Abstract
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.
Highlights
The topologies contained in each data set D conformed to Internet of Things (IoT) network packets, which can vary from different origins, devices, latency, and changing behaviours, that depend directly on the environment that is being observed
{cloneid_20n, cloneid_50n, cloneid_100n} ∈ D were split into a training subset Xtrain built with 80% of random samples from each data set contained in D, a validation set Xval with
With the extensive use of IoT technology in critical infrastructures, it is crucial to address an in-depth security approach, where a detective layer must be put in place
Summary
Carlos D. Morales-Molina 1 , Aldo Hernandez-Suarez 1, * , Gabriel Sanchez-Perez 1 , Linda K. Toscano-Medina 1 , Hector Perez-Meana 1 , Jesus Olivares-Mercado 1 , Jose Portillo-Portillo 1 , Victor Sanchez 2 and Luis Javier Garcia-Villalba 3 Citation: Morales-Molina, C.D.; Hernandez-Suarez, A.; Sanchez-Perez, G.; Toscano-Medina, L.K.; Perez-Meana, H.; Olivares-Mercado, J.; Portillo-Portillo, J.; Sanchez, V.; Garcia-Villalba, L.J. A Dense Neural Clone ID Attacks on the RPL Protocol of the IoT. Sensors 2021, 21, 3173. Group of Analysis, Security and Systems (GASS), Department of Software Engineering and Artificial
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