Threat Detection for Collaborative Adaptive Cruise Control in Connected Cars

Jagielski, Matthew; Jones, N. W.; Lin, Chung-Wei; Nita-Rotaru, Cristina; Shiraishi, Shinichi

doi:10.1145/3212480.3212492

Cited by 45 publications

(22 citation statements)

References 18 publications

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“…Jagielski et al [12], considering attacks on the cooperative adaptive cruise control (CACC) system, suggested that the attacker's goal is deceiving the AV's radar, LiDAR or both of the sensors simultaneously. Attacks were considered on the basis of motion values, which were negatively affected.…”

Section: Classification Of Attacks On Av Lidarsmentioning

confidence: 99%

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

Meshcheryakov

Iskhakov

Mamchenko

et al. 2022

Sensors

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The paper proposes an approach to assessing the allowed signal-to-noise ratio (SNR) for light detection and ranging (LiDAR) of unmanned autonomous vehicles based on the predetermined probability of false alarms under various intentional and unintentional influencing factors. The focus of this study is on the relevant issue of the safe use of LiDAR data and measurement systems within the “smart city” infrastructure. The research team analyzed and systematized various external impacts on the LiDAR systems, as well as the state-of-the-art approaches to improving their security and resilience. It has been established that the current works on the analysis of external influences on the LiDARs and methods for their mitigation focus mainly on physical (hardware) approaches (proposing most often other types of modulation and optical signal frequencies), and less often software approaches, through the use of additional anomaly detection techniques and data integrity verification systems, as well as improving the efficiency of data filtering in the cloud point. In addition, the sources analyzed in this paper do not offer methodological support for the design of the LiDAR in the very early stages of their creation, taking into account a priori assessment of the allowed SNR threshold and probability of detecting a reflected pulse and the requirements to minimize the probability of “missing” an object when scanning with no a priori assessments of the detection probability characteristics of the LiDAR. The authors propose a synthetic approach as a mathematical tool for designing a resilient LiDAR system. The approach is based on the physics of infrared radiation, the Bayesian theory, and the Neyman–Pearson criterion. It features the use of a predetermined threshold for false alarms, the probability of interference in the analytics, and the characteristics of the LiDAR’s receivers. The result is the analytical solution to the problem of calculating the allowed SNR while stabilizing the level of “false alarms” in terms of background noise caused by a given type of interference. The work presents modelling results for the “false alarm” probability values depending on the selected optimality criterion. The efficiency of the proposed approach has been proven by the simulation results of the received optical power of the LiDAR’s signal based on the calculated SNR threshold and noise values.

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Section: Classification Of Attacks On Av Lidarsmentioning

confidence: 99%

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

Meshcheryakov

Iskhakov

Mamchenko

et al. 2022

Sensors

View full text Add to dashboard Cite

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“…In a platoon, a variety of attack models may exist depending on the attacker location and the attack methods. Some attack models use constant bias injection and variable falsification that changes in sinusoidal fashion [30], [31]. Such models may not be effective because the attack patterns are predictable due to a simplicity or a regular variation of the attack methods.…”

Section: B Attack Modelsmentioning

confidence: 99%

An Approach to Detecting Malicious Information Attacks for Platoon Safety

Son

2021

IEEE Access

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Malicious attacks reduce the benefits of cooperative adaptive cruise control (CACC) such as safety, driving convenience, traffic flow, and fuel efficiency, by destabilizing the stability. To reinforce the resiliency of a CACC based platoon of connected and automated vehicles (CAVs), this work investigates a detection method for malicious information attacks in the platoon. In this work, we propose an attack detection method, called LMID (long short-term memory (LSTM) based malicious information detection). We consider two attack models: correlated attacks and non-correlated attacks. In our attack scenarios, one of the platoon members attacks the platoon using the attack models. Using PLEXE, a well-known platoon simulator, we develop a simulation framework to implement attack scenarios and evaluate the proposed detection method. LMID is trained depending on the length of input data and analyzed under various scenarios regarding platoon trajectories, attack types, and an emergency brake case. We have shown that without fast detection of such attacks, crashes may happen within a platoon. The simulation results demonstrate that LMID detects the malicious information attacks with higher than 96% accuracy and the attacks are detected very quickly. The performance evaluation indicates the superiority of the proposed detection method under various circumstances.

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“…Smart cars [32] connect to each other through what is called V2V communication [33] to synchronize traffic and safely [34] carry out driving actions. They may also connect to the road infrastructure in V2I communication [35,36] and generally connect to other entities in V2X communication [37,38] such as to interact with pedestrians [39].…”

Section: Connected Cars and Smart Transport Infrastructurementioning

confidence: 99%

Ubiquitous Computing: Driving in the Intelligent Environment

et al. 2021

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In the context of hyper-connected cars and a growing heterogeneous digital ecosystem, we wish to make the most of the data available from the various sensors, devices and services that compose the ecosystem, in order to propose a proof of concept in-vehicle system that enhances the driving experience. We focus on improving the driving experience along three main directions, namely: (1) driving and trip planning, (2) health and well-being and (3) social and online activities. We approached the in-vehicle space as a smart interface to the intelligent driving environment. The digital data-producers in the ecosystem of the connected car are sources of raw data of various categories, such as data from the outside world, gathered from sensors or online services, data from the car itself and data from the driver gathered with various mobile and wearable devices, by means of observing his state and by means of his social media and online activity. Data is later processed into three information categories—driving, wellness, and social—and used to provide multi-modal interaction, namely visual, audio and gesture. The system is implemented to act in response to the trafficked information on different levels of autonomy, either in a reactive manner, by simple monitoring, or in a proactive manner. The system is designed to provide an in-vehicle system that assists the driver with planning the travel (Drive panel), by providing a comfortable environment for the driver while monitoring him (Wellness panel), and by adaptively managing interactions with their phone and the digital environment (Social panel). Heuristic evaluation of the system is performed, with respect to guidelines formulated for automated vehicles, and a SWOT analysis of the system is also presented in the paper.

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Threat Detection for Collaborative Adaptive Cruise Control in Connected Cars

Cited by 45 publications

References 18 publications

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in “Smart Cities” under Various External Influences

An Approach to Detecting Malicious Information Attacks for Platoon Safety

Ubiquitous Computing: Driving in the Intelligent Environment

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