跨站脚本XSS(Cross Site Scripting)漏洞已经成为了大多数网站共同面对的Web安全问题,对XSS漏洞的有效预防检测有利于提高Web安全。分析XSS漏洞的攻击原理,指出现有动态分析方法在检测存储型XSS漏洞方面的不足,提出一种有效的存储型漏...跨站脚本XSS(Cross Site Scripting)漏洞已经成为了大多数网站共同面对的Web安全问题,对XSS漏洞的有效预防检测有利于提高Web安全。分析XSS漏洞的攻击原理,指出现有动态分析方法在检测存储型XSS漏洞方面的不足,提出一种有效的存储型漏洞动态检测方法。设计并实现了Stored-XSS漏洞动态检测模型,并在实际的场景下对该模型进行了测试评估,实验证明提出的方法能对存储型XSS漏洞进行有效检测。展开更多
Along with the rapid development of social networks, social network worms have constituted one of the major internet security problems. The root of worm is the inevitable software vulnerability during the design and i...Along with the rapid development of social networks, social network worms have constituted one of the major internet security problems. The root of worm is the inevitable software vulnerability during the design and implementation process of software. So it is hard to completely avoid worms in the existing software engineering systems. Due to lots of bandwidth consumption, the patch cannot be transmitted simultaneously by the network administrator to all hosts. This paper studies how to prevent the propagation of social network worms through the immunization of key nodes. Unlike existing containment models for worm propagation, a novel immunization strategy is proposed based on network vertex influence. The strategy selects the critical vertices in the whole network. Then the immunization is applied on the selected vertices to achieve the maximal effect of worm containment with minimal cost. Different algorithms are implemented to select vertices. Simulation experiments are presented to analyze and evaluate the performance of different algorithms.展开更多
Using degree distribution to assess network vulnerability represents a promising direction of network analysis.However,the traditional degree distribution model is inadequate for analyzing the vulnerability of spatial...Using degree distribution to assess network vulnerability represents a promising direction of network analysis.However,the traditional degree distribution model is inadequate for analyzing the vulnerability of spatial networks because it does not take into consideration the geographical aspects of spatial networks.This paper proposes a spatially weighted degree model in which both the functional class and the length of network links are considered to be important factors for determining the node degrees of spatial networks.A weight coefficient is used in this new model to account for the contribution of each factor to the node degree.The proposed model is compared with the traditional degree model and an accessibility-based vulnerability model in the vulnerabil-ity analysis of a highway network.Experiment results indicate that,although node degrees of spatial networks derived from the tra-ditional degree model follow a random distribution,node degrees determined by the spatially weighted model exhibit a scale-free distribution,which is a common characteristic of robust networks.Compared to the accessibility-based model,the proposed model has similar performance in identifying critical nodes but with higher computational efficiency and better ability to reveal the overall vulnerability of a spatial network.展开更多
文摘跨站脚本XSS(Cross Site Scripting)漏洞已经成为了大多数网站共同面对的Web安全问题,对XSS漏洞的有效预防检测有利于提高Web安全。分析XSS漏洞的攻击原理,指出现有动态分析方法在检测存储型XSS漏洞方面的不足,提出一种有效的存储型漏洞动态检测方法。设计并实现了Stored-XSS漏洞动态检测模型,并在实际的场景下对该模型进行了测试评估,实验证明提出的方法能对存储型XSS漏洞进行有效检测。
基金supported by Fundamental Research Funds of the Central Universities under Grant no. N120317001 and N100704001Program for New Century Excellent Talents in University (NCET13-0113)+1 种基金Natural Science Foundation of Liaoning Province of China under Grant no. 201202059Program for Liaoning Excellent Talents in University under LR2013011
文摘Along with the rapid development of social networks, social network worms have constituted one of the major internet security problems. The root of worm is the inevitable software vulnerability during the design and implementation process of software. So it is hard to completely avoid worms in the existing software engineering systems. Due to lots of bandwidth consumption, the patch cannot be transmitted simultaneously by the network administrator to all hosts. This paper studies how to prevent the propagation of social network worms through the immunization of key nodes. Unlike existing containment models for worm propagation, a novel immunization strategy is proposed based on network vertex influence. The strategy selects the critical vertices in the whole network. Then the immunization is applied on the selected vertices to achieve the maximal effect of worm containment with minimal cost. Different algorithms are implemented to select vertices. Simulation experiments are presented to analyze and evaluate the performance of different algorithms.
基金Supported by the Institute of Crustal Dynamics Funds (No. ZDJ2009‐01, No. ZDJ2007‐13)
文摘Using degree distribution to assess network vulnerability represents a promising direction of network analysis.However,the traditional degree distribution model is inadequate for analyzing the vulnerability of spatial networks because it does not take into consideration the geographical aspects of spatial networks.This paper proposes a spatially weighted degree model in which both the functional class and the length of network links are considered to be important factors for determining the node degrees of spatial networks.A weight coefficient is used in this new model to account for the contribution of each factor to the node degree.The proposed model is compared with the traditional degree model and an accessibility-based vulnerability model in the vulnerabil-ity analysis of a highway network.Experiment results indicate that,although node degrees of spatial networks derived from the tra-ditional degree model follow a random distribution,node degrees determined by the spatially weighted model exhibit a scale-free distribution,which is a common characteristic of robust networks.Compared to the accessibility-based model,the proposed model has similar performance in identifying critical nodes but with higher computational efficiency and better ability to reveal the overall vulnerability of a spatial network.
