Pith. sign in

REVIEW 4 major objections 5 minor 60 references

Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Graph-based models detect drone CAN-bus injection attacks without decoding the payload.

desk verdict Useful graph construction for UAVCAN intrusion detection, but the protocol-independence claim fails on the method's own use of transfer ID, and the metrics are too thin to support the comparison. read the letter →

arxiv 2412.02539 v2 pith:DBI3BVHI submitted 2024-12-03 cs.AI

Reek Majumder , Gurcan Comert , David Werth , Adrian Gale , Mashrur Chowdhury , M Sabbir Salek This is my paper · ORCID
classification cs.AI
keywords ControllerAreaNetworkintrusiondetectionsystemgraphneuralnetworksUAVCANprotocolunmannedaerialvehiclesecuritymessageinjectionattacksprotocol-independentPageRank
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

The paper sets out to build a lightweight, protocol-independent intrusion detection system for the Controller Area Network (CAN) bus inside unmanned aerial vehicles. It converts sequences of UAVCAN messages into graphs—nodes are distinct CAN identifiers, edges follow the order messages appear, and edge weights record timing gaps—then feeds node features (PageRank and per-CAN-ID density) into four graph-based machine learning models. The central claim is that these graph models catch flooding, fuzzy, replay, and mixed injection attacks better than a baseline LSTM that does decode the UAVCAN payload, with accuracy gains of 1–29 percent, and that the best results come from inductive models like GraphSAGE and graph transformers. If true, drone makers could deploy intrusion detection without reverse-engineering or disclosing their CAN message formats, and the same pipeline could transfer to other CAN-based vehicles.

What carries the argument

The load-bearing construction is the graph stream built from raw CAN traffic: within each fixed window of 100 samples, each unique CAN ID becomes a vertex, a directed edge connects consecutive CAN IDs, consecutive identical CAN IDs get a self-loop only when the UAVCAN transfer ID changes (marking a new multi-frame message), and the edge weight is the summed timestamp gap between the two endpoints. On top of this graph, the pipeline computes a modified PageRank that incorporates edge weights and a density feature counting each CAN ID's occurrences over the current window plus the previous 150 samples. These per-vertex features are what the graph neural networks classify, so the self-loop rule is what lets a 'protocol-independent' detector still separate repeated message frames from new messages.

What would settle it

Run the identical graph construction on raw CAN frames with the transfer ID removed (or on a CAN-based system with no equivalent field), using only CAN ID, timestamps, and the self-loop rule replaced by a timing threshold, and compare detection on multi-frame replay attacks against the LSTM baseline; if accuracy drops to near-baseline or below, the protocol-independence claim is falsified. A complementary check: craft an injection attack whose fake messages reuse legitimate CAN IDs and transfer-ID plus-minus patterns so the density and PageRank statistics stay inside the normal range, and test whether the detector still flags it.

Watch

Extended reading notes

Core claim

The paper claims that converting CAN bus traffic into a temporal graph—vertices are CAN IDs, edges follow message order, edge weights accumulate timestamp gaps, and self-loops mark new messages when the UAVCAN transfer ID changes—lets graph neural networks detect flooding, fuzzy, replay, and mixed injection attacks on drone CAN buses more accurately than an LSTM trained on decoded UAVCAN payload data. Across ten attack scenarios, the inductive graph models (GraphSAGE and a graph transformer) stayed above about 90 percent accuracy in most scenarios, with gains of roughly 1 to 29 percentage points over the LSTM baseline, and they do this without decoding the payload contents. The authors take this as evidence that the detection signal lives in the structure and timing of CAN IDs rather than in the protocol payload, making the approach portable to proprietary or undocumented CAN protocols.

Load-bearing premise

The claim of protocol independence rests on using the UAVCAN transfer ID—a field taken from decoded protocol messages—to decide when two consecutive identical CAN IDs are separate messages; if that field is unavailable, the graph loses multi-frame message boundaries and the central claim collapses.

Editorial extensions

If this is right

  • A CAN-based drone IDS can be built without access to payload contents, so it applies to proprietary or undocumented protocols.
  • Inductive graph models (GraphSAGE, graph transformers) are the better choice for dynamic CAN traffic because they generalize to new graphs, while transductive GCNNs lag on some scenarios.
  • Replacing a payload-decoding LSTM with graph features buys 1 to 29 percentage points of accuracy across all ten tested attack scenarios.
  • The feature set—CAN ID ordering, density, and timing-weighted PageRank—is cheap enough to run on microcontrollers rather than GPU-equipped boards.

