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

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.

A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things

“…Figure 5b shows that most related studies were published in IEEE Xplore ® digital library (14), followed by Google Scholar (13) and Springer Link (12), while Scopus and Science Direct returned the same number of published studies (5). Furthermore, it is worth mentioning that there are many studies published by other publishers, such as MDPI and Wiley, but rather than directly extracting them from the publishers' databases, we obtained those studies via the Google Scholar database.…”

“…Molina et al [5] introduced a DNN approach for detecting CID attacks in RPL networks. They used the Cooja simulator to implement CID attacks and three types of topological structures with a different number of normal and malicious nodes to build a dataset.…”

“…Consequently, we can reinvent ourselves while simultaneously making the world and our lives smarter with the help of IoT. Moreover, the benefits of IoT are nearly limitless, and its applications are transforming how we work and live by exchanging time and assets and opening up new prospects for growth and development [5]. Moreover, a recent forecast from International Data Corporation (IDC), a well-known provider of industry intelligence, predicts that by 2025, there will be around 41.6 billion connected IoT devices/things (a combination of sensors, machines, cameras, etc.…”

A Review on the Security of IoT Networks: From Network Layer’s Perspective

“…The authors in [83] discussed the inability of conventional techniques to handle the novel attacks like clone ID in RPL network for IoT devices. A framework based on AI is proposed to cater to such kind of attacks.…”

Machine Learning and Fuzzy Logic in Electronics: Applying Intelligence in Practice

“…Morales-Molina et al propose an approach for IoT cyber-attack detection and protection framework driven by artificial intelligence methods [28]. Unsupervised techniques are utilized for the selection of the main characteristics of low power and Lossy Network and Dense Neural Network algorithm for precisely deciding a classification task.…”