| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-10-23 |
| タイトル |
|
|
タイトル |
Efficient IDS for IoT Networks Using Host-Based Data Aggregation and Multi-Entropy Analysis |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
IoT |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
machine learning |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
network security |
| キーワード |
|
|
主題Scheme |
Other |
|
主題 |
malware |
| 資源タイプ |
|
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
| 著者 |
Katsura, Yusei
遠藤, 新
新井, イスマイル
藤川, 和利
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
T IoT devices have limited computational resources, posing challenges to implementing adequate security measures. As a result, numerous attacks targeting vulnerabilities in IoT devices have been observed. Against this backdrop, research on Intrusion Detection Systems (IDSs) leveraging machine learning in IoT environments has been actively conducted. However, packet-based and flow-based IDSs proposed in existing studies are vulnerable to attacks such as DoS and DDoS, which involve numerous packet or flow combination patterns. These methods also face challenges related to computational resource burdens caused by the increased volume of input data. This study proposes a lightweight IDS with the hostbased approach, representing communication behaviors with multiple entropies. The host-based approach aggregates features from different communications sent by the same host, enabling a reduction in input data. Additionally, the method captures host-level communication behaviors by leveraging multiple entropies, focusing on characteristic patterns of IoT devices, such as periodic communication with specific servers during normal operation. This enables the reduction of computational resources during detection processing while maintaining detection accuracy, even when using fewer features and lightweight machine learning algorithms. The evaluation results demonstrate that the proposed method achieves a maximum reduction of 99.7% (2916 milliseconds) in processing time and 86.4% (633 MiB) in memory usage while maintaining an intrusion detection accuracy of 99.97%, proving its feasibility in constrained environments comparable to IoT gateways. |
| 書誌情報 |
en : IEEE Access
巻 13,
p. 12546-125419,
ページ数 14,
発行日 2025-07-14
|
| 出版者 |
|
|
出版者 |
IEEE |
| ISSN |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
2169-3536 |
| 出版者版DOI |
|
|
関連タイプ |
isReplacedBy |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1109/ACCESS.2025.3589057 |
| 出版者版URI |
|
|
関連タイプ |
isReplacedBy |
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|
識別子タイプ |
URI |
|
|
関連識別子 |
https://ieeexplore.ieee.org/document/11080017 |
| 権利 |
|
|
権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
©2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. |
| 著者版フラグ |
|
|
出版タイプ |
NA |