Reading between the lines

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

  • The protocol-independence claim is narrower than it sounds: the self-loop rule reads the transfer ID from a decoded UAVCAN tail byte, so the method is payload-independent but not decoding-free. A truly closed protocol would need a proxy for transfer ID, such as timing gaps between identical IDs, and that proxy's reliability is untested.
  • Because the features are statistical summaries of ID order and frequency, an attacker who paces injected frames to mimic normal CAN ID distributions and transfer-ID patterns could plausibly evade the detector; this evasion scenario is not explored in the paper.
  • A natural testable extension is to run the same graph construction on automotive CAN data (11-bit IDs, no UAVCAN transfer ID) to see whether the self-loop rule can be replaced by inter-frame timing; success there would support the generalization argument.
  • The density window (150 prior samples) and graph window (100 samples) are fixed rather than tuned per attack; varying these windows might improve detection of slow replay attacks, where the current numbers show lower precision.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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 paper proposes a graph-based intrusion detection system for UAV CAN bus traffic using the UAVCAN protocol. The authors decode CAN messages from the HCRL dataset, convert streams of CAN IDs into time-windowed graphs with timestamp-based edge weights and Transfer-ID-dependent self-loops, compute PageRank and density features, and train GCNN, GAT, GraphSAGE, and graph-transformer classifiers. They compare against a single-layer LSTM baseline and claim that the graph models outperform it while being protocol-independent and using no UAVCAN-decoded payload features. The paper concludes that graph methods capture CAN communication structure better than sequence models.

Significance. The problem is timely, and the idea of representing CAN traffic as temporal communication graphs is a reasonable direction; the comparison across ten attack scenarios is also useful. However, the paper's central contribution, protocol-independent detection without decoded payload features, is contradicted by the method's use of Transfer ID, a decoded UAVCAN payload field. In addition, the reported results in several scenarios show F1/precision/recall around 0.5, which indicates near-random attack detection despite high accuracy, and the comparison with the LSTM is not a controlled experiment. If the protocol-independence claim were properly supported, the work would be of practical interest; as written, the evidence does not support it.

major comments (4)
  1. [§VI-A, §VI-B, Eq. (1)] The protocol-independence claim is contradicted by the graph construction. Section VI-A states that "one feature, transfer ID, is used, which defines self-loops in graphs," and Figure 5 shows that the transfer ID is embedded in the payload tail byte of UAVCAN messages. Section VI-B then uses the constraint Transfer ID_ti != Transfer ID_ti+1 in Eq. (1) to decide whether consecutive identical CAN IDs represent a new multi-frame message. Transfer ID is therefore a decoded protocol payload field, not a raw structural property of the CAN bus. The abstract and contributions claim that the graph models perform best "without using any decoded features based on the UAVCAN protocol," but no such variant is evaluated. If Transfer ID is removed, the self-loop structure changes and the reported performance may change; no results are given for that protocol-independent version.
  2. [Tables II–V] Several reported scenarios do not indicate successful detection. In Table II, Scenario 2 shows precision 0.511, recall 0.740, F1 0.516, and Scenario 4 shows precision 0.500, recall 0.499, F1 0.499; Tables III–V report similar values near 0.5 for Scenarios 2 and 4. Since F1 = 0.5 with accuracy above 0.97 is the signature of a classifier that essentially predicts the majority class, the text's claims of "high precision, recall, and F1-score" (Section VII) are not supported for these scenarios. This matters because Scenarios 2 and 4 are not edge cases; they are two of the four single-attack flooding and fuzzy scenarios.
  3. [Table VI] The claim that "all our graph-based models perform better" than the LSTM is not supported by the reported numbers. In Table VI, GAT accuracy in Scenario 6 is 0.697, below the LSTM's 0.706, and the conclusion's statement that GB-ML models show improvements of 1% to 29% "across all attack scenarios" is therefore inaccurate. Moreover, Table VI reports only accuracy, while Section VIII claims improvements "in terms of precision, recall, and F1 scores." The paper does not provide the additional tables needed to support that claim.
  4. [§VII, Tables II–VI] The evaluation lacks the experimental detail needed to compare models. There are no train/test splits, no repeated runs, no confidence intervals or error bars, and no hyperparameters (e.g., GNN layers, hidden dimensions, learning rate, dropout, number of epochs, or the actual class weights used to address imbalance). The LSTM baseline is imported from [59] without specifying whether it uses the same splits, features, and attack scenarios. Under these conditions, the accuracy differences in Table VI cannot be distinguished from noise.
minor comments (5)
  1. [Section VI heading] The heading of Section VI reads "STRATERGY" instead of "Strategy."
  2. [Eq. (5)] Equation (5) is not clearly defined: dividing PageRank(pi)/OutDegree(pi) by EdgeWeight(V1, pi) without normalizing by the total edge weight is dimensionally odd and is not described in the text; the sentence before the equation mentions dividing by a summation that does not appear in the formula.
  3. [§VI-C, §VI-D] The density window length (150 samples), PageRank damping factor d, and graph window size Δt are introduced without justification or sensitivity analysis; these are free parameters of the method.
  4. [References] Several references lack complete bibliographic information, and reference [60] contains a "utm_source=chatgpt.com" tracking parameter in its URL, which is inappropriate for an archival reference list.
  5. [General] The paper does not include a data/code availability statement, which would be useful for verifying the reported results.

