Biblio

Electromagnetic emissions associated with the transmission of automotive controller area network (CAN) messages within a passenger car have been analysed and used to reconstruct the original CAN messages. Concurrent monitoring of the CAN traffic via a wired connection to the vehicle OBD-II port was used to validate the effectiveness of the reconstruction process. These results confirm the feasibility of reconstructing in-vehicle network data for forensic purposes, without the need for wired access, at distances of up to 1 m from the vehicle by using magnetic field measurements, and up to 3 m using electric field measurements. This capability has applications in the identification and resolution of EMI issues in vehicle data network, as well as possible implications for automotive cybersecurity.

In line with the rapid development of the Internet of Things (IoT) network, the challenges faced are ensuring the network performance is capable to support the communication of these IoT devices. As a result, the routing protocols can provide fast route discovery and network maintenance by considering the IoT network's resource constraints. This paper's main contributions are to identify compatible IoT routing protocol using qualitative method and factor that affect network performance. Routing Protocol for Low Power and Lossy Networks (RPL) is a proactive distance- vector routing protocol designed as a proposed standard to meet the requirements of the Low Power and Lossy Networks (LLN). In this project, four influential factors on the performance of RPL in Contiki OS are examined using the Cooja simulator and then RPL performance is assessed in terms of Packet Delivery Ratio (PDR), Energy consumption and Overhead control message for the network. The project provides an insight into the implications of traffic patterns, transmission ranges, network size and node mobility for different scenarios. The results of the simulation show that the PDR and overhead ratio increases proportional to transmission distances increases but decreases while radio interference is increased. From the mobility aspect, PDR decreases by an average of 19.5% when the mobility nodes expand. On the other hand, energy consumption increases by an average of 63.7% and control message size increased up to 213% when the network consists of 40 percent of mobility nodes.

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

Intentional electromagnetic interference (IEMI) of information and communication devices is based on high-power electromagnetic environments far exceeding the device immunity to electromagnetic interference. IEMI dramatically alters the electromagnetic environment throughout the device by interfering with the electromagnetic waves inside the device and destroying low-tolerance integrated circuits (ICs) and other elements, thereby reducing the availability of the device. In contrast, in this study, by using a hardware Trojan (HT) that is quickly mountable by physically accessing the devices, to locally weaken the immunity of devices, and then irradiating electromagnetic waves of a specific frequency, only the attack targets are intentionally altered electromagnetically. Therefore, we propose a method that uses these electromagnetic changes to rewrite or generate data and commands handled within devices. Specifically, targeting serial communication systems used inside and outside the devices, the installation of an HT on the communication channel weakens local immunity. This shows that it is possible to generate an electrical signal representing arbitrary data on the communication channel by applying electromagnetic waves of sufficiently small output compared with the conventional IEMI and letting the IC process the data. In addition, we explore methods for countering such attacks.

Hardware security is a critical concern in design and fabrication of integrated circuits (ICs). Contemporary hardware threats comprise tens of advance invasive and non-invasive attacks for compromising security of modern ICs. Numerous attack-specific countermeasures against the individual threats have been proposed, trading power, area, speed, and design complexity of a system for security. These typical overheads combined with strict performance requirements in advanced technology nodes and high complexity of modern ICs often make the codesign of multiple countermeasures impractical. In this paper, on-chip distribution networks are exploited for detecting those hardware security threats that require non-invasive, yet physical interaction with an operating device-under-attack (e.g., measuring equipment for collecting sensitive information in side-channel attacks). With the proposed approach, the effect of the malicious physical interference with the device-under-attack is captured in the form of on-chip voltage variations and utilized for detecting malicious activity in the compromised device. A machine learning (ML) security IC is trained to predict system security based on sensed variations of signals within on-chip distribution networks. The trained ML ICs are distributed on-chip, yielding a robust and high-confidence security network on-chip. To halt an active attack, a variety of desired counteractions can be executed in a cost-effective manner upon the attack detection. The applicability and effectiveness of these security networks is demonstrated in this paper with respect to power, timing, and electromagnetic analysis attacks.

Embedded and cyber-physical systems are critically dependent on the integrity of input and output signals for proper operation. Input signals acquired from sensors are assumed to correspond to the phenomenon the system is monitoring and responding to. Similarly, when such systems issue an actuation signal it is expected that the mechanism being controlled will respond in a predictable manner. Recent work has shown that sensors can be manipulated through the use of intentional electromagnetic interference (IEMI). In this work, we demonstrate thatboth input and output signals, analog and digital, can be remotely manipulated via the physical layer—thus bypassing traditional integrity mechanisms. Through the use of specially crafted IEMI it is shown that the physical layer signaling used for sensor input to, and digital communications between, embedded systems may be undermined to an attacker's advantage. Three attack scenarios are analyzed and their efficacy demonstrated. In the first scenario the analog sensing channel is manipulated to produce arbitrary sensor readings, while in the second it is shown that an attacker may induce bit flips in serial communications. Finally, a commonly used actuation signal is shown to be vulnerable to IEMI. The attacks are effective over appreciable distances and at low power.

Conducted emission of motors is a domain of interest for EMC as it may introduce disturbances in the system in which they are integrated. Nevertheless few publications deal with the susceptibility of motors, and especially, servomotors despite this devices are more and more used in automated production lines as well as for robotics. Recent papers have been released devoted to the possibility of compromising such systems by cyber-attacks. One could imagine the use of smart intentional electromagnetic interference to modify their behavior or damage them leading in the modification of the industrial process. This paper aims to identify the disturbances that may affect the behavior of a Commercial Off-The-Shelf servomotor when exposed to an electromagnetic field and the criticality of the effects with regards to its application. Experiments have shown that a train of radio frequency pulses may induce an erroneous reading of the position value of the servomotor and modify in an unpredictable way the movement of the motor's axis.

The electric network frequency (ENF) signal can be captured in multimedia recordings due to electromagnetic influences from the power grid at the time of recording. Recent work has exploited the ENF signals for forensic applications, such as authenticating and detecting forgery of ENF-containing multimedia signals, and inferring their time and location of creation. In this paper, we explore a new potential of ENF signals for automatic synchronization of audio and video. The ENF signal as a time-varying random process can be used as a timing fingerprint of multimedia signals. Synchronization of audio and video recordings can be achieved by aligning their embedded ENF signals. We demonstrate the proposed scheme with two applications: multi-view video synchronization and synchronization of historical audio recordings. The experimental results show the ENF based synchronization approach is effective, and has the potential to solve problems that are intractable by other existing methods.