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REVIEW 4 major objections 5 minor 165 references

A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This survey maps 85 learning-based intrusion detection systems for in-vehicle CAN networks and argues that combined known-unknown detection and federated learning are the least developed approaches.

desk verdict A useful map of 85 learning-based in-vehicle IDS papers with a defensible known/unknown/combined taxonomy; the coverage claims need a caveat and the manuscript needs a cleanup pass. read the letter →

arxiv 2505.11551 v1 pith:U7RVC4YN submitted 2025-05-15 cs.CR cs.LG

Muzun Althunayyan , Amir Javed , Omer Rana This is my paper · ORCID
classification cs.CRcs.LG
keywords CANbusintrusiondetectionsystemin-vehiclenetworkmachinelearningdeepfederatedanomalyconnectedautonomousvehicles
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This survey tries to establish a reliable map of learning-based intrusion detection systems (IDSs) for the Controller Area Network (CAN) bus—the internal data bus that connects a vehicle's electronic control units—and to identify where the research is thinnest. It reviews 85 papers that use machine learning, deep learning, or federated learning to detect cyberattacks inside vehicles, and organizes them by whether they detect known attacks, unknown attacks, or both. The paper argues that known-attack detection is a mature research area, while IDSs that can classify known attacks and still catch novel ones, together with federated-learning IDSs, are the least developed. It also argues that most evaluations report accuracy-style metrics while neglecting the inference time, memory footprint, and real-time constraints that decide whether a detector can actually be put into a car. If this map is right, research effort should shift toward combined known-unknown detectors and privacy-preserving federated approaches.

What carries the argument

The machinery carrying the argument is a three-part attack-detection taxonomy—known, unknown, and combined known-unknown attacks—cross-cut by input-feature type (CAN ID, payload, full CAN frame), together with a systematic search protocol of automatic title-filtered Google Scholar queries, manual library searches, and forward and backward snowballing that yields the 85-paper corpus. The taxonomy does the analytical work: it turns 'detect both known and unknown attacks' into a visible category, and the feature cross-cut exposes blind spots such as CAN-ID-only detectors being unable to catch payload-manipulation attacks. A second mechanism is the evaluation-metric rubric, which sorts reported measures into performance, time complexity, memory, and other categories and turns the paper's normative claim—that deployable IDSs must be judged on latency and footprint as well as accuracy—into a concrete checklist.

What would settle it

Rerun the literature search without the title-only restriction, searching abstracts and full texts instead, and count the additional relevant learning-based CAN-bus IDS papers; if that count is large enough to change the 38/27/11/9 distribution, the paper's under-researched-area conclusions would need to be revised.

Watch

Extended reading notes

Core claim

The paper's central claim is that learning-based in-vehicle IDS research splits into three attack-detection regimes—known, unknown, and combined known-unknown attacks—and that its 85 collected papers distribute as 38, 27, and 11 across these regimes, with 9 additional papers on federated learning. Within each regime it further groups systems by input features: CAN ID only, payload only, or full CAN frames. It finds that known-attack detectors are mostly supervised deep-learning classifiers trained on CAN frames, while unknown-attack detectors are mostly unsupervised reconstruction or prediction models that profile normal traffic. The paper also claims to be the first survey to review machine learning, deep learning, and federated learning for in-vehicle networks together under a structured search strategy, and its metric review concludes that deployment-relevant measures such as model size, latency, and memory use are widely omitted.

Load-bearing premise

The survey's counts and gap conclusions stand on the assumption that its search strategy—especially the Google Scholar step that keeps only papers whose titles contain the chosen keywords—recovered essentially all relevant learning-based in-vehicle IDS work published up to January 2025.

