A New Model for Spreading Malware over SMS Using Network Automata

By the year 2026, it is estimated that the number of smartphone users in Mexico will be approximately 118.1 million. Each smartphone has the functionality of sending and receiving SMS (Short Message Service) messages, which pose a significant threat to all users, as it makes any device vulnerable to a malware attack. In particular, worm-type malware takes advantage of this means of communication in order to spread. Studying the dynamics of malware propagation can help understand and prevent massive contagion between mobile devices. In this work, a model based on Network Automata and compartmental epidemiological models is presented, aiming to simulate, analyze and study the spread of worm-like malware through sending SMS on smartphones.

Asymptotic Analysis of Transmission Capacities for Overlaid Spread-Spectrum Wireless Networks

We study the transmission capacities of two coexisting spread-spectrum wireless networks (a primary network vs. a secondary network) that operate in the same geographic region and share the same spectrum. We defi ne transmission capacity as the product among the density of transmissions, the transmission rate, and the successful transmission probability. The primary (PR) network has a higher priority to access the spectrum without particular considerations for the secondary (SR) network, while the SR network limits its interference to the PR network by carefully controlling the density ofits transmitters. Considering two types of spread-spectrum transmission schemes (FH-CDMA and DS-CDMA) and the channel inversion power control mechanism, we quantify the transmission capacities for these two networks based on asymptotic analysis. Our results show that if the PR network permits a small increase ofits outage probability, the sum transmission capacities of the two networks (i.e., the overall spectrumefficiency per unit area) will be boosted significantly over that of a single network.

A random walk evolution model of wireless sensor networks and virus spreading

In this paper, considering both cluster heads and sensor nodes, we propose a novel evolving a network model based on a random walk to study the fault tolerance decrease of wireless sensor networks （WSNs） due to node failure, and discuss the spreading dynamic behavior of viruses in the evolution model. A theoretical analysis shows that the WSN generated by such an evolution model not only has a strong fault tolerance, but also can dynamically balance the energy loss of the entire network. It is also found that although the increase of the density of cluster heads in the network reduces the network efficiency, it can effectively inhibit the spread of viruses. In addition, the heterogeneity of the network improves the network efficiency and enhances the virus prevalence. We confirm all the theoretical results with sufficient numerical simulations.

Dynamic properties of epidemic spreading on finite size complex networks

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS （susceptibleinfected-susceptible） model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Epidemic spreading on networks with vaccination

In this paper, a new susceptible-infected-susceptible （SIS） model on complex networks with imperfect vaccination is proposed. Two types of epidemic spreading patterns （the recovered individuals have or have not immunity） on scale-free networks are discussed. Both theoretical and numerical analyses are presented. The epidemic thresholds related to the vaccination rate, the vaccination-invalid rate and the vaccination success rate on scale-free networks are demonstrated, showing different results from the reported observations. This reveals that whether or not the epidemic can spread over a network under vaccination control is determined not only by the network structure but also by the medicine＇s effective duration. Moreover, for a given infective rate, the proportion of individuals to vaccinate can be calculated theoretically for the case that the recovered nodes have immunity. Finally, simulated results are presented to show how to control the disease prevalence.

Simulating the Spreading of Two Competing Public Opinion Information on Complex Network

In this paper, we investigate the regularity of spreading of information and public opinions towards two competing products in complex networks. By building its mathematical model and simulating its evolution process, we have found the statistical regularity for support rates of two different products at a steady stage. The research shows that strength of the public opinion spreading is proportional to the final support rates of a product.

Neural network approach to blindestimation of PN spreading sequence in lower SNR DS/SS signals

An approach based on discrete Karhunen-Loeve transformation of the DS/SS signals is proposed to estimate PN sequence in lower S/N ratio DS/SS signals. Characteristics of self-organization and principle components extraction of unsupervised neural networks are exploited adequately. Theoretical analysis and experimental results are provided to show that this approach can work well on the lower S/N ratio input signals.