Circularity Check

1 steps flagged · score 6.0 of 10

Protocol-independence claim is self-definitional: graph construction uses Transfer ID, a UAVCAN-decoded payload field.

  1. self definitional [Section VI-A 'Data Decoding'; Section VI-B 'Graph Construction Module', Eq. 1; Abstract]
    "Following the decoding... one feature, transfer ID, is used, which defines self-loops in graphs. ... Only the Timestamp, CAN ID, and Transfer ID were extracted for this task from the decoded dataset. ... all our graph-based models perform better without using any decoded features based on the UAVCAN protocol, highlighting higher detection performance with protocol-independent capability."

    Transfer ID is a decoded UAVCAN payload feature: Section VI-A and Figure 5 locate it in the hexadecimal tail byte of UAVCAN messages, and Section V states the released dataset contains already-decoded UAVCAN messages, meaning no further decoding was performed by the authors. Equation 1's self-loop rule uses 'Transfer ID_ti != Transfer ID_ti+1' to decide whether consecutive identical CAN IDs are new messages, so the temporal graph's edge structure depends on this decoded protocol field. The claimed evaluation 'without using any decoded features based on the UAVCAN protocol' is therefore not actually performed: a decoded feature is load-bearing in the graph construction.

full rationale

The only substantive circularity is the protocol-independence claim. The paper's central assertion that graph models perform better 'without using any decoded features based on the UAVCAN protocol' conflicts with the method's own graph construction, which uses the UAVCAN-decoded Transfer ID to define self-loops. This is a self-definitional reduction: the supposed protocol-independent feature set includes a protocol-decoded field, so the headline comparison is not demonstrated. The supervised training and evaluation of the graph models is otherwise an ordinary benchmark against an external LSTM baseline, with no fitted parameter renamed as a prediction and no load-bearing self-citation chain; citations to prior work by the authors are not used to justify the central derivation. Separately, Tables II-V show several scenarios with precision/recall/F1 near 0.5 despite high accuracy, indicating a class-imbalance correctness concern, but that is not circularity. Overall, the paper retains independent content in its inductive-versus-transductive comparison, but the headline protocol-independence capability is partially circular and warrants a score of 6.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical entities are introduced. The method relies on algorithmic hyperparameters and the correctness of the public UAVCAN dataset and transfer ID decoding. Several choices, including window size, density window, damping factor, and class weights, are not exposed to sensitivity analysis.

free parameters (4)
  • Graph window size Delta t = 100 samples
    Chosen by hand for graph stream construction; no sensitivity analysis reported.
  • Density lookback window = 150 samples
    Chosen as the past 150 samples for the density feature; no justification or sensitivity analysis.
  • PageRank damping factor d = not stated (standard default 0.85)
    Used in the modified PageRank formula; the exact value is not reported.
  • Class weights = not specified
    Used to address class imbalance during training; the exact weighting scheme is omitted.
assumptions (4)
  • domain assumption The HCRL UAVCAN dataset correctly labels benign and attack CAN traffic for a real drone testbed.
    All experiments use this public dataset; no independent validation set or testbed is provided.
  • domain assumption Transfer ID, decoded from UAVCAN payloads, reliably marks message boundaries for multi-frame communication.
    The self-loop constraint in Section VI-B relies on transfer ID equality to distinguish new messages; incorrect decoding would corrupt graph structure.
  • domain assumption PageRank and density features computed on windowed graphs are sufficient to discriminate injected attacks from normal traffic.
    This is the core modeling hypothesis; the paper provides no ablation or theoretical justification showing these features capture attack-specific structure.
  • domain assumption Standard GNN training practices such as random splits and class-weighted loss produce a representative evaluation.
    The paper applies class weights but does not report splits, seeds, or variance; this assumption is implicit in the results.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles." pith.science (2026). https://pith.science/paper/DBI3BVHI