Editorial extensions

If this is right

  • Known-attack detection is the most crowded of the three regimes, so new work that only adds another supervised classifier on the same datasets will add little.
  • If the survey's counts are correct, combined known-unknown detection and federated-learning IDSs are the two least developed areas and the natural focus for future research.
  • Evaluation practice needs to include inference time, detection latency, and model size; the paper notes a vehicle-level IDS should process each packet in under roughly 10 ms to meet real-time safety requirements.
  • Most federated-learning IDS studies assume clients hold identically distributed data, and only a few simulate non-IID conditions, so current FL evaluations are not yet realistic for real vehicle fleets.
  • Because centralized training dominates the surveyed work, privacy, communication overhead, and single-point-of-failure concerns remain open, which is the motivation the paper gives for hierarchical or personalized FL.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the title-only Google Scholar filter means the 85-paper count is better read as a lower bound; a fuller abstract and full-text search would likely surface additional relevant papers that could shift the 38/27/11/9 distribution.
  • Editorial inference: a multi-stage architecture—supervised classification first, unsupervised anomaly detection as a backstop—appears several times in the survey and can be tested as a general template; its main open question is whether the unsupervised stage stays reliable on vehicle makes whose normal traffic differs from the training fleet.
  • Editorial inference: applying the survey's deployment-metric checklist uniformly to the reviewed systems would probably shrink the list of deployable solutions sharply, since many papers report only accuracy and F1.
  • Editorial inference: the taxonomy implies a testable ordering—that payload-only and full-frame detectors should beat ID-only detectors on spoofing and fuzz attacks—but the surveyed papers rarely compare those feature regimes on identical data, so a controlled benchmark across feature types would be a direct follow-up.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This survey reviews learning-based intrusion detection systems (IDSs) for in-vehicle networks, focusing on machine learning (ML), deep learning (DL), and federated learning (FL) approaches. The authors followed a three-stage search strategy (Google Scholar automatic search, manual library search, and forward/backward snowballing) through January 2025, yielding 85 papers that are categorized into known-attack detection (38 papers), unknown-attack detection (27), combined known-unknown attack detection (reported as 11 in Sections 4.1.2 and 8, but as 10 in Section 5.5), and FL-based IDSs (9). The paper also reviews evaluation metrics in terms of performance, time, and memory requirements, and outlines limitations and future research directions. The authors claim to the best of their knowledge that this is the first comprehensive review of ML, DL, and FL-based IDSs for in-vehicle networks.

Significance. If the search is complete, the paper provides a useful map of the field: a documented search protocol, a clear known/unknown/combined taxonomy, detailed tables linking datasets, algorithms, input features, and model sizes, and a dedicated analysis of FL-based IDSs including non-IID and client-selection limitations. The systematic categorization and the review of evaluation metrics beyond accuracy are genuine strengths that would help practitioners assess deployability. The central 'under-researched areas' conclusions—combined known-unknown detection and FL—are falsifiable and important for guiding future work, but they rest on the completeness of the paper collection and on the internal consistency of the reported counts, which currently require attention.

