Network and system level security in connected vehicle applications

Liang, Hengyi; Jagielski, Matthew; Zheng, Bowen; Lin, Chung-Wei; Kang, Eunsuk; Shiraishi, Shinichi; Nita-Rotaru, Cristina; Zhu, Qi

doi:10.1145/3240765.3243488

Cited by 16 publications

(23 citation statements)

References 14 publications

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“…With the increasing research and rapid development of the Internet of Vehicles (IoV) technology, connected vehicles (CVs) and autonomous vehicles (AVs) are becoming increasingly popular in the modern world [1]. IoV serves as a primary vehicular communication framework that enables reliable communications between vehicles and other IoV entities, such as infrastructures, pedestrians, and smart devices.…”

Section: Introductionmentioning

confidence: 99%

“…IoV serves as a primary vehicular communication framework that enables reliable communications between vehicles and other IoV entities, such as infrastructures, pedestrians, and smart devices. [1].…”

Section: Introductionmentioning

confidence: 99%

“…IoV consists mainly of intra-vehicle networks (IVNs) and external vehicular networks [1]. IVNs involve an increasing number of electronic control units (ECUs) to adopt various functionalities [2].…”

Section: Introductionmentioning

confidence: 99%

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MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang

Moubayed

Shami

2022

IEEE Internet Things J.

167

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Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intra-vehicle networks to implement various functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-toeverything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intra-vehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyberattacks. In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the intra-vehicle network data and 99.88% accuracy on the CICIDS2017 dataset illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang

Moubayed

Shami

2022

IEEE Internet Things J.

167

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“…By including (19) in (16) and invoking Lemma 2, we can write the cost function in a way that confines the impact of the channel into the auxiliary metric ρ(⋅):…”

Section: B Equilibrium In Compact Formmentioning

confidence: 99%

“…The number of distinct road signs that a code can express is not directly related to the decodable distance. For example, [11,5,7] 16 can encode as much as around 1 million distinct road signs, but its decodable distance is 3, which is less than the decodable distance of [15,3,13] 16 , which can encode as much as 4096 distinct road signs. In order to examine the performance across a range of relative small to high noise channels, we consider 4 different channels with probabilities of symbol errors: p e = 0.01, 0.05, 0.1, 0.2.…”

Section: Numerical Examplesmentioning

confidence: 99%

Reliable Smart Road Signs

Sayin

Lin

Kang

et al. 2020

IEEE Trans. Intell. Transport. Syst.

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In this paper, we propose a game theoretical adversarial intervention detection mechanism for reliable smart road signs. A future trend in intelligent transportation systems is "smart road signs" that incorporate smart codes (e.g., visible at infrared) on their surface to provide more detailed information to smart vehicles. Such smart codes make road sign classification problem aligned with communication settings more than conventional classification. This enables us to integrate well-established results in communication theory, e.g., error-correction methods, into road sign classification problem. Recently, vision-based road sign classification algorithms have been shown to be vulnerable against (even) small scale adversarial interventions that are imperceptible for humans. On the other hand, smart codes constructed via error-correction methods can lead to robustness against small scale intelligent or random perturbations on them. In the recognition of smart road signs, however, humans are out of the loop since they cannot see or interpret them. Therefore, there is no equivalent concept of imperceptible perturbations in order to achieve a comparable performance with humans. Robustness against small scale perturbations would not be sufficient since the attacker can attack more aggressively without such a constraint. Under a game theoretical solution concept, we seek to ensure certain measure of guarantees against even the worst case (intelligent) attackers that can perturb the signal even at large scale. We provide a randomized detection strategy based on the distance between the decoder output and the received input, i.e., error rate. Finally, we examine the performance of the proposed scheme over various scenarios.

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