@misc{pith2026241202539,
  author       = {Pith},
  title        = {Pith review of: Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DBI3BVHI}},
  note         = {Machine review of arXiv:2412.02539}
}
read the original abstract

The network of services, including delivery, farming, and environmental monitoring, has experienced exponential expansion in the past decade with Unmanned Aerial Vehicles (UAVs). Yet, UAVs are not robust enough against cyberattacks, especially on the Controller Area Network (CAN) bus. The CAN bus is a general-purpose vehicle-bus standard to enable microcontrollers and in-vehicle computers to interact, primarily connecting different Electronic Control Units (ECUs). In this study, we focus on solving some of the most critical security weaknesses in UAVs by developing a novel graph-based intrusion detection system (IDS) leveraging the Uncomplicated Application-level Vehicular Communication and Networking (UAVCAN) protocol. First, we decode CAN messages based on UAVCAN protocol specification; second, we present a comprehensive method of transforming tabular UAVCAN messages into graph structures. Lastly, we apply various graph-based machine learning models for detecting cyber-attacks on the CAN bus, including graph convolutional neural networks (GCNNs), graph attention networks (GATs), Graph Sample and Aggregate Networks (GraphSAGE), and graph structure-based transformers. Our findings show that inductive models such as GATs, GraphSAGE, and graph-based transformers can achieve competitive and even better accuracy than transductive models like GCNNs in detecting various types of intrusions, with minimum information on protocol specification, thus providing a generic robust solution for CAN bus security for the UAVs. We also compared our results with baseline single-layer Long Short-Term Memory (LSTM) and found that all our graph-based models perform better without using any decoded features based on the UAVCAN protocol, highlighting higher detection performance with protocol-independent capability.

Figures

Figures reproduced from arXiv: 2412.02539 by the authors.

Figure 1
Figure 1. Intrusion detection system for in-vehicle network security [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Security threats to CAN bus network key generator [35]. Some research efforts have combined encryption and authentication of CAN messages using the stream cipher RC4 [36]. However, integrating authentication, authorization, and encryption solutions poses a significant challenge, especially when modern vehicles demand real￾time communication. To address this drawback, other IDS were investigated [37], [38]. The IDS f… view at source ↗
Figure 3
Figure 3. Structure of CAN data frames. (a) Standard CAN 2.0A uses an 11-bit identifier. (b) Extended CAN 2.0B uses a 29-bit identifier, incorporating [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Workflow of the proposed graph-based UAVCAN IDS [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Structured representation of UAVCAN messages derived from decoded CAN frames, showing bitwise composition across multiple frame types. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 8
Figure 8. Figure 8: Illustration of the density window used for calculating the local density [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 7
Figure 7. Figure 7: An example of our graph construction approach. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

60 extracted references · 53 canonical work pages

  1. [59]

    Tlili, S

    F. Tlili, S. Ayed, and L. Chaari Fourati, Dynamic Intrusion Detection Framework for UAVCAN Protocol Using AI , presented at the 18th International Conference on Availability, Reliability and Security (ARES), Aug. 2023, pp. 1–10

    work page 2023

  2. [1]

    McKinsey & Company

    Commercial Drones Are Here: The Future of Unmanned Aerial Systems . McKinsey & Company. [Online]. Avail- able: https://www.mckinsey.com/industries/travel-logistics-and- infrastructure/our-insights/commercial-drones-are-here-the-future-of- unmanned-aerial-systems. Accessed: Jul. 8, 2024

    work page 2024

  3. [2]

    Em- bien

    Automotive CAN Bus: Exploring CAN Vehicle Communication . Em- bien. [Online]. Available: https://www.embien.com/automotive-can-bus- exploring-can-vehicle-communication. Accessed: Jul. 30, 2024

    work page 2024

  4. [3]

