Security Issues with In-Vehicle Networks, and Enhanced Countermeasures Based on Blockchain

Khatri, Narayan; Shrestha, Rakesh; Nam, Seung Yeob

doi:10.3390/electronics10080893

Cited by 38 publications

(22 citation statements)

References 85 publications

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“…Blockchain frameworks can also be used for protecting in-vehicle networks. [280] An open challenge for blockchain-based vehicle security is the consensus mechanism, i.e., the election of peers for the consensus algorithm [280] as it directly impacts the integrity of the data in the network. Another challenge for blockchain is to integrate with existing in-vehicle protocols such as CAN and LIN.…”

Section: Security Privacy and Trustmentioning

confidence: 99%

Intelligent In‐Vehicle Interaction Technologies

Murali

Kaboli

Dahiya

2021

Advanced Intelligent Systems

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Autonomous vehicles (AVs) will soon become a reality with almost every major automotive original equipment manufacturer (OEM) such as Tesla, BMW, Daimler, and big technology giants such as Google's Waymo and Apple Car pushing toward the full self-driving objective. According to the SAE J3016 taxonomy [1] of autonomous driving, there are six levels of automated driving systems ranging from level 0 (completely manual) to level 5 (full selfdriving [FSD]) automated systems that are expected to function in all geographic locations, all weather conditions, and under all conditions. The proposed benefits of intelligent vehicles include fewer road accidents, enhanced safety, reduced traffic congestion, effective commute time usage, and importantly enjoyable and comfortable ride. With increased autonomy, the drivers also assume the role of mere passengers who are engaged in nondriving activities and are unavailable to participate in traffic interactions. This would increase complexity in a mixed-autonomy traffic environment as interactions with pedestrians and cyclists are based on visual cues with the driver. Therefore, intelligent vehicles also need to autonomously interact with other traffic participants such as pedestrians and cyclists and other vehicles.Human-vehicle interaction (HVI) is closely related to the field of human-robot interaction (HRI). It entails the problem of understanding and shaping the interaction dynamics between humans and vehicles. Particularly, the domain of interaction involves the study of sensation, perception, information exchange, inference, and decision-making. [2] With increasing levels of automation, the driver will have more time and choice to perform various tasks other than driving and this opens new avenues for interaction. As a result, a growing number of sensors are being incorporated into the vehicles to understand the driver's and/or passenger's actions, emotions, and personal choices to offer the precise functionalities and services for an enjoyable ride. [3] Figure 1 shows the schematic of in-vehicle interaction consisting of various interactive interfaces and sensing modalities present in the vehicle as well as some modes of interaction such as gestures, speech, and eye gaze.The advances in HVI have been presented in previous review articles, which have mainly focused on the detection of particular characteristics for driving assistance such as driver attention detection, [4] emotion recognition, [5] drowsiness detection, [6] mental workload, [7] and so on. A few other articles have reviewed the interior designs of AVs to better support and interact with drivers and passengers. [8,9] Likewise, the user interfaces (UIs) and user experience (UX) of vehicle interiors have been studied in context with manual as well as automated driving. [10] A number of previous works have also studied the interaction with the external

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Section: Security Privacy and Trustmentioning

confidence: 99%

Intelligent In‐Vehicle Interaction Technologies

Murali

Kaboli

Dahiya

2021

Advanced Intelligent Systems

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“…They implemented ACGAN and attained 99% accuracy, while CNN has given the output of around 96% accuracy. Narayan Khatri et al [5] proposed a comprehensive blockchain technology usage for Intra-vehicle network security. They insisted on using private blockchain nomenclature for the sensors and ECUs on the inside and the public blockchain method for connecting with the outside network.…”

Section: Related Workmentioning

confidence: 99%

“…Essentially, all of the components are linked and can communicate with one another. Telematics and infotainment systems are two significant sections of the communication of ECUs [5] [6]. Telematics equipment such as On-Board Diagnostics (OBD), black boxes, and other telematics devices are part of the telematics system.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Intra-Vehicle Networks

Dwivedi¹

2022

Preprint

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The progression of innovation and technology and ease of inter-connectivity among networks has allowed us to evolve towards one of the promising areas, the Internet of Vehicles. Nowadays, modern vehicles are connected to a range of networks, including intra-vehicle networks and external networks. However, a primary challenge in the automotive industry is to make the vehicle safe and reliable; particularly with the loopholes in the existing traditional protocols, cyberattacks on the vehicle network are rising drastically. Practically every vehicle uses the universal Controller Area Network (CAN) bus protocol for the communication between electronic control units to transmit key vehicle functionality and messages related to driver safety. The CAN bus system, although its critical significance, lacks the key feature of any protocol authentication and authorization. Resulting in compromises of CAN bus security leads to serious issues to both car and driver safety. This paper discusses the security issues of the CAN bus protocol and proposes an Intrusion Detection System (IDS) that detects known attacks on in-vehicle networks. Multiple Artificial Intelligence (AI) algorithms are employed to provide recognition of known potential cyber-attacks based on messages, timestamps, and data packets traveling through the CAN. The main objective of this paper is to accurately detect cyberattacks by considering time-series features and attack frequency. The majority of the evaluated AI algorithms, when considering attack frequency, correctly identify known attacks with remarkable accuracy of more than 99%. However, these models achieve approximately 92% to 97% accuracy when timestamps are not taken into account. Long Short Term Memory (LSTM), Xgboost, and SVC have proved to the well-performing classifiers.

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“…Surveys on in-vehicle network attacks and countermeasures can be found in several works, e.g., [14], [15], [16]. Many solutions were considered, ranging from the use of cryptographic security up to physical layer protection [5] and the industry was not slow in responding with security standards that are part of the AUTomotive Open System ARchitecture (AUTOSAR).…”

Section: B Related Workmentioning

confidence: 99%

Android Head Units vs. In-Vehicle ECUs: Performance Assessment for Deploying In-Vehicle Intrusion Detection Systems for the CAN Bus

et al. 2022

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Following the numerous attacks that exploited vulnerabilities of Controller Area Networks (CAN), intrusion detection systems have become a topic of prime importance for in-vehicle buses. Newer in-vehicle communication layers, such as CAN-FD, despite the larger payloads which can easily integrate cryptographic elements, need similar attention. But detecting intrusions may call for demanding algorithms that are not computationally cheap while timely detection is necessary in order to process frames in realtime and take the appropriate actions. In this work we evaluate the performance of several binary classifiers on traditional in-vehicle Electronic Control Units (ECUs) and compare them to modern Android devices which have become widespread inside cars with the adoption of Android-capable infotainment systems. Needless to say, these modern devices benefit from higher computational and memory resources while cloud connectivity may alleviate computational costs even further. Contrasting between traditional controllers and Android devices has become necessary and so far there have been little efforts in this direction. To create a realistic testbed, we use collected in-vehicle CAN bus traffic from an SUV as well as more demanding logs from Advanced Driver-assistance Systems (ADAS) implemented on CAN-FD which we augment with adversarial activity.

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Security Issues with In-Vehicle Networks, and Enhanced Countermeasures Based on Blockchain

Cited by 38 publications

References 85 publications

Intelligent In‐Vehicle Interaction Technologies

Intelligent In‐Vehicle Interaction Technologies

Anomaly Detection in Intra-Vehicle Networks

Android Head Units vs. In-Vehicle ECUs: Performance Assessment for Deploying In-Vehicle Intrusion Detection Systems for the CAN Bus

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