Virus spreading in wireless sensor networks with a medium access control mechanism

In this paper, an extended version of standard susceptible-infected （SI） model is proposed to consider the influence of a medium access control mechanism on virus spreading in wireless sensor networks. Theoretical analysis shows that the medium access control mechanism obviously reduces the density of infected nodes in the networks, which has been ignored in previous studies. It is also found that by increasing the network node density or node communication radius greatly increases the number of infected nodes. The theoretical results are confirmed by numerical simulations.

Rejection of Narow─Band Interference in DS Spread Spectrum Systems Using Recurent Neural Networks

Adaptive whitening filters have been proven to be powerful to eliminate narrow band interference in spread spectrum (SS) communication systems. However digital implementation of such kind adaptive filters is difficult for applications with chip rate as

Effect of incubation period on epidemic spreading in complex networks

We study the effect of incubation period on epidemic spreading in the Barabasi-Albert scale-free network and the Watts-Strogatz small world network by using a Suspectable-Incubated-Infected-Suspectable model. Our analytical investigations show that the epidemic threshold is independent of incubation period in both networks, which is verified by our large-scale simulation results. We also investigate the effect of incubation period on the epidemic dynamics in a supercritical regime. It is found that with the increase of incubation period Ω, a damped oscillation evolution of ρT （the ratio of persons in incubated state） appears and the time needed to reach a saturation value increases. Moreover, the steady value of ρT increases and approaches to an asymptotic constant with the value of Ω increasing. As a result, the infected ratio ρI decreases with the increase of Ω according to a power law.

Spreading behavior of SIS epidemic model on networks with dynamical topology

Based on the two-dimensional regular lattice,a modified SIS(Susceptible-Infected-Susceptible)epidemic model with motion rules is presented to study the spreading behavior on networks with dynamical topology.The mean-field theory is utilized to analyze the critical threshold(λc)of epidemic spreading under the randomly mixing conditions.It is found that λc is only related with the population density within the lattice.Large-scale numerical simulations are carried out to verify the mean-field results,and it is observed that the long-range probability p largely affects the epidemic spreading behavior.In addition,the effect of the dual time scales on epidemic spreading is also investigated by the simulations,and it is shown that the dual time scales accelerate the dynamic spreading behavior.The results indicate that the model with motion can help us to further understand the real epidemics.

Controlling disease spread on networks with feedback mechanism

Many real-world networks have the ability to adapt themselves in response to the state of their nodes. This paper studies controlling disease spread on network with feedback mechanism, where the susceptible nodes are able to avoid contact with the infected ones by cutting their connections with probability when the density of infected nodes reaches a certain value in the network. Such feedback mechanism considers the networks＇ own adaptivity and the cost of immunization. The dynamical equations about immunization with feedback mechanism ave solved and theoretical predictions are in agreement with the results of large scale simulations. It shows that when the lethality a increases, the prevalence decreases more greatly with the same immunization g. That is, with the same cost, a better controlling result can be obtained. This approach offers an effective and practical policy to control disease spread, and also may be relevant to other similar networks.

Modelling the spread of sexually transmitted diseases on scale-free networks

In this paper a new model for the spread of sexually transmitted diseases （STDs） is presented. The dynamic behaviors of the model on a heterogenons scale-free （SF） network are considered, where the absence of a threshold on the SF network is demonstrated, and the stability of the disease-free equilibrium is obtained. Three immunization strategies, uniform immunization, proportional immunization and targeted immunization, are applied in this model. Analytical and simulated results are given to show that the proportional immunization strategy in the model is effective on SF networks.

A Neural Network Approach to Blind Estimation of PN Spreading Sequence in DS/SS Signals

In this paper, a new approach is proposed to estimate pseudo noise(PN) sequence in the lower SNR DS/SS signals blindly. This method utilizes the characteristics of self-organization, principal components analysis and extraction of unsupervised neural networks adequately, in addition to its higher-speed operation ability, successfully solve the difficult problem about PN sequence blind estimation. The theoretic analysis and experimental results show that this approach can work very well on lower SNR input signals.