    Can Bus Technology - Revolutionizing Aerospace Commu- nication and Connectivity

    avia2b-admin. Can Bus Technology - Revolutionizing Aerospace Commu- nication and Connectivity . avia2b, Jan. 4, 2024

    work page 2024

  5. [4]

    S. Adly, A. Moro, S. Hammad, and S. A. Maged, Prevention of Con- troller Area Network (CAN) Attacks on Electric Autonomous Vehicles , Applied Sciences, vol. 13, no. 16, p. 9374, 2023. [Online]. Available: https://doi.org/10.3390/app13169374

    work page doi:10.3390/app13169374 2023

  6. [5]

    Fakhfakh, M

    F. Fakhfakh, M. Tounsi, and M. Mosbah, Cybersecurity Attacks on CAN Bus Based Vehicles: A Review and Open Challenges , Library Hi Tech, vol. 40, no. 5, pp. 1179–1203, 2022. [Online]. Available: https://doi.org/10.1108/LHT-01-2021-0013

    work page doi:10.1108/lht-01-2021-0013 2022

  7. [6]

    Y . Meng, J. Li, F. Liu, S. Li, H. Hu, and H. Zhu, GB-IDS: An Intrusion Detection System for CAN Bus Based on Graph Analysis , presented at the 2023 IEEE/CIC International Conference on Communications in China (ICCC), 2023

    work page 2023

  8. [7]

    Trend Micro

    DHS Warning: Small Planes Vulnerable to Cyberattack Through CAN Bus Component . Trend Micro. [Online]. Available: https://www.trendmicro.com/vinfo/us/security/news/vulnerabilities-and- exploits/dhs-warning-small-planes-vulnerable-to-cyberattack-through- can-bus-component. Accessed: Oct. 27, 2024

    work page 2024

Show all 60 references
  1. [8]

    H. Wei, Q. Ai, W. Zhao, and Y . Zhang, Real-Time Security Warn- ing and ECU Identification for In-Vehicle Networks , IEEE Sensors Journal, vol. 23, no. 17, pp. 20258–20266, 2023. [Online]. Available: https://doi.org/10.1109/JSEN.2023.3249240

    2023

  2. [9]

    Zniti and N

    A. Zniti and N. Ouazzani, Hardware Implementation of an Enhanced Security- and Authentication-Related Automotive CAN Bus Prototype , International Journal of Electronics and Telecommunications, vol. 69, no. 1, 2023. [Online]. Available: https://baztech.icm.edu.pl/baztech/eleme...

    2023

  3. [10]

    Security of Connected and Automated Vehicles,

    M. Chowdhury, M. Islam, and Z. Khan, “Security of Connected and Automated Vehicles,” The Bridge, vol. 49, no. 3, 2019

    2019

  4. [11]

    Schiller, M

    N. Schiller, M. Chlosta, M. Schloegel, N. Bars, T. Eisenhofer, T. Scharnowski, F. Domke, L. Sch ¨onherr, and T. Holz, Drone Security and the Mysterious Case of DJI’s DroneID , in NDSS Symposium, 2023. [Online]. Available: https://www.ndss-symposium.org/ndss-paper/drone- securi...

    2023

  5. [12]

    Popular Science

    Amazon’s Prime Air Delivery Drones Get a Major FAA Clearance . Popular Science. [Online]. Available: https://www.popsci.com/technology/amazon-drone-bvlos/. Accessed: Oct. 29, 2024

    2024

  6. [13]

    Shafique, A

    A. Shafique, A. Mehmood, and M. Elhadef, Survey of Secu- rity Protocols and Vulnerabilities in Unmanned Aerial Vehicles , IEEE Access, vol. 9, pp. 46927–46948, 2021. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3066778

    2021

  7. [14]

    Top Secret Stealth Drone Was Captured by Iran

    Infographic: How the U.S. Top Secret Stealth Drone Was Captured by Iran . The Aviationist. [Online]. Available: https://theaviationist.com/2011/12/17/drone-infographic/. Accessed: Oct. 29, 2024

    2011

  8. [15]

    Shafik, S

    W. Shafik, S. M. Matinkhah, and F. Shokoor, Cybersecurity in Un- manned Aerial Vehicles: A Review

  9. [16]

    Niyonsaba, K

    S. Niyonsaba, K. Konate, and M. M. Soidridine, A Survey on Cybersecu- rity in Unmanned Aerial Vehicles: Cyberattacks, Defense Techniques and Future Research Directions, International Journal of Computer Networks and Applications, vol. 10, no. 5, p. 688, 2023. [Online]. Availab...

