How does the proposed approach compare to existing methods for privacy-preserving intrusion detection in software-defined VANETs?
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The proposed approach for privacy-preserving intrusion detection in software-defined VANETs outperforms existing methods in terms of security and accuracy. The approach combines the advantages of group-based methods and identity-based methods, using a double key approach for trusted communication between vehicles and road side units. This addresses key escrow and exculpability issues that exist in previous schemes . Additionally, the approach utilizes machine learning techniques such as transfer learning and deep learning to improve the performance of intrusion detection. It achieves high accuracy in detecting unknown attacks, small samples, and class imbalance scenarios . The proposed approach also considers the resource constraints of low power IoT networks and implements privacy preservation techniques, resulting in low false alarm rates and energy efficiency . Overall, the proposed approach provides a more secure and accurate solution for privacy-preserving intrusion detection in software-defined VANETs compared to existing methods.
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| The provided paper does not discuss existing methods for privacy-preserving intrusion detection in software-defined VANETs. | |
01 Dec 2022 8 Citations | The paper does not mention anything about privacy-preserving intrusion detection in software-defined VANETs. |
| The provided paper does not discuss privacy-preserving intrusion detection in software-defined VANETs. |
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