Battery draining attacks against edge computing nodes in IoT networks

Many IoT devices, especially those deployed at the network edge have limited power resources. In this work, we study the effects of a variety of battery draining attacks against edge nodes. Specifically, we implemented hello flooding, packet flooding, selective forwarding, rank attack, and versioning attack in ContikiOS and simulated them in the Cooja simulator. We consider a number of relevant metrics, such as CPU time, low power mode time, TX/RX time, and battery consumption. Besides, we test the stretch attack with three different batteries as an extreme scenario. Our results show that versioning attack is the most severe in terms of draining the power resources of the network, followed by packet flooding and hello flooding attacks. Furthermore, we find that selective forwarding and rank attacks are not able to considerably increase the power resource usage in our scenarios. By quantifying the effects of these attacks, we demonstrate that under specific scenarios, versioning attack can be three to four times as effective as packet flooding and hello flooding attacks in wasting network resources. At the same time, packet flooding is generally comparable to hello flooding in CPU and TX time usage increase but twice as powerful in draining device batteries

Battery Drain Denial-of-Service Attacks and Defenses in the Internet of Things

IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) is a popular routing protocol used in wireless sensor networks and in the Internet of Things (IoT). RPL was standardized by the IETF in 2012 and has been designed for devices with limited resources and capabilities. Open-source RPL implementations are supported by popular IoT operating systems (OS), such as ContikiOS and TinyOS. In this work, we investigate the possibility of battery drain Denial-of-Service (DoS) attacks in the RPL implementation of ContikiOS. In particular, we use the popular Cooja simulator and implement two types of DoS attacks, particularly version number modification and “Hello” flooding. We demonstrate the impact of these attacks on the power consumption of IoT devices. Finally, we discuss potential defenses relying on distributed intrusion detection modules

An Intrusion Detection System for RPL-Based IoT Networks

The Internet of Things (IoT) has become very popular during the last decade by providing new solutions to modern industry and to entire societies. At the same time, the rise of the industrial Internet of Things (IIoT) has provided various benefits by linking infrastructure around the world via sensors, machine learning, and data analytics. However, the security of IoT devices has been proven to be a major concern. Almost a decade ago, the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) was designed to handle routing in IoT and IIoT. Since then, numerous types of attacks on RPL have been published. In this paper, a novel intrusion detection system (IDS) is designed and implemented for RPL-based IoT. The objective is to perform an accurate and efficient detection of various types of routing and denial-of-service (DoS) attacks such as version number attack, blackhole attack, and grayhole attack, and different variations of flooding attacks such as Hello flood attack, DIS attack, and DAO insider attack. To achieve this, different detection strategies are combined, taking advantage of the strengths of each individual strategy. In addition, the proposed IDS is experimentally evaluated by performing a deep analysis of the aforementioned attacks in order to study the impact caused. This evaluation also estimates the accuracy and effectiveness of the IDS performance when confronted with the considered attacks. The obtained results show high detection accuracy. Furthermore, the overhead introduced in terms of CPU usage and power consumption is negligible. In particular, the CPU usage overhead is less than 2% in all cases, whereas the average power consumption increase is no more than 0.5%, which can be considered an insignificant impact on the overall resource utilisation