    2023 doi

  10. [17]

    Pyzynski and T

    M. Pyzynski and T. Balcerzak, Cybersecurity of the Unmanned Aircraft System (UAS), Journal of Intelligent & Robotic Systems, vol. 102, no. 2, pp. 1–13, 2021. [Online]. Available: https://doi.org/10.1007/s10846-021- 01399-x

    2021 doi

  11. [18]

    Miller and C

    C. Miller and C. Valasek, A Survey of Remote Automotive Attack Surfaces, Black Hat USA, 2014

    2014

  12. [19]

    K. L. Best, J. Schmid, S. Tierney, J. Awan, N. M. Beyene, M. A. Holliday, R. Khan, and K. Lee, How to Analyze the Cyber Threat from Drones: Background, Analysis Frameworks, and Analysis Tools , RAND Corporation, 2020

    2020

  13. [20]

    Clark, How Thieves Steal Cars Using Vehicle CAN Bus

    L. Clark, How Thieves Steal Cars Using Vehicle CAN Bus . [Online]. Available: https://www.theregister.com/2023/04/06/can injection attack car theft/. Accessed: Oct. 27, 2024

    2023

  14. [21]

    J. Suo, X. Zhang, S. Zhang, W. Zhou, and W. Shi, Feasibility Analysis of Machine Learning Optimization on GPU-Based Low-Cost Edges, 2021 IEEE SmartWorld, Ubiquitous Intelligence & Comput- ing, Advanced & Trusted Computing, Scalable Computing & Com- munications, Internet of Peo...

    2021

  15. [22]

    SmartSAGE: Training Large-Scale Graph Neural Networks Us- ing In-Storage Processing Architectures . ar5iv. [Online]. Available: https://ar5iv.labs.arxiv.org/html/2205.04711. Accessed: Oct. 27, 2024

    2024 arXiv

  16. [23]

    Low-Latency Mini-Batch GNN Inference on CPU- FPGA Heterogeneous Platform . ar5iv. [Online]. Available: https://ar5iv.labs.arxiv.org/html/2206.08536. Accessed: Oct. 27, 2024

    2024 arXiv

  17. [24]

    J. Long, Automobile Electronic Control Network Design Based on CAN Bus , presented at the 2018 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 2018

    2018

  18. [25]

    R. Zhao, G. Qin, and J. Liu, Gateway System for CAN and FlexRay in Automotive ECU Networks , 2010 International Conference on Infor- mation, Networking and Automation (ICINA), 2010, pp. V2-49–V2-53. [Online]. Available: https://doi.org/10.1109/ICINA.2010.5636782

    2010

  19. [26]

    Semantic Scholar

    Intrusion Prevention System of Automotive Network CAN Bus . Semantic Scholar. [Online]. Available: https://www.semanticscholar.org/paper/Intr usion-prevention-system-of-automotive-network-Abbott-McCune-Sha y/ad12844bc47675db22b25901d10ff5168f6808a9. Accessed: Nov. 29, 2024

    2024

  20. [27]

    D. Kim, Y . Song, S. Kwon, H. Kim, J. D. Yoo, and H. K. Kim, UAVCAN Dataset Description. [Online]. Available: http://arxiv.org/abs/2212.09268. Accessed: Jul. 27, 2024

    2024 arXiv

  21. [28]

    H. Lee, S. H. Jeong, and H. K. Kim, OTIDS: A Novel Intrusion Detection System for In-Vehicle Network by Using Remote Frame , presented at the 2017 15th Annual Conference on Privacy, Security and Trust (PST), 2017

    2017

  22. [29]

    H. Ueda, R. Kurachi, H. Takada, T. Mizutani, M. Inoue, and S. Horihata, Security Authentication System for In-Vehicle Network , 2015, pp. 5–9

    2015

  23. [30]

    Boudguiga, W

    A. Boudguiga, W. Klaudel, A. Boulanger, and P. Chiron, A Simple Intrusion Detection Method for Controller Area Network, presented at the 2016 IEEE International Conference on Communications (ICC), 2016

    2016

  24. [31]

    IEEE INTERNET OF THINGS (IOT) JOURNAL 12 InfoconDB

    How to Hack Your Mini Cooper: Reverse Engineering Controller Area Network (CAN) Messages on Passenger Automobiles (DEF CON 21) . IEEE INTERNET OF THINGS (IOT) JOURNAL 12 InfoconDB. [Online]. Available: https://infocondb.org/con/def-con/def-c on-21/how-to-hack-your-mini-cooper-...

