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arxiv: 1907.07455 · v1 · pith:BH3P7UHMnew · submitted 2019-07-17 · 💻 cs.CR

An Overview of Attacks and Defences on Intelligent Connected Vehicles

Mahdi Dibaei , Xi Zheng , Kun Jiang , Sasa Maric , Robert Abbas , Shigang Liu , Yuexin Zhang , Yao Deng
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Sheng Wen Jun Zhang Yang Xiang Shui Yu
This is my paper

Pith reviewed 2026-05-24 20:31 UTC · model grok-4.3

classification 💻 cs.CR
keywords cybersecurityintelligent connected vehiclessecurity attacksdefense classificationcryptographynetwork securityCAN busV2X communications
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The pith

Intelligent connected vehicles face attacks on ECUs, CAN bus, and V2X links, with defenses grouped into cryptography, network security, software vulnerability detection, and malware detection.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper reviews the architecture of next-generation vehicles and identifies major cybersecurity attacks that threaten in-vehicle systems and inter-vehicle communications. It then surveys available defenses and organizes them into four categories to provide a structured overview of the field. A reader would care because these vehicles rely on remote connectivity that can directly affect passenger safety, and a clear mapping of threats to fixes helps focus protection efforts. The authors also point to future directions for improving prevention as communication technologies advance.

Core claim

After outlining the vehicle architecture, this review identifies a few major security attacks on intelligent connected vehicles and surveys defenses against them, classifying the defenses into cryptography, network security, software vulnerability detection, and malware detection while exploring future prevention directions.

What carries the argument

The four-category classification of defenses (cryptography, network security, software vulnerability detection, and malware detection) that structures the survey of protections for Electronic Control Units, the CAN bus, and V2X communications.

If this is right

  • Security efforts can target specific components like the CAN bus using the matching defense category.
  • The classification supports building layered protections that combine multiple categories for V2X systems.
  • Gaps identified in current defenses highlight where software and malware tools need more development.
  • Future work on 5G-based communications can build directly on the surveyed cryptography and network approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same four-category lens might apply to other connected systems such as industrial IoT devices to test its broader usefulness.
  • Vehicle manufacturers could use the attack list to run targeted penetration tests on their specific models.
  • Over time the categories may need expansion if new attack vectors emerge from advances in autonomous driving software.

Load-bearing premise

The selected attacks and the four defense categories together cover the main threats and solutions without leaving out important ones.

What would settle it

Discovery of a significant attack type on intelligent connected vehicles or a defense method that fits none of the four listed categories would show the survey's classification is incomplete.

Figures

Figures reproduced from arXiv: 1907.07455 by Jun Zhang, Kun Jiang, Mahdi Dibaei, Robert Abbas, Sasa Maric, Sheng Wen, Shigang Liu, Shui Yu, Xi Zheng, Yang Xiang, Yao Deng, Yuexin Zhang.

Figure 1
Figure 1. Figure 1: Key structures of our technical contribution [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Gateway-based E/E architecture directions to address those security challenges in intelligent vehicle systems. Limitations and threats to the validity of this study are discussed in Section VI. Finally, Section VII summarises our findings. II. INTELLIGENT VEHICLE SYSTEM ARCHITECTURE The series production of high-level intelligent connected vehicle (ICV) is an active research topic in the automotive industr… view at source ↗
Figure 4
Figure 4. Figure 4: Centralized E/E Architecture of domain ECU, which is the core computation platform of each domain. The vehicle components can be classified into different domains according to their functionalities. Usually, the sensors and actuators those can be shared by different functionalities would be grouped as one domain. For example, the commonly used domains are the infotainment domain, the chassis and safety dom… view at source ↗
Figure 5
Figure 5. Figure 5: Mainstream sensors used in autonomous driving competitions and [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Typical components of intelligent vehicles [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Existing defences against the attacks asymmetric cryptography, which uses one key for encryption and another, separate, key for decryption. In symmetric-key encryption, it is essential that a secure channel is established so that keys can be exchanged safely. If this channel is compromised or the key is mistakenly shared with the at￾tacker, that attacker would have full access to the network. Traditionally… view at source ↗
Figure 8
Figure 8. Figure 8: Symmetric encryption schemes. The results of performance evaluation indicate that in 2FLIP, computation cost has been reduced 100-1000 times and communication overhead has been decreased between 55% and 77%. The reduction in overhead makes this method highly practical. A disadvantage of this algorithm is that all new drivers would have to be subjected to an authentication phase to add them to the list of a… view at source ↗
Figure 9
Figure 9. Figure 9: Asymmetric encryption However, the use of group keys could lead to serious breaches in the security framework. If an adversary was able to gain access to the group keys they would be able to authenticate their own messages. A privacy-preserving group communication scheme for VANETs (PPGCV) is proposed in [98]. The algorithm works in two phases. In the first phase, each user on the network is given a pool o… view at source ↗
Figure 10
Figure 10. Figure 10: Architecture for a typical ABE [138] application of Fuzzy Identity based Encryption. This was later expanded in [137], where the authors present a general framework for attribute based cryptology, seen as a more flexible alternative to the rigid traditional public-private key cryptography. Instead of using fixed public and private keys, encryption is done using specific attributes. The attributes are take… view at source ↗
Figure 11
Figure 11. Figure 11: (a) Signatute-based detection; (b) Anomaly-based detection [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: X by wire Intelligent vehicles including aircraft, airplane, and car have million lines of code (Table VI) and software is responsible for many safety-critical functions of the vehicle. Drive-by-wire, brake-by-wire, suspension-by-wire and in general X-by-wire ( [PITH_FULL_IMAGE:figures/full_fig_p020_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Various types of malware attacks on intelligent vehicles [PITH_FULL_IMAGE:figures/full_fig_p022_13.png] view at source ↗
Figure 15
Figure 15. Figure 15: Use case out of 5G coverage A key element in V2X communication is the ability for vehicles and roadside units to effectively and efficiently com￾municate. The 3GPP group outlines PC5 as the primary com￾munication protocol used between two autonomous cars. To facilitate communication between the vehicle and the roadside unit a protocol called Uu is used. The Vehicle to road-side unit/server is carried over… view at source ↗
Figure 14
Figure 14. Figure 14: V2X models B. 3GPP on V2X Security 3GPP is assigned to create technical specification services for LTE support of V2X (3GPP TS33.185 V15.0.0 (2018-06)) [190]. The 3GPP V2X standard will develop the specifications for all aspects of LTE Advanced and 5G networks, including the protocols architecture, Vehicle-to-Vehicle (V2V), Vehicle￾to-Infrastructure (V2I), Vehicle to Network (V2N), Vehicle to Pedestrian (… view at source ↗
Figure 17
Figure 17. Figure 17: Secure software update for electronic control unit. [PITH_FULL_IMAGE:figures/full_fig_p028_17.png] view at source ↗
Figure 16
Figure 16. Figure 16: Concept of intersection safety information system [PITH_FULL_IMAGE:figures/full_fig_p028_16.png] view at source ↗
Figure 18
Figure 18. Figure 18: A typical edge computing network automation on a large scale, in addition to the open stack standard as a platform to manage the cloud and distributed data centers. Vehicle-to-everything (V2X) is the future of land-based transport. Autonomous vehicles look to eliminate human errors by continuously monitoring and adapting to environmental variations. In such networks, security management is key to ensuring… view at source ↗
Figure 19
Figure 19. Figure 19: Classification error rates of ImageNet challenges [PITH_FULL_IMAGE:figures/full_fig_p029_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Input layer, hidden layers, and output layer in deep learning [PITH_FULL_IMAGE:figures/full_fig_p030_20.png] view at source ↗
read the original abstract

