Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2024
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| author | Vuong, Tuan-Cuong Nguyen, Cong Chi Pham, Van-Cuong Le, Thi-Thanh-Huyen Tran, Xuan-Nam Van Luong, Thien |
| author_facet | Vuong, Tuan-Cuong Nguyen, Cong Chi Pham, Van-Cuong Le, Thi-Thanh-Huyen Tran, Xuan-Nam Van Luong, Thien |
| contents | This paper proposes a novel intrusion detection method for unmanned aerial vehicles (UAV) in the presence of recent actual UAV intrusion dataset. In particular, in the first stage of our method, we design an autoencoder architecture for effectively extracting important features, which are then fed into various machine learning models in the second stage for detecting and classifying attack types. To the best of our knowledge, this is the first attempt to propose such the autoencoder-based machine learning intrusion detection method for UAVs using actual dataset, while most of existing works only consider either simulated datasets or datasets irrelevant to UAV communications. Our experiment results show that the proposed method outperforms the baselines such as feature selection schemes in both binary and multi-class classification tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_02827 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach Vuong, Tuan-Cuong Nguyen, Cong Chi Pham, Van-Cuong Le, Thi-Thanh-Huyen Tran, Xuan-Nam Van Luong, Thien Robotics Artificial Intelligence Machine Learning Signal Processing This paper proposes a novel intrusion detection method for unmanned aerial vehicles (UAV) in the presence of recent actual UAV intrusion dataset. In particular, in the first stage of our method, we design an autoencoder architecture for effectively extracting important features, which are then fed into various machine learning models in the second stage for detecting and classifying attack types. To the best of our knowledge, this is the first attempt to propose such the autoencoder-based machine learning intrusion detection method for UAVs using actual dataset, while most of existing works only consider either simulated datasets or datasets irrelevant to UAV communications. Our experiment results show that the proposed method outperforms the baselines such as feature selection schemes in both binary and multi-class classification tasks. |
| title | Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach |
| topic | Robotics Artificial Intelligence Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2410.02827 |