Machine Learning with Real-time and Small Footprint Anomaly Detection System for In-Vehicle Gateway Article Swipe
Yi Wang
,
Yuanjin Zheng
,
Yajun Ha
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.16369
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2406.16369
Anomaly Detection System (ADS) is an essential part of a modern gateway Electronic Control Unit (ECU) to detect abnormal behaviors and attacks in vehicles. Among the existing attacks, ``one-time`` attack is the most challenging to be detected, together with the strict gateway ECU constraints of both microsecond or even nanosecond level real-time budget and limited footprint of code. To address the challenges, we propose to use the self-information theory to generate values for training and testing models, aiming to achieve real-time detection performance for the ``one-time`` attack that has not been well studied in the past. Second, the generation of self-information is based on logarithm calculation, which leads to the smallest footprint to reduce the cost in Gateway. Finally, our proposed method uses an unsupervised model without the need of training data for anomalies or attacks. We have compared different machine learning methods ranging from typical machine learning models to deep learning models, e.g., Hidden Markov Model (HMM), Support Vector Data Description (SVDD), and Long Short Term Memory (LSTM). Experimental results show that our proposed method achieves 8.7 times lower False Positive Rate (FPR), 1.77 times faster testing time, and 4.88 times smaller footprint.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2406.16369
- https://arxiv.org/pdf/2406.16369
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400023898
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400023898Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2406.16369Digital Object Identifier
- Title
-
Machine Learning with Real-time and Small Footprint Anomaly Detection System for In-Vehicle GatewayWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-06-24Full publication date if available
- Authors
-
Yi Wang, Yuanjin Zheng, Yajun HaList of authors in order
- Landing page
-
https://arxiv.org/abs/2406.16369Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2406.16369Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2406.16369Direct OA link when available
- Concepts
-
Footprint, Anomaly detection, Gateway (web page), Anomaly (physics), Computer science, Real-time computing, Artificial intelligence, Geography, World Wide Web, Archaeology, Physics, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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