While cyber physics system (CPS) provides forward-looking and personalized services for users, it also creates conditions for the spread of malware.Therefore, it's critical for us to analyse affecting factors and study the propagation characteristics of malware.On the basis of the difference of intelligent device's dissemination capacity and discriminant ability, a dynamic malware propagation model (Disseminate&Discriminate-Spread-Exposed-Ignorant-Recover, DDSEIR) is proposed.First, intelligent devices are classified into different groups according to the level of dissemination capability by hierarchical mechanism, which takes the networks topology into account.And subsequently, intelligent device's discriminant ability can be evaluated by sender's identity and information attributes.Then, the mean field equation is constructed to analyze the dynamic characteristics of DDSEIR and the factors that influence malware propagation, which is used to further derive the malware propagation scale and threshold value of diffusion.Finally, this paper uses Live Journal dataset to verify the effectiveness of DDSEIR.The experiments illustrate that intelligent device's dissemination capacity and discriminant ability have significant influences on malware propagation.

The construction of smart cities is the concentrated embodiment of the application of Internet of Things (IoT), which provides a variety of solutions to various problems faced by urban development and urban management. However, the openness, dynamics, and heterogeneity of city IoT increase the risk of attacks, especially the social attributes contained in such systems accelerate virus propagation. To reduce the risk and harm of virus propagation, it is of great significance to analyze the mode and the characteristics of virus propagation. This article proposes a dynamic virus propagation model [i.e., Ignorant-DKs-HN-Exposed-Pvirus-Spread-Recover (IDEPSR)], which focuses on two social attributes (i.e., intelligent device's propagation capability and identification ability). First, this article presents a new algorithm (i.e., DKs-HN) to measure the first attribute based on the evidence theory and an improved K-shell method. The DKs-HN merges the direct influence of a particular node and the indirect influence of 1-hop neighbors by utilizing the combination rules of the evidence theory. Second, this article proposes a Pvirus method to measure the second attribute based on the social hierarchy theory and the nonnegative matrix factor method. Every intelligent device can identify its trust relationship with other devices by using the Pvirus, and then the probability of virus activation lurking in devices can be predicted. Finally, a real data set is used to simulate a social-aware city IoT environment. Convincing experimental results show that the IDEPSR is more reasonable in design and good in performance. The IDEPSR performs better in controlling the virus propagation than the other five models.

The existence of botnet puts people in an extremely insecure environment, which has seriously affected the development of Internet of Things (IoT). In order to prevent the formation of a botnet, it is necessary to understand the propagation behaviors and influence factors. As IoT devices become more intelligent, they can gradually generate social characteristics by mimicking human behaviors. However, in the process of coping with the botnet problem, little consideration has been given to the potential social characteristics of IoT. In this article, we build a dynamic botnet propagation model (i.e., IoT-BSI model) to study the influences of two social characteristics (i.e., device's spread capability and device's identification ability) on botnet formation. First, in terms of device's spread capability, this article makes great improvements on the K- shell decomposition algorithm and calculates this characteristic more accurately. Second, this article divides the device's identification ability into the rational identification ability and the irrational identification ability based on the sociological theory, and the calculation of the former is mainly realized by utilizing the PageRank idea. Third, this article applies the mean-field equation theory to analyze the dynamic characteristics of botnet propagation theoretically. Finally, the comprehensive results show that our model is not merely more consistent with the actual situation but also performs better than four compared models.