XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones

doi:10.3390/drones9050324

	XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
	Sun, Qian 1,2,3; Zeng, Jie 4; Dai, Lulu 1,2; Hu, Yangliu 5; Tian, Lin 1,2,3
	2025-04-23
发表期刊	DRONES
卷号	9 期号:5 页码:31
摘要	Although deep learning (DL) methods are effective for detecting protocol attacks involving drones in sixth-generation (6G) nonterrestrial networks (NTNs), classifying novel attacks and identifying anomalous sequences remain challenging. The internal capture processes and matching results of DL models are useful for addressing these issues. The key challenges involve obtaining this internal information from DL-based anomaly detection methods, using this internal information to establish new classifications for uncovered protocol attacks and tracing the input back to the anomalous protocol sequences. Therefore, in this paper, we propose an interpretable anomaly classification and identification method for 6G NTN protocols. We design an interpretable anomaly detection framework for 6G NTN protocols. In particular, we introduce explainable artificial intelligence (XAI) techniques to obtain internal information, including the matching results and capture process, and design a collaborative approach involving different detection methods to utilize this internal information. We also design a self-evolving classification method for the proposed interpretable framework to classify uncovered protocol attacks. The rule and baseline detection approaches are made transparent and work synergistically to extract and learn from the fingerprint features of the uncovered protocol attacks. Furthermore, we propose an online method to identify anomalous protocol sequences; this intrinsic interpretable identification approach is based on a two-layer deep neural network (DNN) model. The simulation results show that the proposed classification and identification methods can be effectively used to classify uncovered protocol attacks and identify anomalous protocol sequences, with the precision increasing by a maximum of 32.8% and at least 26%, respectively, compared with that of existing methods.
关键词	drone 6G NTN self-evolving protocol anomaly classification online anomalous protocol identification XAI
DOI	10.3390/drones9050324
收录类别	SCI
语种	英语
资助项目	Beijing Natural Science Foundation ; National Natural Science Foundation of China[62371039] ; [L222004]
WOS研究方向	Remote Sensing
WOS类目	Remote Sensing
WOS记录号	WOS:001496182800001
出版者	MDPI
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42410
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zeng, Jie; Tian, Lin
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Nanjing Inst InforSuperBahn, Nanjing 211100, Peoples R China 4.Beijing Inst Technol, Sch Cyberspace Sci & Technol, Beijing 100081, Peoples R China 5.Zhengzhou Univ, Henan Inst Adv Technol, Zhengzhou 450052, Peoples R China
推荐引用方式 GB/T 7714	Sun, Qian,Zeng, Jie,Dai, Lulu,et al. XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones[J]. DRONES,2025,9(5):31.
APA	Sun, Qian,Zeng, Jie,Dai, Lulu,Hu, Yangliu,&Tian, Lin.(2025).XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones.DRONES,9(5),31.
MLA	Sun, Qian,et al."XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones".DRONES 9.5(2025):31.