Jingyuan Li

Stochastic game net and applications in security analysis for enterprise network

Stochastic game theoretic framework has been used in many fields of networks with interactive behaviors. However, further use of this framework is limited due to the following reasons. Firstly, it is difficult to build comprehensive and rigorous models for complex network structures by the state-based game model. Secondly, solving and extending the dynamic behaviors of participators of the network are nearly impossible, because of the complexity of state transitions. Last but not least, general game model is not able to describe and analyze specific events and behaviors in some kinds of networks, like enterprise networks. In this paper, we propose a new modeling paradigm (stochastic game net, or SGN) for stochastic games representation with Petri nets. Based on our graphical tool, stochastic game problems can be described clearly, and the model can be solved and extended easily. Moreover, this paper puts forth a series of methods for modeling and analyzing the competitive game by SGN, which is the main contribution of this work. Our achievements are applied to the security analysis for enterprise networks. The analysis results prove the powerful ability of our achievements in solving the complicated and dynamic game problems. Furthermore, our approaches can be used to calculate the existence and the value of an equilibrium point.

Modeling and security analysis of enterprise network using attack–defense stochastic game Petri nets

In this paper, we propose a novel modeling method attack–defense stochastic game Petri nets (or ADSGN) to model and analyze the security issues in enterprise network. We firstly give the definition and modeling method algorithm of ADSGN and then propose the algorithm of the strategy. The proposed ADSGN method is successfully applied to describe the attack and defense courses in the enterprise network. Finally, we analyze the mean time to first security breach and the mean time to security breach in the enterprise network quantifiably, and proved that our method can also be applied to other areas with respect to game issues. Copyright

Comprehensive Quantitative Analysis on Privacy Leak Behavior

Privacy information is prone to be leaked by illegal software providers with various motivations. Privacy leak behavior has thus become an important research issue of cyber security. However, existing approaches can only qualitatively analyze privacy leak behavior of software applications. No quantitative approach, to the best of our knowledge, has been developed in the open literature. To fill this gap, in this paper we propose for the first time four quantitative metrics, namely, possibility, severity, crypticity, and manipulability, for privacy leak behavior analysis based on Privacy Petri Net (PPN). In order to compare the privacy leak behavior among different software, we further propose a comprehensive metric, namely, overall leak degree, based on these four metrics. Finally, we validate the effectiveness of the proposed approach using real-world software applications. The experimental results demonstrate that our approach can quantitatively analyze the privacy leak behaviors of various software types and reveal their characteristics from different aspects.

Evolutionary analysis on online social networks using a social evolutionary game

In this paper, we propose a social evolutionary game to investigate the evolution of social networks. Through comparison between simulation and empirical analysis on the social networks of Twitter and Sina Weibo, we validate the effectiveness of the proposed model and estimate the evolutionary phases of the two networks. We find that the users of Sina Weibo can withstand comparatively more costs than the users of Twitter. Therefore, they can perform more positive behavior and consider more about their reputation than Twitter users. Moreover, the evolutionary time of Sina Weibo to a stable state is longer than that of Twitter.

Privacy Petri Net and Privacy Leak Software

Private information leak behavior has been widely discovered in malware and suspicious applications. We refer to such software as privacy leak software (PLS). Nowadays, PLS has become a serious and challenging problem to cyber security. Previous methodologies are of two categories: one focuses on the outbound network traffic of the applications; the other dives into the inside information flow of the applications. We present an abstract model called Privacy Petri Net (PPN) which is more applicable to various applications and more intuitive and vivid to users. We apply our approach to both malware and suspicious applications in real world. The experimental result shows that our approach can effectively find categories, content, procedure, destination and severity of the private information leaks for the target software.

Hierarchy-Based Link Prediction in Knowledge Graphs

Link prediction over a knowledge graph aims to predict the missing entity h or t for a triple (h,r,t). Existing knowledge graph embedding based predictive methods represent entities and relations in knowledge graphs as elements of a vector space, and employ the structural information for link prediction. However, knowledge graphs contain many hierarchical relations, which existing methods have pay little attention to. In this paper, we propose a hierarchy-constrained locally adaptive knowledge graph embedding based link prediction method, called hTransA, by integrating hierarchical structures into the predictive work. Experiments over two benchmark data sets demonstrate the superiority of hTransA.

Mechanism Analysis of Competitive Information Synchronous Dissemination in Social Networks

Different group of information, such as advertising and product promotion, compete with each other as they diffuse over social networks. Most of the existing methods analyze the dissemination mechanism mainly upon the information itself, without considering human characteristics. This paper uses a framework of social evolutionary game to simulate the dissemination and adjusts utility function and updating mechanism based on coordination game. We find that individuals consider more about their own reputation and more communication between them, individuals are more cautious in the face of strategy choice. When the benefit of competitive information is nearly 1.2 times of the original one, it can make up the loss of reputation caused by changing strategy. For the specific network environment based on simulation, the actual data on Sina Weibo strongly verify this rule and shows that factor of reputation promotes the cooperation and users won’t easily change their information.

The Competition of User Attentions Among Social Network Services: A Social Evolutionary Game Approach

As the total amount of users in the social network services approach the total amount of netizens, social network service providers have to compete with each other for the attention of the existing users, rather than attracting totally new users without any social network service using experiences. Most of the current game theoretical studies on social network services focus on the evolution of the cooperation/defection between users, and the evolution of the structure of the networks for a single social networks, and fail to consider the competition of multiple social networks over a same and stable users group and their user attentions. In this paper, we propose a competitive social evolutionary game model to describe the competition of user attentions among social network services. We introduce the concept of user attention, and the popularity of social networks to describe the local and general user attention distributions, respectively. Our simulation of the competition between two social networks with different initial network structures and cooperation/defection utilities shows that a greater reputation awareness can suppress the influence of the defect temptation value.