The Library
A dense neural network approach for detecting clone ID attacks on the RPL protocol of the IoT
Tools
Morales-Molina, Carlos D., Hernandez-Suarez, Aldo, Sanchez-Perez, Gabriel, Toscano-Medina, Linda K., Perez-Meana, Hector, Olivares-Mercado, Jesus, Portillo-Portillo, Jose, Sanchez Silva, Victor and Garcia-Villalba, Luis Javier (2021) A dense neural network approach for detecting clone ID attacks on the RPL protocol of the IoT. Sensors, 21 (9). e3173. doi:10.3390/s21093173 ISSN 1424-8220.
|
PDF
sensors-21-03173.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1499Kb) | Preview |
Official URL: https://doi.org/10.3390/s21093173
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.
Item Type: | Journal Article | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
|||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||
SWORD Depositor: | Library Publications Router | |||||||||
Library of Congress Subject Headings (LCSH): | Computer networks -- Security measures, Internet of things -- Security measures, Cyberspace -- Security measures, Deep learning (Machine learning), Neural networks (Computer science) | |||||||||
Journal or Publication Title: | Sensors | |||||||||
Publisher: | MDPI | |||||||||
ISSN: | 1424-8220 | |||||||||
Official Date: | 3 May 2021 | |||||||||
Dates: |
|
|||||||||
Volume: | 21 | |||||||||
Number: | 9 | |||||||||
Article Number: | e3173 | |||||||||
DOI: | 10.3390/s21093173 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||
Date of first compliant deposit: | 11 March 2022 | |||||||||
Date of first compliant Open Access: | 11 March 2022 | |||||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year