Figure/Table detail
Research on airborne CAN intrusion detection of UAV based on deep learning
Journal of Cybersecurity,
DOI: 10.20172/j.issn.2097-3136.250115
| 消息类型 | 精确率 | 召回率 | F1分数 |
| DoS_GNSS | 98.89% | 98.00% | 98.44% |
| DoS_propulsion | 98.90% | 99.10% | 99.00% |
| FuzzyAttack_GNSS | 82.19% | 87.12% | 84.58% |
| FuzzyAttack_propulsion | 98.99% | 98.40% | 98.70% |
| Normal_GNSS | 82.87% | 76.75% | 79.69% |
| Normal_propulsion | 73.78% | 86.10% | 79.46% |
| ReplayAttack_GNSS | 98.79% | 98.20% | 95.50% |
| ReplayAttack_propulsion | 82.71% | 70.30% | 76.00% |
Table 3 UAV equipment configuration
Other figure/table from this article
