Detecting Reinforcement Learning-Based Grey Hole Attack in Mobile Wireless Sensor Networks
-
- GAO Boqi
- Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
-
- MAEKAWA Takuya
- Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
-
- AMAGATA Daichi
- Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
-
- HARA Takahiro
- Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University
抄録
<p>Mobile wireless sensor networks (WSNs) are facing threats from malicious nodes that disturb packet transmissions, leading to poor mobile WSN performance. Existing studies have proposed a number of methods, such as decision tree-based classification methods and reputation based methods, to detect these malicious nodes. These methods assume that the malicious nodes follow only pre-defined attack models and have no learning ability. However, this underestimation of the capability of malicious node is inappropriate due to recent rapid progresses in machine learning technologies. In this study, we design reinforcement learning-based malicious nodes, and define a novel observation space and sparse reward function for the reinforcement learning. We also design an adaptive learning method to detect these smart malicious nodes. We construct a robust classifier, which is frequently updated, to detect these smart malicious nodes. Extensive experiments show that, in contrast to existing attack models, the developed malicious nodes can degrade network performance without being detected. We also investigate the performance of our detection method, and confirm that the method significantly outperforms the state-of-the-art methods in terms of detection accuracy and false detection rate.</p>
収録刊行物
-
- IEICE Transactions on Communications
-
IEICE Transactions on Communications E103.B (5), 504-516, 2020-05-01
一般社団法人 電子情報通信学会
- Tweet
詳細情報 詳細情報について
-
- CRID
- 1390848250108846592
-
- NII論文ID
- 130007839127
-
- ISSN
- 17451345
- 09168516
-
- 本文言語コード
- en
-
- データソース種別
-
- JaLC
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可