| 研究課題/領域番号 |
24K07482
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分21020:通信工学関連
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| 研究機関 | 東京電機大学 |
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研究代表者 |
朱 金暁 東京電機大学, システム デザイン 工学部, 助教 (30754329)
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| 研究期間 (年度) |
2024-04-01 – 2027-03-31
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| 研究課題ステータス |
交付 (2024年度)
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| 配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2026年度: 780千円 (直接経費: 600千円、間接経費: 180千円)
2025年度: 1,820千円 (直接経費: 1,400千円、間接経費: 420千円)
2024年度: 1,950千円 (直接経費: 1,500千円、間接経費: 450千円)
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| キーワード | Physical Layer Security / Wi-Fi / Radio Frequency / Device Identification / RF Fingerprint / Device Authentication / Wireless Security / IoT Network |
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| 研究開始時の研究の概要 |
IoT技術の進化に伴い、新しい無線技術の導入により通信のセキュリティ問題が再度注目されています。このプロジェクトの目的は、6G時代のIoTシステムのセキュリティ問題を解決するための物理層認証スキームを設計することです。特に、WiFi 6とミリ波通信に焦点を当てます。研究では、実際のIoTデバイスからデータを収集し、認証用の電波特徴を提案し、その性能を検証します。このプロジェクトが次世代IoTシステムの開発に貢献することを期待しています。
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| 研究実績の概要 |
This project addresses security challenges posed by IoT devices in next-generation wireless networks. As advanced technologies create wider attack surfaces, we investigate physical layer authentication as a robust security solution that complements traditional cryptographic methods. By now, we have made significant progress in building the foundation for this work:
(1) Experimental System Development: We developed a Wi-Fi testbed using software-defined radio (GNU Radio) to analyze OFDM signal characteristics. This system enables the capture of unique hardware fingerprints from IoT devices during wireless transmissions.
(2) Dataset Collection and Analysis: We collected a comprehensive dataset of physical layer features from Wi-Fi 3 (802.11g)-based IoT devices. The dataset includes frequency offset (caused by mismatched local oscillators), I/Q imbalance, channel state information (CSI), and other radiometric features.
(3) Methodology Development: We designed an approach to extract and analyze these physical layer features for device fingerprinting. State-of-the-art clustering algorithms have been applied to classify known and unknown devices based on their feature patterns.
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| 現在までの達成度 |
現在までの達成度
2: おおむね順調に進展している
理由
According to our research proposal, the primary objectives for the first year are: (1) to develop an experimental testbed, and (2) to construct a dataset of radiometric features. As of now, we have established the experimental system, built a dataset capturing key physical-layer features, and (3) proposed several device identification algorithms based on machine learning to classify transmitters using the collected data.
While the progress appears to be ahead of schedule, it is important to note that the current dataset is based on 802.11g communications, rather than the targeted Wi-Fi 6+ standards. To address this, I plan to extend the data collection system to support Wi-Fi 4 (802.11n) in the next phase, within the originally scheduled period for Task 1. This extension is essential, as 802.11n introduces MIMO technologies that are also fundamental to Wi-Fi 6+ environments.
In conclusion, the project is progressing smoothly and remains well-aligned with the proposed timeline.
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| 今後の研究の推進方策 |
To further advance the research, I plan to extend the current experimental system to support Wi-Fi 4 (802.11n) and beyond, enabling the analysis of physical-layer features in MIMO-enabled environments. This will allow us to more accurately simulate and evaluate scenarios relevant to Wi-Fi 6 and future standards. Additionally, we will expand the dataset by collecting signals from a broader range of IoT devices and conditions. Building on the current identification algorithms, we also aim to explore advanced classification and anomaly detection techniques using deep learning to improve the robustness and scalability of physical layer authentication.
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