanıldı. Saldırıya uğramamış veriler Ethereum Blok Zincir ağına kaydedildi. Ethereum blok zincir ağı, sensör verilerinin merkezi bir otoriteye dayanmadan güvenli bir şekilde saklanmasını ve herhangi bir saldırı durumunda veri kaybının önlenmesini sağlamaktadır. Blok zincir ağı üzerinden iletişim süreci sayesinde veri güncelleme ve paylaşımı kolaylaştırıldı. Önerilen derin öğrenme tabanlı saldırı tespit sistemi, normal ve anormal verileri %100 doğrulukla ayırabilmektedir. Anormal verilerinde, hangi saldırı tipine ait oldukları ortalama %95 doğrulukla tanımlandı. Saldırılara maruz kalmayan veriler blok zincir ağında işlendi ve tespit edilen saldırı verileri için uyarı sistemi geliştirildi. Bu çalışma, şirketlerin IIoT sensör verilerinin güvenliğini sağlamak için kullanabileceği bir yöntem sunmaktadır.

Anahtar Kelimeler

Endüstriyel Nesnelerin İnterneti, Blok Zinciri, Siber Güvenlik, Sızma Tespit Sistemi, Derin Öğrenme

Interactive Use of Deep Learning and Ethereum Blockchain for the Security of IIoT Sensor Data

Öz

The Industrial Internet of Things (IIoT) refers to a structure where multiple devices and sensors communicate with each other over a network. As the number of internet-connected devices increases, so does the number of attacks on these devices. Therefore, it has become important to secure the data and prevent potential threats to the data in factories or workplaces. In this study, a deep learning-based architecture was used to determine whether the data collected from IIoT sensors was under attack by looking at network traffic. The data that was not exposed to attacks was stored on the Ethereum Blockchain network. The Ethereum blockchain network ensured that sensor data was stored securely without relying on any central authority and prevented data loss in case of any attack. Thanks to the communication process over the blockchain network, updating and sharing data was facilitated. The proposed deep learning-based intrusion detection system separated normal and anomaly data with 100% accuracy. The anomaly data were identified with an average of 95% accuracy for which attack type they belonged to. The data that was not exposed to attacks was processed on the blockchain network, and an alert system was implemented for the detected attack data. This study presents a method that companies can use to secure IIoT sensor data.

Anahtar Kelimeler

Industrial Internet of Things, Blockchain, Cyber Security, Intrusion Detection System, Deep Learning

Kaynakça

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