Cyber security is one of the most significant challenges in connected vehicular systems and connected vehicles are prone to different cybersecurity attacks that endanger passengers' safety. Cyber security in intelligent connected vehicles is composed of in-vehicle security and security of inter-vehicle communications. Security of Electronic Control Units (ECUs) and the Control Area Network (CAN) bus are the most significant parts of in-vehicle security. Besides, with the development of 4G LTE and 5G remote communication technologies for vehicle-toeverything (V2X) communications, the security of inter-vehicle communications is another potential problem. After giving a short introduction to the architecture of next-generation vehicles including driverless and intelligent vehicles, this review paper identifies a few major security attacks on the intelligent connected vehicles. Based on these attacks, we provide a comprehensive survey of available defences against these attacks and classify them into four categories, i.e. cryptography, network security, software vulnerability detection, and malware detection. We also explore the future directions for preventing attacks on intelligent vehicle systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The paper is a survey on cybersecurity for intelligent connected vehicles. It begins with an overview of next-generation vehicle architecture (including driverless and intelligent vehicles), identifies several major attacks on in-vehicle systems (ECUs and CAN bus) and inter-vehicle communications (V2X over 4G/5G), surveys available defenses, and organizes those defenses into four categories (cryptography, network security, software vulnerability detection, and malware detection). It concludes by discussing future research directions.

Significance. If the literature coverage is representative and the four-category taxonomy is applied consistently, the survey could serve as a useful entry point for researchers entering the vehicular cybersecurity area, particularly given the growth of V2X and autonomous systems. No machine-checked proofs, reproducible artifacts, or falsifiable predictions are present, as expected for a descriptive survey.

minor comments (2)
  1. [Abstract] Abstract: the phrase 'vehicle-toeverything' is missing a hyphen or space before '(V2X)'.
  2. [Abstract] The abstract states that the paper 'identifies a few major security attacks' yet also claims to deliver a 'comprehensive survey' of defenses; the full text should clarify the selection criteria for the attacks and the scope of the literature reviewed to support the comprehensiveness claim.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their review and recommendation of minor revision. The provided summary accurately reflects the scope and structure of our survey on cybersecurity attacks and defenses for intelligent connected vehicles. No specific major comments were listed in the report.

Circularity Check

0 steps flagged

No significant circularity; survey of external literature

full rationale

This is a review paper whose central claim is a descriptive survey of selected attacks on intelligent connected vehicles and an organizing classification of existing defenses into four categories drawn from prior work. No derivations, equations, predictions, fitted parameters, or self-citation chains appear in the text. The classification is explicitly presented as one possible lens rather than a provably exhaustive result derived from the paper's own content. The paper is self-contained as a literature overview against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a survey paper, the central claim rests on the authors' selection of literature and classification scheme rather than new axioms, free parameters, or invented entities.

pith-pipeline@v0.9.0 · 5733 in / 951 out tokens · 18449 ms · 2026-05-24T20:31:14.705309+00:00 · methodology

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Reference graph

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