A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT

Sensors

Victor Sanchez¹  LuisJavier Garcia-Villalba²  Gabriel Sanchez-Perez³  LindaK. Toscano-Medina³  Jesus Olivares-Mercado³  Aldo Hernandez-Suarez³  Jose Portillo-Portillo³  Hector Perez-Meana³  CarlosD. Morales-Molina³ 

[1] Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK;Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial Intelligence (DISIA), Faculty of Computer Science and Engineering, Office 431, Universidad Complutense de Madrid (UCM), Calle Profesor José García Santesmases, 9, Ciudad Universitaria, 28040 Madrid, Spain;Instituto Politecnico Nacional, ESIME Culhuacan, Mexico City 04440, Mexico;

关键词: Clone ID attack;    deep learning;    Internet of Things;    IoT;    intrusion detection;    IDS;   

DOI  :  10.3390/s21093173

来源: DOAJ

【 摘 要 】

At present, new data sharing technologies, such as those used in the Internet of Things (IoT) paradigm, are being extensively adopted. For this reason, intelligent security controls have become imperative. According to good practices and security information standards, particularly those regarding security in depth, several defensive layers are required to protect information assets. Within the context of IoT cyber-attacks, it is fundamental to continuously adapt new detection mechanisms for growing IoT threats, specifically for those becoming more sophisticated within mesh networks, such as identity theft and cloning. Therefore, current applications, such as Intrusion Detection Systems (IDS), Intrusion Prevention Systems (IPS), and Security Information and Event Management Systems (SIEM), are becoming inadequate for accurately handling novel security incidents, due to their signature-based detection procedures using the matching and flagging of anomalous patterns. This project focuses on a seldom-investigated identity attack—the Clone ID attack—directed at the Routing Protocol for Low Power and Lossy Networks (RPL), the underlying technology for most IoT devices. Hence, a robust Artificial Intelligence-based protection framework is proposed, in order to tackle major identity impersonation attacks, which classical applications are prone to misidentifying. On this basis, unsupervised pre-training techniques are employed to select key characteristics from RPL network samples. Then, a Dense Neural Network (DNN) is trained to maximize deep feature engineering, with the aim of improving classification results to protect against malicious counterfeiting attempts.

【 授权许可】

Unknown