    2024

  25. [32]

    Bozdal, M

    M. Bozdal, M. Samie, S. Aslam, and I. Jennions, Evaluation of CAN Bus Security Challenges, Sensors, vol. 20, no. 8, p. 2364, 2020. [Online]. Available: https://doi.org/10.3390/s20082364

    2020 doi

  26. [33]

    Shaghaghi, M

    A. Shaghaghi, M. A. Kaafar, and S. Jha, WedgeTail: An Intrusion Prevention System for the Data Plane of Software Defined Networks , New York, NY , USA, 2017

    2017

  27. [34]

    Kammerer, B

    R. Kammerer, B. Fr ¨omel, and A. Wasicek, Enhancing Security in CAN Systems Using a Star Coupling Router , presented at the 7th IEEE International Symposium on Industrial Embedded Systems (SIES’12), 2012

    2012

  28. [35]

    W. A. Farag, CANTrack: Enhancing Automotive CAN Bus Security Using Intuitive Encryption Algorithms , presented at the 2017 7th International Conference on Modeling, Simulation, and Applied Optimization (ICM- SAO), 2017

    2017

  29. [36]

    Z. Lu, Q. Wang, X. Chen, G. Qu, Y . Lyu, and Z. Liu, LEAP: A Lightweight Encryption and Authentication Protocol for In-Vehicle Communications, presented at the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 2019

    2019

  30. [37]

    W. Wu, R. Li, G. Xie, J. An, Y . Bai, J. Zhou, and K. Li, A Survey of Intrusion Detection for In-Vehicle Networks , IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 3, pp. 919–933, 2020. [Online]. Available: https://doi.org/10.1109/TITS.2019.2908074

    2020

  31. [38]

    Young, J

    C. Young, J. Zambreno, H. Olufowobi, and G. Bloom, Survey of Automotive Controller Area Network Intrusion Detection Systems , IEEE Design & Test, vol. 36, no. 6, pp. 48–55, 2019. [Online]. Available: https://doi.org/10.1109/MDAT.2019.2899062

    2019

  32. [39]

    Cho and K

    K.-T. Cho and K. G. Shin, Fingerprinting Electronic Control Units for Vehicle Intrusion Detection, USA, 2016

    2016

  33. [40]

    IEEE Xplore

    VoltageIDS: Low-Level Communication Characteristics for Automotive Intrusion Detection System . IEEE Xplore. [Online]. Available: https://ie eexplore.ieee.org/document/8306904. Accessed: Jul. 28, 2024

    2024

  34. [41]

    M. L. Han, J. Lee, A. R. Kang, S. Kang, J. K. Park, and H. K. Kim, A Statistical-Based Anomaly Detection Method for Connected Cars in Internet of Things Environment , Berlin, Heidelberg, 2015

    2015

  35. [42]

    Taylor, N

    A. Taylor, N. Japkowicz, and S. Leblanc, Frequency-Based Anomaly Detection for the Automotive CAN Bus , presented at the 2015 World Congress on Industrial Control Systems Security (WCICSS), 2015

    2015

  36. [43]

    Anomaly-Based Intrusion Detection Using the Density Estimation of Re- ception Cycle Periods for In-Vehicle Networks. SAE. [Online]. Available: https://www.sae.org/publications/technical-papers/content/11-01-01-000 3/. Accessed: Jul. 28, 2024

    2024

  37. [44]

    Marchetti, D

    M. Marchetti, D. Stabili, A. Guido, and M. Colajanni, Evaluation of Anomaly Detection for In-Vehicle Networks through Information- Theoretic Algorithms , presented at the 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry (RTSI), 2016

    2016

  38. [45]

    IEEE Xplore

    Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security. IEEE Xplore. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/9091063. Accessed: Jul. 28, 2024

    2024

  39. [46]

    M. S. Salek, P. K. Biswas, J. Pollard, J. Hales, Z. Shen, V . Dixit, M. Chowdhury, S. M. Khan, and Y . Wang, A Novel Hybrid Quantum- Classical Framework for an In-Vehicle Controller Area Network Intrusion Detection, IEEE Access, vol. 11, pp. 96081–96092, 2023. [Online]. Availa...

