Research on Attack Construction and Intrusion Detection Methods for TDMA Wireless Sensor Networks

doi:10.19678/j.issn.1000-3428.0069432

Abstract

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

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

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.

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.

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Figures/Tables 17

Fig.1 TDMA superframe

Fig.2 Network model

Fig.3 Idle time-slot attack model

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

Fig.5 Retransmission time-slot attack model

Fig.6 Framework of intrusion detection

Fig.7 Training process of IF method

Fig.8 Wireless sensor network physical experiment platform

Fig.9 DSR values for retransmission time-slot attack

Fig.10 DFR values for retransmission time-slot attack

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

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

Fig.13 DSR values of idle time-slot attack

References 26

1	YU J Y , LEE E , OH S R , et al. A survey on security requirements for WSNs: focusing on the characteristics related to security. IEEE Access, 2020, 8, 45304- 45324. doi: 10.1109/ACCESS.2020.2977778
2	WANG J W , ZHANG P , TANG L , et al. Intelligent passive eavesdropping in massive MIMO-OFDM systems via reinforcement learning. IEEE Wireless Communications Letters, 2022, 11 (6): 1248- 1252. doi: 10.1109/LWC.2022.3163268
3	BALAKRISHNAN S, WANG P, BHUYAN A, et al. Modeling and analysis of eavesdropping attack in 802.11ad mmWave wireless networks[J]. IEEE Access, 2019, 7: 70355-70370.
4	MPITZIOPOULOS A , GAVALAS D , KONSTANTOPOULOS C , et al. A survey on jamming attacks and countermeasures in WSNs. IEEE Communications Surveys & Tutorials, 2009, 11 (4): 42- 56.
5	STOJANOVSKI S, KULAKOV A. Efficient attacks in industrial wireless sensor networks[EB/OL]. [2023-10-05]. https://link.springer.com/chapter/10.1007/978-3-319-09879-1_29.
6	徐宜敏, 孙子文. 工业无线传感器网络中干扰攻击的入侵检测. 传感技术学报, 2017, 30 (11): 1693- 1699.
	XU Y M , SUN Z W . Intrusion detection of jamming attack in industrial wireless sensor networks. Chinese Journal of Sensors and Actuators, 2017, 30 (11): 1693- 1699.
7	CHENG X, SHI J Y, SHA M, et al. Launching smart selective jamming attacks in WirelessHART networks[C]//Proceedings of the IEEE INFOCOM Conference on Computer Communications. Washington D.C., USA: IEEE Press, 2021: 1-10.
8	YANG W , WAN Y D , WANG Q . Enhanced secure time synchronisation protocol for IEEE802.15.4e-based industrial Internet of Things. IET Information Security, 2017, 11 (6): 369- 376. doi: 10.1049/iet-ifs.2016.0232
9	张思超, 梁炜, 苑旭东, 等. 面向工业无线网络的时间同步攻击检测. 物联网学报, 2023, 7 (2): 88- 97.
	ZHANG S C , LIANG W , YUAN X D , et al. Time synchronization attack detection for industrial wireless network. Chinese Journal on Internet of Things, 2023, 7 (2): 88- 97.
10	ZENG Y, ZHANG R. Active eavesdropping via spoofing relay attack[C]//Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Washington D.C., USA: IEEE Press, 2016: 2159-2163.
11	NA X, GUO X Z, HE Y, et al. Wi-attack: cross-technology impersonation attack against iBeacon services[C]//Proceedings of the 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). Washington D.C., USA: IEEE Press, 2021: 1-9.
12	ANTONIOLI D, TIPPENHAUER N O, RASMUSSEN K. BIAS: bluetooth impersonation attacks[C]//Proceedings of the IEEE Symposium on Security and Privacy (SP). Washington D.C., USA: IEEE Press, 2020: 549-562.
13	霍文君, 王伟, 李文. AnomalyDetect: 一种基于欧式距离的在线异常检测算法. 中国科学技术大学学报, 2019, 49 (7): 555-563, 571.
	HUO W J , WANG W , LI W . AnomayDetect: an online distance-based anomaly detection algorithm. Journal of University of Science and Technology of China, 2019, 49 (7): 555-563, 571.
14	FENG H L , LIANG L , LEI H . Distributed outlier detection algorithm based on credibility feedback in wireless sensor networks. IET Communications, 2017, 11 (8): 1291- 1296. doi: 10.1049/iet-com.2016.0986
15	SINGH A , NAGAR J , SHARMA S , et al. A Gaussian process regression approach to predict the k-barrier coverage probability for intrusion detection in wireless sensor networks. Expert Systems with Applications, 2021, 172, 114603. doi: 10.1016/j.eswa.2021.114603
16	MA C, YANG X H. A hierarchical intrusion detection model in wireless sensor networks[C]//Proceedings of the 4th IEEE International Conference on Electronics and Communication Engineering (ICECE). Washington D.C., USA: IEEE Press, 2021: 215-219.
17	孙扬威, 戚湧. 基于聚类混合采样与PSO-Stacking的车载CAN入侵检测方法. 计算机工程, 2023, 49 (1): 138- 145. doi: 10.19678/j.issn.1000-3428.0064949
	SUN Y W , QI Y . Intrusion detection method for in-vehicle CAN based on cluster mixed sampling and PSO-Stacking. Computer Engineering, 2023, 49 (1): 138- 145. doi: 10.19678/j.issn.1000-3428.0064949
18	AL-QATF M , YU L S , AL-HABIB M , et al. Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access, 2018, 6, 52843- 52856.
19	丁庆丰, 李晋国. 一种物联网环境下的分布式异常流量检测方案. 计算机工程, 2022, 48 (8): 152- 159. doi: 10.19678/j.issn.1000-3428.0063284
	DING Q F , LI J G . A distributed abnormal traffic detection scheme in Internet of Things environment. Computer Engineering, 2022, 48 (8): 152- 159. doi: 10.19678/j.issn.1000-3428.0063284
20	COOK A A , MISIRLI G , FAN Z . Anomaly detection for IoT time-series data: a survey. IEEE Internet of Things Journal, 2020, 7 (7): 6481- 6494.
21	GAO C, CHEN Y P, WANG Z M, et al. Anomaly detection frameworks for outlier and pattern anomaly of time series in wireless sensor networks[C]//Proceedings of the International Conference on Networking and Network Applications (NaNA). Washington D.C., USA: IEEE Press, 2020: 229-232.
22	LIU Y X , WANG J , LI J Q , et al. Machine learning for the detection and identification of Internet of Things devices: a survey. IEEE Internet of Things Journal, 2022, 9 (1): 298- 320.
23	LIANG W , ZHENG M , ZHANG J L , et al. WIA-FA and its applications to digital factory: a wireless network solution for factory automation. Proceedings of the IEEE, 2019, 107 (6): 1053- 1073.
24	LIU F T, TING K M, ZHOU Z H. Isolation Forest[C]//Proceedings of the 8th IEEE International Conference on Data Mining. Washington D.C., USA: IEEE Press, 2008: 413-422.
25	DAVIS J, GOADRICH M. The relationship between precision-recall and ROC curves[C]//Proceedings of the 23rd International Conference on Machine Learning. New York, USA: ACM Press, 2006: 233-240.
26	FAWCETT T . An introduction to ROC analysis. Pattern Recognition Letters, 2006, 27 (8): 861- 874.

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