面向TDMA无线传感器网络的攻击构造与入侵检测方法研究

doi:10.19678/j.issn.1000-3428.0069432

摘要/Abstract

摘要：

无线介质的开放性使得信息安全成为其应用中的挑战性问题之一。时分多址(TDMA)协议是面向工业等时延敏感应用的主要协议。针对TDMA无线传感器网络的时隙调度特征, 提出空闲时隙攻击模型、重传时隙攻击模型这2种伪装攻击模型, 并针对这2种攻击模型, 从TDMA无线传感器网络自身的传输特点出发, 考虑网络传输的周期性及传输基本单元为一个时隙的特点, 提出一种基于细粒度时间特征提取的高精度入侵检测方法。首先, 利用数据包接收时间、超帧开始时间等信息进行时间维度上的细粒度特征提取, 计算传输时隙的位置信息; 然后, 将位置信息输入无监督模型IF(Isolation Forest)进行训练和学习; 最后, 对在一个超帧周期内收到的同一个节点的序列号相同的2个数据包进行合法性判断。实验结果表明, 所提2种伪装攻击能够逃避现有的入侵检测方法, 而所提入侵检测方法可以有效检测出这2种伪装攻击, 相较于传统方法, 该方法在丢包率为30%时检测成功率提升14.5%。

关键词: 无线传感器网络, 时分多址协议, 伪装攻击, 入侵检测, 细粒度时间特征提取

Abstract:

The open nature of wireless media poses a challenge for information security. The Time Division Multiple Access (TDMA) protocol is a predominant protocol tailored for time-sensitive industrial applications. Considering the time-slot scheduling characteristics of TDMA-based wireless sensor networks, this study proposes two types of masquerade attack models: an idle time-slot attack model and a retransmission time-slot attack model. In response to these two attack models and starting from the inherent transmission features of TDMA wireless sensor networks while considering their periodic transmission pattern and the fundamental transmission unit being a single time slot, a high-precision intrusion detection method based on fine-grained temporal feature extraction is proposed. First, fine-grained temporal features are extracted in the time dimension by leveraging information such as packet reception time and superframe start time to calculate the positional information of the transmission time slot. Subsequently, the positional information is fed into the Isolation Forest (IF)-an unsupervised learning model-for training and learning. Finally, a legitimacy assessment is conducted on two data packets received from the same node within one superframe cycle that have identical sequence numbers. The experimental results demonstrate that the two proposed masquerade attacks can evade existing intrusion detection methods and the proposed intrusion detection approach can effectively detect these two masquerade attacks. Compared to traditional methods, this approach achieves a 14.5% increase in the detection success rate when the packet loss rate is 30%.

Key words: wireless sensor networks, Time Division Multiple Access(TDMA)protocol, masquerade attack, intrusion detection, fine-grained time feature extraction

温敏初, 梁炜, 张嘉麟. 面向TDMA无线传感器网络的攻击构造与入侵检测方法研究[J]. 计算机工程, 2025, 51(8): 203-214.

WEN Minchu, LIANG Wei, ZHANG Jialin. Research on Attack Construction and Intrusion Detection Methods for TDMA Wireless Sensor Networks[J]. Computer Engineering, 2025, 51(8): 203-214.

https://www.ecice06.com/CN/Y2025/V51/I8/203

图/表 17

图1 TDMA超帧

Fig.1 TDMA superframe

图2 网络模型

Fig.2 Network model

图3 空闲时隙攻击模型

Fig.3 Idle time-slot attack model

图4 计算开始发动空闲时隙攻击的时间

Fig.4 Calculate the start time of launching idle time-slot attacks

图5 重传时隙攻击模型

Fig.5 Retransmission time-slot attack model

图6 入侵检测框架

Fig.6 Framework of intrusion detection

图7 IF方法训练过程

Fig.7 Training process of IF method

图8 无线传感器网络物理实验平台

Fig.8 Wireless sensor network physical experiment platform

图9 重传时隙攻击的DSR值

Fig.9 DSR values for retransmission time-slot attack

图10 重传时隙攻击的DFR值

Fig.10 DFR values for retransmission time-slot attack

图11 不同φ值下的重传时隙攻击DSR值

Fig.11 DSR values of retransmission time-slot attack under different values of φ

图12 不同丢包率下重传时隙攻击的平均DFR值

Fig.12 Average DFR values of retransmission time-slot attacks under different packet loss rates

图13 空闲时隙攻击的DSR值

Fig.13 DSR values of idle time-slot attack

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