Performance Analysis of Wireless Mobile Radio Hybrid(DS/FH) Spread Spectrum Network

By the flexible redefinition of frequency-occupation and frequency-collision event, the frequency-(collision) probability of hybrid(DS/FH) spread spectrum network is analyzed. This probability is based on the simultaneous transmission number threshold and is discussed in synchronous and asynchronous circumstances respectively. And then, the network throughput based on the packet correct reception probability is analyzed. Two models which have finite and infinite population respectively is discussed. At last, the numerical results are given.

Steady-State Analysis of SECIR Rumor Spreading Model in Complex Networks

In this paper, the SECIR rumor spreading model is formulated and analyzed, in which the social education level and the counterattack mechanism are taken into consideration. The results show that improving education level and increasing the ratio of counter are effective in reducing the risk of rumor propagation and enhancing the resistance to rumor propagation.

Epidemic spreading on random surfer networks with infected avoidance strategy

In this paper, we study epidemic spreading on random surfer networks with infected avoidance （IA） strategy. In particular, we consider that susceptible individuals＇ moving direction angles are affected by the current location information received from infected individuals through a directed information network. The model is mainly analyzed by discrete-time numerical simulations. The results indicate that the IA strategy can restrain epidemic spreading effectively. However, when long-distance jumps of individuals exist, the IA strategy＇s effectiveness on restraining epidemic spreading is heavily reduced. Finally, it is found that the influence of the noises from information transferring process on epidemic spreading is indistinctive.

引入激活扩散的类分布关系近邻分类器

针对同质性关系分类器基于一阶Markov假设简化处理的局限性,在类分布关系近邻分类器构建类向量和参考向量时,引入局部图排序激活扩散方法,并结合松弛标注的协作推理方法,通过适当扩大分类时邻居节点的范围增加网络数据中待分类节点的同质性,从而降低分类错误率.对比实验结果表明,该方法扩大了待分类节点的邻域,在网络数据上分类精度较好.

基于靴襻渗流模型的国际信息通信产业贸易前景分析

信息通信技术(ICT)产业是国际数字经济发展的核心产业,交互贯通的国际ICT产业贸易网络能够客观反映出ICT产业贸易的时空分异格局。本文运用复杂网络分析方法探测国际ICT产业贸易网络的基础拓扑特征及中心性特征,同时运用靴襻渗流模型讨论ICT产业贸易的危机传播过程。研究发现,国际ICT产业整体贸易强度有所扩大,呈现出明显的区域集中化趋势,但贸易联系的整体活跃度、贸易范围和网络传输效率都明显降低;主要ICT产业贸易国(地区)整体的危机传染力高于以往,防控核心贸易国(地区)的点危机与预防核心板块的区域性联动危机相结合将是阻止危机全球蔓延的有效举措。

识别高阶网络传播中最有影响力的节点

识别网络传播中最有影响力的节点是控制传播速度和范围的重要步骤,有助于加速有益信息扩散,抑制流行病、谣言和虚假信息的传播等.已有研究主要基于描述点对交互的低阶复杂网络.然而,现实中个体间的交互不仅发生在点对之间,也发生在3个及以上节点形成的群体中.群体交互可利用高阶网络来刻画,如单纯复形与超图.本文研究单纯复形上最有影响力的传播者识别方法.首先,提出单纯复形上易感-感染-恢复(SIR)微观马尔可夫链方程组,定量刻画单纯复形上的疾病传播动力学.接下来利用微观马尔可夫链方程组计算传播动力学中节点被感染的概率.基于网络结构与传播过程,定义节点的传播中心性,用于排序节点传播影响力.在两类合成单纯复形与4个真实单纯复形上的仿真结果表明,相比于现有高阶网络中心性和复杂网络中最优的中心性指标,本文提出的传播中心性能更准确地识别高阶网络中最有影响力的传播者.