HybridSecNet: In-Vehicle Security on Controller Area Networks Through a Hybrid Two-Step LSTM-CNN Model
Bibliographic record
Abstract
The modern Intelligent Vehicle (IV) is a complex technological marvel that heavily relies on the Controller Area Network (CAN) bus system to enable seamless communication among different electronic control units (ECUs). However, the CAN bus system lacks security mechanisms for authentication and authorization, leaving it vulnerable to various attacks. Malicious actors can freely broadcast CAN messages without protection, making the system susceptible to DoS, Fuzzing, and Spoofing attacks. Therefore, it is crucial to devise methods to safeguard modern vehicles from such threats. In this research paper, we introduce HybridSecNet, A hybrid two-step LSTM-CNN Model for Intrusion Detection, a deep learning-based architecture specifically designed to bolster in-vehicle security on Controller Area Networks (CAN). HybridSecNet comprises two stages of classification: the first stage employs long short-term memory (LSTM) to categorize input data as either normal or attacked, and the second stage further classifies the attacks into specific types using Convolutional Neural Networks (CNN). This two-step approach significantly enhances classification accuracy and reliability, yielding remarkable results with accuracy, precision, recall, and an F1-score of approximately 99.5% for CAN bus network attacks. Comparative analyses with existing single-step models underscore the superiority of our proposed model, demonstrating its potential to revolutionize in-vehicle security in the realm of modern intelligent vehicles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".