CVNNs-IDS: Complex-Valued Neural Network Based In-Vehicle Intrusion Detection System

Autor:	Mu Han, Shidian Ma, Pengzhou Cheng
Rok vydání:	2020
Předmět:	Artificial neural network business.industry Computer science Intrusion detection system computer.software_genre CAN bus Domain (software engineering) Feature (computer vision) The Internet Anomaly detection Data mining business Encoder computer
Zdroj:	Communications in Computer and Information Science ISBN: 9789811591280 SPDE
DOI:	10.1007/978-981-15-9129-7_19
Popis:	The bus of the Controller area Network (CAN) in the vehicle is frequently attacked under the environment of efficient communication. This paper explores ways to hide features of the intrusion detection system (IDS) and obtain a high-precision during an attack on the Internet of vehicle (IoV). To protect the privacy features of the hidden layer with regard to anomaly detection, we proposed the CVNNs-IDS. The system converts the data into an image in real-time using the encoder and then maps it into the complex domain whiles it rotates it to reconstruct the real features to achieve the purpose of system protection. Available researches show that features from random angles are obtained by attackers, making it impossible to distinguish between the real or fake feature. The accuracy of the proposed method CVNNs-IDS is 98%. Results obtained represents that our proposed method performed better than the traditional techniques with regard to performance and security.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::9ca8b6849574d561fbca471a000ad79b https://doi.org/10.1007/978-981-15-9129-7_19 Zobrazit plný text záznamu