基于多属性决策及污点跟踪的大数据平台敏感信息泄露感知方法

doi:10.11959/j.issn.1000-436x.2017143

Abstract

Abstract:

Based on multiple-attribute-decision-making and taint tracking,a sensitive-information leakage awareness method was proposed,some relative known vulnerabilities in big data platform was analyzed,target database was extracted and extended,multiple attribute model was built combined with operation semantic,a grey-correlation-analysis and technique for order preference by similarity to an ideal solution based sensitivity measurement was designed in combination of regular operation semantic for sensitive information.A prototype was built based on taint tracking,sensitive-information leakage vulnerabilities could be verified and discovered across big data platforms in this method.The experiment shows that verification for known bugs and discovery for unknown vulnerabilities can be accomplished based on leakage scenarios,which can be regarded as a support for protection in dynamic sensitive information data flow.

Key words: multi-attributes decision making, taint tracking, big data platform, sensitive information

CLC Number:

TP393

Le-tian SHA,Fu XIAO,Wei CHEN,Jing SUN,Ru-chuan WANG. Sensitive information leakage awareness method for big data platform based on multi-attributes decision-making and taint tracking[J]. Journal on Communications, 2017, 38(7): 56-69.

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References 20

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ISD.A	量化后标准化影响因子
	U:Factor^S(ISD_(i).A)=0.1
ISD_(i).Addr	V:Factor^S(ISD_(i).A)=0.325
	VBU:Factor^S(ISD_(i).A)=0.5
	W:Factor^S(ISD_(i).A)=0.9
	P:Factor^S(ISD_(i).A)=0.9
ISD_(i).F	C:Factor^S(ISD_(i).A)=0.1
	OF:Factor^S(ISD_(i).A)=0.5
	OU:Factor^S(ISD_(i).A)=0.9
ISD_(i).P	IU:Factor^S(ISD_(i).A)=0.5
	IA:Factor^S(ISD_(i).A)=0.1
	N:Factor^S(ISD_(i).A)=0.1
ISD_(i).FI	CoFI:Factor^S(ISD_(i).A)=0.5
	CrFI:Factor^S(ISD_(i).A)=0.9
	N:Factor^S(ISD_(i).A)=0.1
ISD_(i).SR	CoSR:Factor^S(ISD_(i).A)=0.5
	CrSR:Factor^S(ISD_(i).A)=0.9
	NR:Factor^S(ISD_(i).A)=0.1
ISD_(i).IL	RBNW:Factor^S(ISD_(i).A)=0.5
	W:Factor^S(ISD_(i).A)=0.9

对比项	BLP模型	本文方法
模型本质	用来描述系统安全状态及状态转移过程	基于操作规则计算敏感信息动态敏感度
属性定义	主要包括主体、客体、访问属性和判决属性等	主要包括敏感信息、敏感信息的访问属性和判决属性
规则定义	基于状态机定义的系统状态转换规则，用于定义主体请求客体的读、添加、写、执行的访问权	基于敏感信息数据流的常规操作定义的访问规则，并总结不同操作中访问属性的可变区间
安全评估	面向状态机预定义状态的安全级，基于转换规则定义不同状态之间的信息流向，安全性较高	面向敏感信息定义特征化的访问属性，基于多属性决策聚合生成动态安全级，安全性较高
可用评估	为操作系统提供安全策略，但部门之间信息的横向流动被禁止，且缺乏灵活的授权机制	面向大数据平台中的敏感信息给出针对性的敏感度计算方法，在当前目标环境中可用性较好，若兼容其他平台，需要收集并总结相应的常规操作定义及访问属性的可变区间

种类	Hadoop	Storm	Spark
Func	62(3)	31(1)	21(1)
Scr	43(2)	47(0)	53(2)
IntD	87(3)	64(2)	44(3)

漏洞	平台	类型	SL	ST	结果
Vul_A	Hadoop	Func	0.845 1	0.471 2	未知
Vul_B	Hadoop	Func	0.881 3	0.485 3	2016-5393
Vul_C	Hadoop	Func	0.792 9	0.664 5	未知
Vul_D	Hadoop	Scr	0.773 2	0.539 3	2015-7430
Vul_E	Hadoop	Scr	0.839 5	0.611 7	未知
Vul_F	Hadoop	IntD	0.894 8	0.823 2	未知
Vul_G	Hadoop	IntD	0.880 3	0.826 3	2014-3627
Vul_H	Hadoop	IntD	0.911 7	0.845 9	2015-1776
Vul_I	Storm	Func	0.630 2	0.492 2	2015-3188
Vul_J	Storm	IntD	0.908 8	0.853 0	未知
Vul_K	Storm	IntD	0.894 0	0.829 4	未知
Vul_L	Spark	Func	0.695 1	0.447 3	未知
Vul_M	Spark	Scr	0.745 9	0.554 1	未知
Vul_N	Spark	Scr	0.803 5	0.609 6	未知
Vul_O	Spark	IntD	0.914 3	0.833 7	未知
Vul_P	Spark	IntD	0.905 8	0.850 1	未知
Vul_Q	Spark	IntD	0.883 2	0.843 9	未知

污点跟踪方案	污点部署前计算系统的性能损耗/μs	污点部署后计算系统的性能损耗/μs	平均性能损耗
TaintEraser	67.227 1	98.230 6	46.12%
TaintDroid	52.491 7	70.239 1	33.81%
TaintBigData	57.612 0	80.397 5	39.55%

Sensitive information leakage awareness method for big data platform based on multi-attributes decision-making and taint tracking

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