An Intrusion Detection System for Smart Autonomous E-Bikes
Bibliographic record
Abstract
As the world continues to face environmental challenges, cost-effective ecological mobility solutions are sought. In this context, E-bikes have been gaining popularity. In this context, a Smart Autonomous E-Bike prototype has been implemented at our institution. This Bike can be seen as an IoT (Internet of Things) network of synchronized embedded devices and sensors/actuators that communicate and exchange data. With multiple connected modules that must function safely, securing a Smart Bike becomes crucial to avoid breaches that can cause severe consequences, such as accidents or disclosure of sensitive information. Hence, this paper presents an effective Smart Autonomous Bike Intrusion Detection System (IDS) specifically designed for smart autonomous bikes to address the aforementioned concerns. This IDS is capable of detecting and mitigating various types of cyber threats targeting different modules within the smart bike architecture, including path planning, platooning and Advanced Rider Assistance Systems (ARAS) mechanisms. The proposed system is capable of detecting both network attacks and physical tampering on the hardware components of the bike. An open-source Network-Based IDS (Snort) is first used to detect network intrusions, and an anomaly detection system is used for physical attacks. As an enhancement, the proposed IDS employs an additional hybrid Machine Learning (ML) techniques for anomaly and signature classification to analyse incoming external network traffic to detect intrusion attempts, anomalous activities, and potential security breaches. The enhanced module of the proposed IDS is trained using a comprehensive dataset (Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS)-2017) and assessed using different evaluation parameters.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".