An Immunization Strategy for Social Network Worms Based on Network Vertex Influence

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.

Random walk immunization strategy on scale-free networks

A novel immunization strategy called the random walk immunization strategy on scale-free networks is proposed. Different from other known immunization strategies, this strategy works as follows： a node is randomly chosen from the network. Starting from this node, randomly walk to one of its neighbor node; if the present node is not immunized, then immunize it and continue the random walk; otherwise go back to the previous node and randomly walk again. This process is repeated until a certain fraction of nodes is immunized. By theoretical analysis and numerical simulations, we found that this strategy is very effective in comparison with the other known immunization strategies.

Rumor Spreading Model with Immunization Strategy and Delay Time on Homogeneous Networks

In order to prevent and control the spread of rumors, the implementation of immunization strategies for ignorant individuals is very necessary, where the immunization usually means letting them learn the truth of rumors.Considering the facts that there is always a delay time between rumor spreading and implementing immunization, and that the truth of rumors can also be spread out, this paper constructs a novel susceptible-infected-removed(SIR) model.The propagation dynamical behaviors of the SIR model on homogeneous networks are investigated by using the meanfield theory and the Monte Carlo method. Research shows that the greater the delay time, the worse the immune effect of the immunization strategy. It is also found that the spread of the truth can inhibit to some extent the propagation of rumors, and the trend will become more obvious with the increase of reliability of the truth. Moreover, under the influence of delay time, the existence of nodes' identification force still slightly reduces the propagation degree of rumors.

Immunization for scale-free networks by random walker

Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks. Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.

Correlation and trust mechanism-based rumor propagation model in complex social networks

In real life, the rumor propagation is influenced by many factors. The complexity and uncertainty of human psychology make the diffusion model more challenging to depict. In order to establish a comprehensive propagation model, in this paper, we take some psychological factors into consideration to mirror rumor propagation. Firstly, we use the Ridenour model to combine the trust mechanism with the correlation mechanism and propose a modified rumor propagation model. Secondly, the mean-field equations which describe the dynamics of the modified SIR model on homogenous and heterogeneous networks are derived. Thirdly, a steady-state analysis is conducted for the spreading threshold and the final rumor size. Fourthly, we investigate rumor immunization strategies and obtain immunization thresholds. Next, simulations on different networks are carried out to verify the theoretical results and the effectiveness of the immunization strategies.The results indicate that the utilization of trust and correlation mechanisms leads to a larger final rumor size and a smaller terminal time. Moreover, different immunization strategies have disparate effectiveness in rumor propagation.

System Recovery-Aware Virus Propagation Model and Its Steady-State Analysis

Network structures and human behaviors are considered as two important factors in virus defense currently. However, due to ignorance of network security, normal users usually take simple activities, such as reinstalling computer system, or using the computer recovery system to clear virus. How system recovery influences virus spreading is not taken into consideration currently. In this paper, a new virus propagation model considering the system recovery is proposed first, and then in its steady-state analysis, the virus propagation steady time and steady states are deduced. Experiment results show that models considering system recovery can effectively restrain virus propagation. Furthermore, algorithm with system recovery in BA scale-free network is proposed. Simulation result turns out that target immunization strategy with system recovery works better than traditional ones in BA network.

Immune Control Strategies for Vaccinia Virus-related Laboratory-acquired Infections

While presenting biological characteristics of vaccinia virus and laboratory-acquired infections during related research processes, this paper focuses on benefits and risks of vaccinia virus immunization in relation to laboratory-acquired infections, describes characteristics and the adaptation of vaccinia virus vaccine, analyses the role vaccinia virus immunization plays in the prevention and control of laboratory-acquired infections, and finally proposes solutions and countermeasures to further promote and implement immune control strategies. The problem related to immune strategy and laboratory- acquired infections which is being raised, analyzed and explored plays an active and instructive role in vaccinia virus related researches and laboratory- acquired infections, and also helps to recommend and develop relevant immune strategy for future vaccine control of such infections.

Vaccination for solid organ transplanted patients:Recommendations,efficacy,and safety

Solid organ transplant recipients face unique challenges in managing their im-munosuppressed status,making vaccination a critical consideration.This review aimed to comprehensively analyze current recommendations,evaluate the effi-cacy of vaccinations in this population,and assess safety concerns.We explored the latest evidence on vaccine types,timing,and potential benefits for transplant patients,highlighting the importance of individualized approaches for routinely used vaccines as well as coronavirus disease 2019 vaccines.By synthesizing avai-lable data,this review underscored the pressing need to optimize vaccination strategies,ensuring that transplant recipients can obtain the full protection against many pathogens while minimizing risks associated with their post-transplant immunosuppression.

PREVENTING RUMOR SPREADING ON SMALL-WORLD NETWORKS

Since the spreading of harmful rumors can deeply endanger a society, it is valuable to investigate strategies that can efficiently prevent hazardous rumor propagation. To conduct this investigation, the authors modify the SIR model to describe rumor propagation on networks, and apply two major immunization strategies, namely, the random immunization and the targeted immunization to the rumor model on a small-world network. The authors find that when the average degree of the network is small, both two strategies are effective and when the average degree is large, neither strategy is efficient in preventing rumor propagation. In the latter case, the authors propose a new strategy by decreasing the credibility of the rumor and applying either the random or the targeted immunization at the same time. Numerical simulations indicate that this strategy is effective in preventing rumor spreading on the small-world network with large average degree.