基于BP神经网络的Wi-Fi安全评价模型的研究

doi:10.11959/j.issn.1000-436x.2016247

Abstract

Abstract:

A Wi-Fi security evaluation model was proposed based on BP neural network.Firstly a large number of Wi-Fi hotspots was analyzed,safety information sources about wireless hotspots were selected.Secondly,a model of BP neural network was constructed,the weights were adjusted,and the evaluation system for Wi-Fi safety was formed,which was higher credibility.Finally,on the Android flatform the application based on above theory was designed.A lot of experi-ments show the model is able to scan and evaluate the wireless hotspot around user.It can also provide the disconnect function on unsafe wireless hotspot.

Key words: Wi-Fi, intelligent protection shield, Android, Wi-Fi security evaluation

Hai-yan KANG,Han YAN,Hao-ran HUANG,Xuan1 SUN. Wi-Fi security evaluation model based on BP neural network[J]. Journal on Communications, 2016, 37(Z1): 50-56.

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References 14

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序号	类型	属性名	属性解释
1	字符型（public string）	接入点地址（BSSID）	接入点地址
2	字符型（public string）	Wi-Fi名称（SSID）	网络名称
3	字符型（public string）	加密（capabilities）	接入点支持的身份验证、密钥管理和加密方案
4	整型（public int）	信号频率（frequency）	用户接入网络的信道频率（MHz）
5	整型（public int）	信号强度（level）	被测信号强度（dBm）
6	长整型（public long）	时间戳（timestamp）	同步时间戳（ms）

等级	说明
安全	Wi-Fi网络具有较强的安全保障能力，应用安全
基本安全	Wi-Fi网络具有一定的安全保障能力，应用基本安全
不安全	Wi-Fi网络安全保障能力有限，应用存在安全隐患
危险（很不安全）	Wi-Fi网络安全保障能力较差，应用存在安全形势严峻

组别	频率/GHz	强度/dBm	加密类型	输入节点1（权值）	输入节点2（权值）	输入节点3（权值）	期望输出
1	2 480	?55	WPA2	0.8	0.9	1	安全（1,1）
2	2 467	?70	WPA	0.67	0.6	0.67	基本安全（1,?1）
3	2 438	?80	WEP	0.38	0.4	0.33	不安全（?1,1）
4	2 412	?90	ESS	0.12	0.2	0	危险（?1,?1）
5	2 497	?76	WEP	0.97	0.48	0.33	安全（1,1）
6	2 419	?62	WPA	0.19	0.76	0.67	基本安全（1,?1）

测试地点	Wi-Fi名称	信号强度/ dBm	信号频率/GHz	加密	安全系数	准确性	准确率
	Hhr	?33	2.4	是	安全	√
	TP-LINK_601	?45	2.4	是	安全	√
图书馆	System Attack	?67	2.4	是	危险	×	83.3%
	Saierhao	?84	2.4	是	基本安全	√
	TP-Link_7FF76	?88	2.4	是	基本安全	√
	53637389	?85	2.4	是	基本安全	√
	Hhr	?23	2.4	是	安全	√
	teacher li lab	?60	2.4	是	基本安全	√
实验楼	Bistu	?79	2.4	是	基本安全	√	80%
	CMCC-AUTO	?76	2.4	是	不安全	×
	CMCC	?76	2.4	是	基本安全	√

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Wi-Fi security evaluation model based on BP neural network

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