机器学习在WSN入侵检测中的应用研究

doi:10.19665/j.issn1001-2400.20231202

Abstract

Abstract:

With the continuous development of computer and communication technologies,networks often face a variety of attacks.The distributed and wireless transmission characteristics of the Wireless Sensor Network(WSN) make it easy to suffer from network attacks,which brings a severe test for the design of the WSN security protection program.As an important means of network attack detection,intrusion detection is a proactive security protection technology and a key technology to ensure the security of WSN network environment.In recent years machine learning methods have made tremendous progress in many fields,and have achieved certain application research results in the field of WSN intrusion detection.In order to facilitate the in-depth study of WSN intrusion detection technology,this paper starts from the characteristics of WSN and the uniqueness of WSN intrusion detection research,and categorizes and synthesizes the relevant research in this field in recent years.First,the challenges and development status of the WSN are briefly introduced.Then,the challenges faced when intrusion detection is designed in WSNs are analyzed based on the characteristics of WSNs.Subsequently,literature review and categorization of research related to intrusion detection in WSNs are conducted,focusing on the categorization and discussion of applied research methods based on machine learning.Finally,the future prospects and directions of this research direction are discussed to provide valuable references for promoting in-depth research and practical applications in the field of WSN intrusion detection.

Key words: wireless sensor network, safety protection, intrusion detection, machine learning

CLC Number:

TP393.08

JIANG Laiwei, GU Haiyang, XIE Lixia, YANG Hongyu. Research on the application of machine learning to intrusion detection in WSN[J].Journal of Xidian University, 2024, 51(4): 206-225.

Figures/Tables 7

文献	改进/使用传统机器学习技术	数据预处理/处理	特征选择 /提取	公开数据集	性能指标
[13]	MSVM		IMOPSO	KDD CUP’99、 CIDD、 NSL-KDD 19	Acc,FP、Number of Features
[14]	FSVM	归一化,PCA	DKFCM	WSN-DS	TP、FP、TN、FN、Precision、FPR、FNR
[15]	SVM				TP、FP、TN、FN、Acc、Recall、Precision
[16]	SVM	归一化	GWO	NSL-KDD’99	Acc、Number of Features、Execution time、DR、FAR
[17]	SVM		RBM	NSL-KDD	Acc、DR、FAR、MAR
[18]	SVM				TP、FP、TN、FN、PPV、TPR、OCA
[19]	SVM			KDD CUP’99	Acc、TPR、FNR、DR
[20]	KNN			UNSW-NB15	Acc、FPR、ROC、CPU running time
[21]	KNN	数值化、归一化		WSN-DS	TP、FP、TN、FN、Acc、FPR、DR、fitness
[22]	KNN	数值化、归一化		NSL-KDD、 UNSW-NB15	TP、FP、TN、FN、Acc、FAR、DR、fitness
[23]	C4.5				Acc、classification accuracy
[24]	C5.0		信息增益、 BiLSTM	KDD CUP’100	TP、FP、TN、FN、FPR、DR、Acc、FNR
[25]	XGBoost			CICIDS 2017、 WSN-DS	TP、FP、TN、FN、Acc、FAR
[26]	XGBoost			CICIDS 2017、 NSL-KDD	TP、FP、TN、FN、Acc、Precision
[27]	LightGBM	SMOTE-Tomek、 Min-Max	递归特征消除、 SHAP	WSN-DS	Acc、TP、FP、TN、FN、Precision、Recall、F-measure
[28]	LightGBM	DATA MAPPING、 Min-Max	SBS	WSN-DS	TP、FP、TN、FN、F-measure、Acc、Recall、F-measure
[29]	LightGBM		互信息	WSN-DS	Acc、DR、FPR、Precision、PT、TT
[30]	LightGBM	独热编码、归一化、 Smote、Tomek-Link	Pearson相关系数、信息增益比	WSN-DS	Precision、Recall、F-measure、Acc、ROC
[31]	RF	归一化		NSL-KDD	TP、FP、TN、FN、TPR、FPR、Precision、Acc
[32]	RF	SMOTE		KDD CUP’99	TP、FP、TN、FN、Acc、Precision、training time、testing time
[33]	KSOM	归一化	PSO		DR、FPR、detection time
[34]	SOM	归一化
[35]	c-means	Min-Max		NSL-KDD	TP、FP、TN、FN、Recall、Acc、Precision、F-measure、Specificity
[36]	k-means	归一化	改进的深度稀疏自编码器	KDD CUP’99	DR、FPR、ROC

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攻击类型	所属层	攻击特点
干扰攻击	物理层	通过产生干扰信号来干扰、破坏或阻止设备的正常通信。
耗尽攻击	链路层	通过消耗目标资源或节点能量使其无法正常运行,主要手段为在报文传输过程中创建路由环路或延长传输路径。
碰撞攻击	链路层	在通信过程中通过干扰导致数据包碰撞或通信失败。这种攻击不会消耗攻击者太多的能量,但是会导致网络中断,且难以被检测。
漏洞攻击	网络层	攻击发生在消息路由过程中,簇头是其攻击的主要目标。该攻击可能导致数据泄露、系统瘫痪、节点被劫持等后果。
Sybil攻击	网络层	通过从单个当前节点创建多个节点id模拟传感器节点的存在,从而获取非法访问和控制权。
黑洞攻击	网络层	攻击者扮演簇头的角色,在BS之前接收传递的数据包并将所有数据包丢弃,从而使数据包无法传输到正确的目的地。
灰洞攻击	网络层	攻击者将对其他节点发出自己作为簇头节点的消息,从而在接收到其他节点的数据包后选择性丢弃一些数据包。
调度攻击	网络层	通过干扰、修改调度的方式或顺序使数据包发生冲突,从而导致数据丢失。
洪泛攻击	传输层	通过向传感器发送大量错误或无效的数据包从而消耗节点能量和网络资源。