major comments (4)
  1. [4.1.1 Data Sources and Search Strategy] The central claim of comprehensiveness and the gap statistics (38/27/11/9) depend on the completeness of the search, but the paper never quantifies the recall of its title-only filter. Table 2 shows 53,890 'anywhere in the article' results reduced to 95 with 'in the title of the article,' yet the manual search examples are also title-focused (ACM and IEEE use Document Title) and the Scopus query uses TITLE-ABS-KEY with a different term set. Without a PRISMA-style flow count of papers excluded at each stage and without a recall check (for example, by comparing against the reference list of a recent independent survey), the claim that these numbers represent the state of the art up to January 2025 is not fully supportable. Please add per-stage exclusion counts and quantify the fraction of relevant papers that the title filter misses.
  2. [5.5 Known and Unknown Attacks Detection] The number of combined known-unknown attack detection papers is inconsistent across the manuscript: Sections 4.1.2 and 8 state 11, Table 6 contains 11 rows, but Section 5.5 states '10 papers' and lists 10 references (excluding Althunayyan et al. 2024a), even though that work is reviewed in Section 5.5.3 and appears in Table 6. Since the total count of 85 is the paper's headline result, this discrepancy must be resolved.
  3. [5.4.3 / Table 5] The sentence after Table 5 reads 'Among these studies, only three [Song et al., Fenzl et al., Le et al.] measure the trainable parameters,' which is verbatim from Section 5.3 (after Table 4) and is incorrect in the unknown-attack context: Table 5 itself lists model sizes or parameter counts for Sun et al., Thiruloga et al., Kukkala et al., Kristianto et al., Longari et al., Rajapaksha et al., and Kim et al. The duplication misstates the evidence on model-size reporting in the unknown-attack literature and should be corrected or removed.
  4. [4.1.2 Selection Strategy] The selection flow is not traceable: the automatic search yielded 95 title-filtered papers, yet the text says 'The papers from the automatic search were reduced from 10 to 9 after filtering by reading the full text.' The paper does not explain how 95 became 10 before the full-text step. The same applies to the manual (1,831 to 59) and snowballing (57 to 17) stages; a per-stage exclusion count is needed to support the reported totals.
minor comments (5)
  1. [5.6] The section heading 'Evaluation Metics' should be corrected to 'Evaluation Metrics'.
  2. [2.5.2] In the sentence 'Koscher et al. [Chockalingam et al.(2016)...] highlight the feasibility of executing various types of wireless attack injections,' the narrative author name and the cited reference do not match; please change the narrative name to Chockalingam et al. or replace the citation.
  3. [6.1] The phrase 'without transfer-ring row data' should read 'without transferring raw data.'
  4. [Table 6 note] The note 'DL: Deap Learning' should be corrected to 'Deep Learning.'
  5. [5.5.1 / Table 6] The text states that Hoang et al. and Seo et al. use only CAN IDs as input features, but the checkmark placement in Table 6 is ambiguous because the M-C, ID, and Payload columns are not visually separated in the rendering; please ensure the table formatting clearly aligns with the text to avoid reader confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the survey's classification is descriptive and the authors' self-citations are not load-bearing.

full rationale

This paper is a literature survey; it contains no fitted parameters, equations, or derived numeric predictions. The paper's central claim—being the first comprehensive review of ML, DL, and FL-based in-vehicle IDSs—is supported by a documented Kitchenham-style search and a comparison table of prior surveys, not by a definitional or self-referential reduction. The 38/27/11/9 category counts are descriptive statistics over the 85 collected papers, and the statement that combined known-unknown detection and FL-based IDSs are under-researched follows directly from those counts; no category is defined in terms of the conclusion it is used to support. The only self-citations appear in Section 5.5.3 (the authors' multi-stage ANN-LSTM-AE IDS as one reviewed design), Section 6.2 (their H-FL deployment), Section 6.3 (as one of three studies modeling Non-IID data), and Section 6.1 (a cited benefit of FL). In each case the citation is descriptive of prior work and not used to justify the survey's taxonomy or gap conclusions. No uniqueness theorem, ansatz, or fitted input is imported from the authors' own prior papers. The title-only Google Scholar filtering concern raised in Section 4.1.1 is a potential recall/completeness threat to coverage, but it is not circularity: an incomplete search can make a comprehensiveness claim weaker without making the conclusions equivalent to the search inputs.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

This survey makes no empirical measurements and introduces no free parameters or new entities. Its conclusions rest on the completeness of the literature search and the accuracy of its categorization of 85 cited papers, which is why the domain assumptions above are the load-bearing items.

assumptions (3)
  • domain assumption The 85-paper corpus returned by the Section 4 search is complete and representative of learning-based in-vehicle IDS research through January 2025.
    The survey's summary statistics (38/27/11/9) and its gap analysis depend on the search not missing a substantial body of work. The title-only Google Scholar filter makes this assumption fragile.
  • domain assumption Papers categorized as 'unknown attack detection' genuinely detect novel attacks, using their reported evaluations as evidence.
    The survey takes author-reported capabilities and datasets at face value; Section 5.4 itself notes some models are trained per CAN ID and limited to certain anomaly types, so real-world generalization is not established.
  • domain assumption Taxonomy categories are mutually consistent across the 85 papers.
    The paper assigns each paper to exactly one of known, unknown, or combined known-unknown, but Section 5.5 says 10 papers while Sections 4 and 8 say 11, so the counts are not fully reliable.

