Modeling the propagation of worms in networks: a survey

There are the two common means for propagating worms: scanning vulnerable computers in the network and spreading through topological neighbors. Modeling the propagation of worms can help us understand how worms spread and devise effective defense strategies. However, most previous researches either focus on their proposed work or pay attention to exploring detection and defense system. Few of them gives a comprehensive analysis in modeling the propagation of worms which is helpful for developing defense mechanism against worms' spreading. This paper presents a survey and comparison of worms' propagation models according to two different spreading methods of worms. We first identify worms characteristics through their spreading behavior, and then classify various target discover techniques employed by them. Furthermore, we investigate different topologies for modeling the spreading of worms, analyze various worms' propagation models and emphasize the performance of each model. Based on the analysis of worms' spreading and the existing research, an open filed and future direction with modeling the propagation of worms is provided. © 2014 IEEE.

Plantations, not farmlands, cause biotic homogenisation of ground-active beetles in South-Eastern Australia

Following landscape change, species invasions and extinctions may lead to biotic homogenisation, resulting in increased taxonomic and functional similarity between previously distinct biotas. Biotic homogenisation is more likely to occur in landscapes where the matrix contrasts strongly with native vegetation patches. To test this, we examined the distribution of ground-active beetles in a landscape of remnant Eucalyptus open woodland patches where large areas of lower contrast matrix (farmland) are being transformed to high-contrast pine plantations in south-eastern Australia. We sampled beetles from 30 sites including six replicates of five categories; (1) remnants adjacent to farmland, (2) remnants adjacent to plantation, (3) farmland, (4) plantation, and, (5) remnants between pine plantation and farmland. Community composition in the pine matrix was similar to native patches embedded in pine (ANOSIM, Global R=. 0.49, P<. 0.000), which we suggest is due to biotic homogenisation. Remnant patches with edges of both farmland and pine plantation did not represent an intermediate community composition between patches surrounded by either matrix type, but rather a unique habitat with unique species. Farmland supported the greatest number of individuals (. F=. 9.049, df. =. 25, P<. 0.000) and species (. F=. 5.875, df. =. 25, P=. 0.002), even compared to native remnant patches. Our results suggest that matrix transformations can reduce species richness and homogenise within-patch populations. This may increase the risk of species declines in fragmented landscapes where plantations are not only replacing native vegetation patches, but also other matrix types that may better support biodiversity. Our findings are particularly concerning given expanding plantation establishment worldwide.

A web-based, social networking physical activity intervention for insufficiently active adults delivered via Facebook app: randomized controlled trial

BACKGROUND: Online social networks offer considerable potential for delivery of socially influential health behavior change interventions. OBJECTIVE: To determine the efficacy, engagement, and feasibility of an online social networking physical activity intervention with pedometers delivered via Facebook app. METHODS: A total of 110 adults with a mean age of 35.6 years (SD 12.4) were recruited online in teams of 3 to 8 friends. Teams were randomly allocated to receive access to a 50-day online social networking physical activity intervention which included self-monitoring, social elements, and pedometers ("Active Team" Facebook app; n=51 individuals, 12 teams) or a wait-listed control condition (n=59 individuals, 13 teams). Assessments were undertaken online at baseline, 8 weeks, and 20 weeks. The primary outcome measure was self-reported weekly moderate-to-vigorous physical activity (MVPA). Secondary outcomes were weekly walking, vigorous physical activity time, moderate physical activity time, overall quality of life, and mental health quality of life. Analyses were undertaken using random-effects mixed modeling, accounting for potential clustering at the team level. Usage statistics were reported descriptively to determine engagement and feasibility. RESULTS: At the 8-week follow-up, the intervention participants had significantly increased their total weekly MVPA by 135 minutes relative to the control group (P=.03), due primarily to increases in walking time (155 min/week increase relative to controls, P<.001). However, statistical differences between groups for total weekly MVPA and walking time were lost at the 20-week follow-up. There were no significant changes in vigorous physical activity, nor overall quality of life or mental health quality of life at either time point. High levels of engagement with the intervention, and particularly the self-monitoring features, were observed. CONCLUSIONS: An online, social networking physical activity intervention with pedometers can produce sizable short-term physical activity changes. Future work is needed to determine how to maintain behavior change in the longer term, how to reach at-need populations, and how to disseminate such interventions on a mass scale. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR): ACTRN12614000488606; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366239 (Archived by WebCite at http://www.webcitation.org/6ZVtu6TMz).

Exponential stability for markovian jumping stochastic BAM neural networks with mode-dependent probabilistic time-varying delays and impulse control

Abstract
In this article, an exponential stability analysis of Markovian jumping stochastic bidirectional associative memory (BAM) neural networks with mode-dependent probabilistic time-varying delays and impulsive control is investigated. By establishment of a stochastic variable with Bernoulli distribution, the information of probabilistic time-varying delay is considered and transformed into one with deterministic time-varying delay and stochastic parameters. By fully taking the inherent characteristic of such kind of stochastic BAM neural networks into account, a novel Lyapunov-Krasovskii functional is constructed with as many as possible positive definite matrices which depends on the system mode and a triple-integral term is introduced for deriving the delay-dependent stability conditions. Furthermore, mode-dependent mean square exponential stability criteria are derived by constructing a new Lyapunov-Krasovskii functional with modes in the integral terms and using some stochastic analysis techniques. The criteria are formulated in terms of a set of linear matrix inequalities, which can be checked efficiently by use of some standard numerical packages. Finally, numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the proposed results.

Practices of active citizenship among migrant youth: beyond conventionalities

© 2015 Taylor & Francis This paper discusses the empirical manifestations of the notion of active citizenship in the context of the experiences of migrant youth. It focuses on the practices of active citizenship through involvement in social networks and creative civic engagement. In doing so, the article examines the complex and multi-faceted nature of social networking among migrant youth and the extent to which their approach to engagement is dependent on the specificities of the local environment, the type of social issues involved, and the cultural norms of one's own cultural heritage. Key empirical insights are derived from quantitative and qualitative research conducted among migrant youth of African, Arabic-speaking and Pacific Island backgrounds in Australia. These empirical insights are used to examine the changing perceptions of active citizenship among migrant youth, and the possibilities offered through non-traditional networks to engender civic engagement and social participation.

Malware propagation in large-scale networks

Malware is pervasive in networks, and poses a critical threat to network security. However, we have very limited understanding of malware behavior in networks to date. In this paper, we investigate how malware propagates in networks from a global perspective. We formulate the problem, and establish a rigorous two layer epidemic model for malware propagation from network to network. Based on the proposed model, our analysis indicates that the distribution of a given malware follows exponential distribution, power law distribution with a short exponential tail, and power law distribution at its early, late and final stages, respectively. Extensive experiments have been performed through two real-world global scale malware data sets, and the results confirm our theoretical findings.

Target coverage through distributed clustering in directional sensor networks

Maximum target coverage with minimum number of sensor nodes, known as an MCMS problem, is an important problem in directional sensor networks (DSNs). For guaranteed coverage and event reporting, the underlying mechanism must ensure that all targets are covered by the sensors and the resulting network is connected. Existing solutions allow individual sensor nodes to determine the sensing direction for maximum target coverage which produces sensing coverage redundancy and much overhead. Gathering nodes into clusters might provide a better solution to this problem. In this paper, we have designed distributed clustering and target coverage algorithms to address the problem in an energy-efficient way. To the best of our knowledge, this is the first work that exploits cluster heads to determine the active sensing nodes and their directions for solving target coverage problems in DSNs. Our extensive simulation study shows that our system outperforms a number of state-of-the-art approaches.