# A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
## 1 Introduction  
The Internet of Things (IoT) is a network comprised of smart devices, equipped with embedded sensors, actuators, processors, and transceivers [1], mostly connected to an application or server over the Internet. A recent study [2] mentioned that, by 2020, IoT technology will be available in 95% of the electronic circuitry of newly emerging products, comprising approximately twenty billion smart devices in constant use. Moreover, a report presented by Cisco [3] demonstrated that, due to the rapid evolution and popularity of IoT gadgets and wearables, the amount of fully connected devices around the world may be as high as fifty billion. Due to the interoperability of the IoT paradigm, Wireless SensorNetworks (WSN) have been sketched as a subset of IoT technology, in which each device can employ inherent sensors to work as a mesh network. At present, the applications of IoT and WSNs are predominantly being engaged for industrial needs; as an example, in [4], WSNs were used to monitor power transmission lines, in order to reduce the damage caused by natural disasters and extreme weather conditions, by collecting data from different types of sensors distributed on transmission towers. On the other hand, in [5], cluster-based data aggregation methods were executed in different-sized WSNs, in order to gauge configurations and identify pests in coffee plantations. Furthermore, in [6], a set of WSNs was arranged to measure earth vibrations and to monitor the structural health of constructions. One of the most important (and often critical) form of industrialized WSNs is in aircraft manufacturing: Figure 1 illustrates Aircraft Strength Testing (AST), an auditing procedure that exploits linked sensors to assess structural damage, such as fatigue cracks, part rigidity, corrosion, and so on [7]. By their very nature, IoT and WSNs are likely to crash due to packet transmission loss and low network throughput--constraints that are being progressively amended by standardizing different routing protocols [8], among which the Routing Protocol for Low-Power and Lossy Networks protocol (RPL) stands out, due to its rapid flexibility and efficient routing--characteristics required to deal with different network technologies, smart IP addresses, the enabling of IoT nodes, and quality of service (QoS) support [8; 9]. In view of its noticeable advantages, RPL is becoming the underlying technology for many IoT device manufacturers and vendors in a wide range of application domains; however, it also provides an entry point for security threats that compromise legacy structures that are implicit in RPL networks, such as the Low Power and Lossy Networks (LLNs). Data collection, sharing, transparency, updating, and secure communication enforcing are not just the only inquiries for IoT and WSNs protocols to reach security goals, but also how protection tactics and strategies are being modeled to offer reliable end-to-end solutions. A lack of security controls, mechanisms, and incident monitoring capabilities can expose distinct weaknesses across the network layers of RPL and IoT architectures. Firstly, at the network layer, common vulnerabilities include eavesdropping, packet sniffing, routing attacks, and Denial of Service (DoS); the latter is possible thanks to the high accessibility to smart devices running with default configuration, with hardly any security measures implemented [10]. At the application layer, frequent attacks include phishing, malicious code and object injection, and Cross-Site-Request-Forgery (CSRF), as well as any risk concerning web-based applications. Figure 1: The AST–WSN topology comprises cluster head nodes that forward data to the router nodes, which finally sends the sensed information to an AST Server for storage and analysis. As explained in [11], RPL and LLN security is an extensive subject of study; however, certain breaches remain unaddressed: Routing falsification, Clone ID, and Byzantine intrusion.