In-Vehicle Data Communication with CAN &Security Monitoring: A Review

Automobiles are now being created with more electronic components for efficient functioning such as Anti Lock Braking system, Adaptive Cruise Control, Traction control system, Airbag, Power Steering etc. managed by networked controllers that include hundreds of ECUs (electronic control units) that can coordinate, control, and monitor loads of internal vehicle components. Each component, such as ABS, TCS (Traction control system), tire pressure monitoring system and telematics system, may communicate with nearby components over the CAN (Controller Area Network) bus, establishing an in-vehicle communication network. These modern automobile system networks intended for safety with minimal consideration for security have drawn the attention of researchers for providing security in CAN. The Paper reviews the behavior and vulnerabilities of CAN within an in-vehicle network including various attacks possible in CAN along with the proposed solutions in the literature with extensive survey on a security promising approach named as IDS (Intrusion detection system)

A hierarchical detection method in external communication for self-driving vehicles based on TDMA

Security is considered a major challenge for self-driving and semi self-driving vehicles. These vehicles depend heavily on communications to predict and sense their external environment used in their motion. They use a type of ad hoc network termed Vehicular ad hoc networks (VANETs). Unfortunately, VANETs are potentially exposed to many attacks on network and application level. This paper, proposes a new intrusion detection system to protect the communication system of self-driving cars; utilising a combination of hierarchical models based on clusters and log parameters. This security system is designed to detect Sybil and Wormhole attacks in highway usage scenarios. It is based on clusters, utilising Time Division Multiple Access (TDMA) to overcome some of the obstacles of VANETs such as high density, high mobility and bandwidth limitations in exchanging messages. This makes the security system more efficient, accurate and capable of real time detection and quick in identification of malicious behaviour in VANETs. In this scheme, each vehicle log calculates and stores different parameter values after receiving the cooperative awareness messages from nearby vehicles. The vehicles exchange their log data and determine the difference between the parameters, which is utilised to detect Sybil attacks and Wormhole attacks. In order to realize efficient and effective intrusion detection system, we use the well-known network simulator (ns-2) to verify the performance of the security system. Simulation results indicate that the security system can achieve high detection rates and effectively detect anomalies with low rate of false alarms

A robust, reliable and deployable framework for In-vehicle security

Cyber attacks on financial and government institutions, critical infrastructure, voting systems, businesses, modern vehicles, etc., are on the rise. Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. This is due to the fact that the protocols used for in-vehicle communication i.e. controller area network (CAN), FlexRay, local interconnect network (LIN), etc., lack basic security features such as message authentication, which makes it vulnerable to a wide range of attacks including spoofing attacks. This research presents methods to protect the vehicle against spoofing attacks. The proposed methods exploit uniqueness in the electronic control unit electronic control unit (ECU) and the physical channel between transmitting and destination nodes for linking the received packet to the source. Impurities in the digital device, physical channel, imperfections in design, material, and length of the channel contribute to the uniqueness of artifacts. I propose novel techniques for electronic control unit (ECU) identification in this research to address security vulnerabilities of the in-vehicle communication. The reliable ECU identification has the potential to prevent spoofing attacks launched over the CAN due to the inconsideration of the message authentication. In this regard, my techniques models the ECU-specific random distortion caused by the imperfections in digital-to-analog converter digital to analog converter (DAC), and semiconductor impurities in the transmitting ECU for fingerprinting. I also model the channel-specific random distortion, impurities in the physical channel, imperfections in design, material, and length of the channel are contributing factors behind physically unclonable artifacts. The lumped element model is used to characterize channel-specific distortions. This research exploits the distortion of the device (ECU) and distortion due to the channel to identify the transmitter and hence authenticate the transmitter.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154568/1/Azeem Hafeez Final Disseration.pdfDescription of Azeem Hafeez Final Disseration.pdf : Dissertatio

AI-based intrusion detection systems for in-vehicle networks: a survey.

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyber attacks. Various Intrusion Detection Systems (IDSs) have been developed to detect these attacks. However, the high generalization capabilities of Artificial Intelligence (AI) make AI-based IDS an excellent countermeasure against automotive cyber attacks. This article surveys AI-based in-vehicle IDS from 2016 to 2022 (August) with a novel taxonomy. It reviews the detection techniques, attack types, features, and benchmark datasets. Furthermore, the article discusses the security of AI models, necessary steps to develop AI-based IDSs in the CAN bus, identifies the limitations of existing proposals, and gives recommendations for future research directions

GPS Anomaly Detection And Machine Learning Models For Precise Unmanned Aerial Systems

The rapid development and deployment of 5G/6G networks have brought numerous benefits such as faster speeds, enhanced capacity, improved reliability, lower latency, greater network efficiency, and enablement of new applications. Emerging applications of 5G impacting billions of devices and embedded electronics also pose cyber security vulnerabilities. This thesis focuses on the development of Global Positioning Systems (GPS) Based Anomaly Detection and corresponding algorithms for Unmanned Aerial Systems (UAS). Chapter 1 provides an overview of the thesis background and its objectives. Chapter 2 presents an overview of the 5G architectures, their advantages, and potential cyber threat types. Chapter 3 addresses the issue of GPS dropouts by taking the use case of the Dallas-Fort Worth (DFW) airport. By analyzing data from surveillance drones in the (DFW) area, its message frequency, and statistics on time differences between GPS messages were examined. Chapter 4 focuses on modeling and detecting false data injection (FDI) on GPS. Specifically, three scenarios, including Gaussian noise injection, data duplication, data manipulation are modeled. Further, multiple detection schemes that are Clustering-based and reinforcement learning techniques are deployed and detection accuracy were investigated. Chapter 5 shows the results of Chapters 3 and 4. Overall, this research provides a categorization and possible outlier detection to minimize the GPS interference for UAS enhancing the security and reliability of UAS operations

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Future connected and autonomous vehicles (CAVs) must be secured againstcyberattacks for their everyday functions on the road so that safety of passengersand vehicles can be ensured. This article presents a holistic review of cybersecurityattacks on sensors and threats regardingmulti-modal sensor fusion. A compre-hensive review of cyberattacks on intra-vehicle and inter-vehicle communicationsis presented afterward. Besides the analysis of conventional cybersecurity threatsand countermeasures for CAV systems,a detailed review of modern machinelearning, federated learning, and blockchain approach is also conducted to safe-guard CAVs. Machine learning and data mining-aided intrusion detection systemsand other countermeasures dealing with these challenges are elaborated at theend of the related section. In the last section, research challenges and future direc-tions are identified

An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge from previous experiences facilitate the radio spectrum perception and allow the radio to detect abnormal behaviours caused by jamming attacks. Detecting abnormalities realize a fundamental step towards growing up incrementally the radio\u2019s long-term memory. Deviations from predictions estimated during abnormal situations are used to characterize jammers at multiple levels and discover their dynamic behavioural rules. Besides, a proactive consequence can be drawn after estimating the jammer\u2019s signal to act efficiently by mitigating its effects on the received stimuli. Simulation results show that the introduction of the novel SA functionalities with the proposed HDBN framework provides the high accuracy of characterizing, detecting and predicting the jammer\u2019s activities