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Cite this review

Pith. "Pith review of A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network." pith.science (2026). https://pith.science/paper/U7RVC4YN

@misc{pith2026250511551,
  author       = {Pith},
  title        = {Pith review of: A Survey of Learning-Based Intrusion Detection Systems for In-Vehicle Network},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U7RVC4YN}},
  note         = {Machine review of arXiv:2505.11551}
}
read the original abstract

Connected and Autonomous Vehicles (CAVs) enhance mobility but face cybersecurity threats, particularly through the insecure Controller Area Network (CAN) bus. Cyberattacks can have devastating consequences in connected vehicles, including the loss of control over critical systems, necessitating robust security solutions. In-vehicle Intrusion Detection Systems (IDSs) offer a promising approach by detecting malicious activities in real time. This survey provides a comprehensive review of state-of-the-art research on learning-based in-vehicle IDSs, focusing on Machine Learning (ML), Deep Learning (DL), and Federated Learning (FL) approaches. Based on the reviewed studies, we critically examine existing IDS approaches, categorising them by the types of attacks they detect - known, unknown, and combined known-unknown attacks - while identifying their limitations. We also review the evaluation metrics used in research, emphasising the need to consider multiple criteria to meet the requirements of safety-critical systems. Additionally, we analyse FL-based IDSs and highlight their limitations. By doing so, this survey helps identify effective security measures, address existing limitations, and guide future research toward more resilient and adaptive protection mechanisms, ensuring the safety and reliability of CAVs.

Figures

Figures reproduced from arXiv: 2505.11551 by the authors.

Figure 1
Figure 1. CAN data frame 2.4 CAN Vulnerabilities The CAN bus was introduced to reduce costs, simplify installation, and improve real￾time communication efficiency within vehicles. However, it is vulnerable to cyberat￾tacks due to several inherent vulnerabilities [Aliwa et al.(2021)Aliwa, Rana, Perera, and Burnap, Carsten et al.(2015)Carsten, Andel, Yampolskiy, and McDonald, Liu et al.(2017)Liu, Zhang, Sun, and Shi], including… view at source ↗
Figure 2
Figure 2. CAN bus attacks [Rajapaksha et al.(2023a)Rajapaksha, Kalutarage, Al-Kadri, Petrovski, Madzudzo, and Cheah]. Koscher et al. [Koscher et al.(2010)Koscher, Czeskis, Roesner, Patel, Kohno, Checkoway, McCoy, Kantor, Anderson, Shacham, et al.] demonstrated that DoS attacks can disable individual CAN bus components. • Attack Method: Since message priority is determined by the arbitration field, an attacker can exploit the … view at source ↗
Figure 3
Figure 3. Search and selection processes flowchart [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Distribution of collected papers by category [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Categories of reviewed literature 5.1 Intrusion Detection System for In-Vehicle Networks According to NIST SP 800-94, intrusion detection is “the process of monitoring the events oc￾curring in a computer system or network and analysing them for signs of possible incide…
Figure 6
Figure 6. Figure 6: Related work on known attack detection 16 [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Related work on unknown attack detection [PITH_FULL_IMAGE:figures/full_fig_p022_7.png]
Figure 8
Figure 8. Figure 8: Related work on known and unknown attack detection [PITH_FULL_IMAGE:figures/full_fig_p028_8.png]
Figure 9
Figure 9. Figure 9: Federated Learning Architecture [Agrawal et al.(2022b)Agrawal, Sarkar, Aouedi, Yenduri, Piamrat, Alazab, Bhattacharya, Maddikunta, and Gadekallu]. This integration of FL into IDSs enhances security and privacy, addressing the growing challenges of protecting data in an…

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.