数据集	获取途径	特点	缺点
WSN-DS^[56]	https://www.kaggle.com/datasets/bassamkasasbeh1/wsnds	包含18个特征属性和1个标签字段,通过仿真模拟WSN环境收集数据,包含正常数据和4种DoS攻击(Blackhole攻击、Grayhole攻击、Flooding攻击、Scheduling攻击),是WSN的专用数据集。	包含的攻击类型较为局限。
KDD CUP’99^[57]	http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html	包含41个特征属性和1个标签字段,将所有观察结果标记为正常或4种攻击之一(DoS攻击、R2L攻击、U2R攻击、Probe攻击)。	攻击数据是通过仿真和人工数据生成,不是网络真实数据;缺少详细的注释和对攻击类型描述;数据过时,且存在冗余、缺失值。
NSL-KDD^[58]	https://www.unb.ca/cic/datasets/nsl.html	特征属性和标签字段的数量与KDD CUP’99相同,删除了KDD CUP’99中多余的数据。	虽然是KDD CUP’99的改进版本,但是仍然不能完全代表真实的网络数据。
UNSW-NB15^[59]	https://cloudstor.aarnet.edu.au/plus/index.php/s/2DhnLGDdEECo4ys?path=2FUNSW-NB1520-20CSV20Files	共有49个特征,其中包含2个标签字段。包含真实的网络数据和合成的异常流量数据。攻击类型包括Analysus、Backdoor、DoS、Exploits、Fuzzers、Generic、Reconnaissance、Shellcode、Worms。	虽然包含网络真实数据,但是仍然存在合成数据,不能完全反映真实网络环境。
CICIDS 2017^[60]	https://www.unb.ca/cic/datasets/ids-2017.html	包含80个特征和一个标签字段,是基于真实网络环境收集的,包含了多种类型的网络流量数据,也包含多种攻击类型,同时包含详细的特征定义,规模较大。	规模大且分散,包含大量冗余数据。

Research on the application of machine learning to intrusion detection in WSN

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文献	改进/使用深度学习技术	数据预处理/处理	特征选择/提取	公开数据集	性能指标
[24]	BiLSTM		BiLSTM、信息增益	KDD CUP’100	TP、FP、TN、FN、FPR、DR、Acc、FNR
[26]	CGAN			CICIDS 2017、 NSL-KDD	TP、FP、TN、FN、Acc、Precision
[38]	CNN		WOA-ABC	NSL-KDD	Acc、Execution time、DR、FAR、TP、FP、TN、FN
[39]	CNN、GRU	数值化、Min-Max	CNN	WSN-DS	TP、FP、TN、FN、Acc、FPR、TPR
[40]	LSTM	归一化	EECA	CICIDS 2017、 NSL-KDD、 UNSW NB2015	TP、FP、TN、FN、Precision、Acc、FAR、FPR、TPR、Recall、F-measure
[41]	BiLSTM	独热编码	BiLSTM	WSN-DS	TP、FP、TN、FN、Acc、DR、FDR
[42]	LSTM	数值化、Min-Max		WSN-DS	Acc、Precision、Recall、specificity、F-measure
[43]	GRU	数值化、Min-Max		WSN-DS	TP、FP、TN、FN、Acc、Precision、Recall、Specificity、F-measure
[44]	EESNN	核相关方法	BCOMA	WSN-DS	TP、TN、FP、FN、F-measure、Acc、Precision、Specificity、Recall、error rate
[45]	GAN	数值化、 Min-Max、独热编码		WSN-DS、 KDD99、 UNSW-NB15	Precision、Recall、F-measure、FPR
[46]	ANN	Min-Max			TP、FP
[47]	ANN		PSO	WSN-DS	Acc
[48]	ELM	数字化、归一化		NSL-KDD、 UNSW-NB15	TP、FP、TN、FN、Acc、TPR、FPR、FNR
[49]	ELM			CICIDS 2017	TP、FP、TN、FN、Precision、Recall、F-measure、ROC
[50]	CCR-ELM	Min-Max	BCOA	WSN-DS	Acc、Specificity、F-measrue、AUC、Sensitivity、computational time
[51]	MLP	独热编码		WSN-DS	TP、FP、TN、FN、Recall、Precision、FPR、F-measure
[52]	DNN		互相关方法	NSL-KDD	TP、FP、TN、FN、Recall、Precision、Recall、FPR、F-measure
[53]	BPNN			NSL-KDD、 UNSW-NB15	TP、FP、TN、FN、TPR、Recall、Precision、Acc、FPR

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