    2023

  40. [47]

    Theissler, Anomaly Detection in Recordings from In-Vehicle Net- works, 2014

    A. Theissler, Anomaly Detection in Recordings from In-Vehicle Net- works, 2014

    2014

  41. [48]

    Kang and J.-W

    M.-J. Kang and J.-W. Kang, A Novel Intrusion Detection Method Using Deep Neural Network for In-Vehicle Network Security , presented at the 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 2016

    2016

  42. [49]

    A. K. Desta, S. Ohira, I. Arai, and K. Fujikawa, MLIDS: Handling Raw High-Dimensional CAN Bus Data Using Long Short-Term Memory Networks for Intrusion Detection in In-Vehicle Networks, presented at the 2020 30th International Telecommunication Networks and Applications Confere...

    2020

  43. [50]

    Islam, M

    M. Islam, M. Chowdhury, Z. Khan, and S. M. Khan, Hybrid Quantum- Classical Neural Network for Cloud-Supported In-Vehicle Cyberattack Detection, IEEE Sensors Letters, vol. 6, no. 4, pp. 1–4, 2022. [Online]. Available: https://doi.org/10.1109/LSENS.2022.3153931

    2022

  44. [51]

    Baral, R

    B. Baral, R. Majumdar, B. Bhalgamiya, and T. D. Roy, Evaluating Quantum Machine Learning Approaches for Histopathological Cancer Detection: Classical, Hybrid Simulation, and IBM Quantum Computing , in 2023 IEEE International Conference on Quantum Computing and Engineering (QCE...

    2023

  45. [52]

    Majumder, S

    R. Majumder, S. M. Khan, F. Ahmed, Z. Khan, F. Ngeni, G. Comert, J. Mwakalonge, D. Michalaka, and M. Chowdhury, Hybrid Classical- Quantum Deep Learning Models for Autonomous Vehicle Traffic Image Classification Under Adversarial Attack . [Online]. Available: http://arxi v.org/...

    2024 arXiv

  46. [53]

    Mishra, S

    A. Mishra, S. Dey, J. Zhao, M. Wu, B. Li, and K. Madduri, Graphite: A Graph-Based Extreme Multi-Label Short Text Classifier for Keyphrase Recommendation, arXiv.org. [Online]. Available: https://arxiv.org/abs/24 07.20462v1. Accessed: Jul. 31, 2024

    2024

  47. [54]

    [Online]

    Detecting the Onset of a Network Layer DoS Attack with a Graph-Based Approach. [Online]. Available: https://scholar.googleusercontent.com/sc holar?q=cache:kJqdmJgzr6QJ:scholar.google.com/&hl=en&as sdt=0,41. Accessed: Jul. 28, 2024

    2024

  48. [55]

    Paudel, T

    R. Paudel, T. Muncy, and W. Eberle, Detecting DoS Attack in Smart Home IoT Devices Using a Graph-Based Approach, presented at the 2019 IEEE International Conference on Big Data (Big Data), 2019

    2019

  49. [56]

    M. K. Devnath, GCNIDS: Graph Convolutional Network-Based Intru- sion Detection System for CAN Bus . [Online]. Available: http://arxiv.org/ abs/2309.10173. Accessed: Jul. 29, 2024

    2024 arXiv

  50. [57]

    Bosch CAN Specification

  51. [58]

    Brin and L

    S. Brin and L. Page, The Anatomy of a Large-Scale Hypertextual Web Search Engine, Computer Networks and ISDN Systems, vol. 30, no. 1, pp. 107–117, 1998. [Online]. Available: https://doi.org/10.1016/S0169-7 552(98)00110-X

    1998 doi

  52. [60]

    Applied Intelligence

    Multi-Intent Autonomous Decision-Making for Air Combat with Deep Reinforcement Learning. Applied Intelligence. [Online]. Available: https: //link.springer.com/article/10.1007/s10489-023-05058-6?utm source=ch atgpt.com. Accessed: Nov. 29, 2024. BIOGRAPHY SECTION Reek Majumder r...

    doi

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.