Summary of Cav-ad: a Robust Framework For Detection Of Anomalous Data and Malicious Sensors in Cav Networks, by Md Sazedur Rahman et al.
CAV-AD: A Robust Framework for Detection of Anomalous Data and Malicious Sensors in CAV Networks
by Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
First submitted to arxiv on: 7 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed novel framework, called CAV-AD, is designed to address security threats in connected and automated vehicles (CAVs) by detecting anomalies in sensor readings. The framework consists of two main components: a novel CNN model architecture, optimized omni-scale CNN (O-OS-CNN), which optimizes time scale selection for input time series data; and an amplification block to enhance sensitivity for detecting anomalies. CAV-AD also integrates the proposed O-OS-CNN with a Kalman filter to identify malicious sensors. The framework is trained on real-world datasets containing both instant and constant attacks, achieving an average accuracy of 98% and an average F1 score of 89%. These results outperform state-of-the-art methods in detecting intrusions from multiple anomalies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CAVs are special vehicles that can drive themselves and connect to the internet. They’re useful for things like transporting people or goods, but they also make it easier for bad guys to hack into them. This paper talks about a new way to keep CAVs safe by detecting when someone is trying to hack into them. The method uses special computer algorithms that can look at lots of data and find the bad stuff. It’s really good at finding the bad hackers and figuring out which sensors they’re using to do their dirty work. This makes it easier to catch the hackers and keep the CAVs safe. |
Keywords
» Artificial intelligence » Cnn » F1 score » Time series
