Modeling propagation dynamics of social network worms

Social network worms, such as email worms and facebook worms, pose a critical security threat to the Internet. Modeling their propagation dynamics is essential to predict their potential damages and develop countermeasures. Although several analytical models have been proposed for modeling propagation dynamics of social network worms, there are two critical problems unsolved: temporal dynamics and spatial dependence. First, previous models have not taken into account the different time periods of Internet users checking emails or social messages, namely, temporal dynamics. Second, the problem of spatial dependence results from the improper assumption that the states of neighboring nodes are independent. These two problems seriously affect the accuracy of the previous analytical models. To address these two problems, we propose a novel analytical model. This model implements a spatial-temporal synchronization process, which is able to capture the temporal dynamics. Additionally, we find the essence of spatial dependence is the spreading cycles. By eliminating the effect of these cycles, our model overcomes the computational challenge of spatial dependence and provides a stronger approximation to the propagation dynamics. To evaluate our susceptible-infectious-immunized (SII) model, we conduct both theoretical analysis and extensive simulations. Compared with previous epidemic models and the spatial-temporal model, the experimental results show our SII model achieves a greater accuracy. We also compare our model with the susceptible-infectious-susceptible and susceptible-infectious- recovered models. The results show that our model is more suitable for modeling the propagation of social network worms.

Effect of ECG-derived respiration (EDR) on modeling ventricular repolarization dynamics in different physiological and psychological conditions

Ventricular repolarization dynamics is an important predictor of the outcome in cardiovascular diseases. Mathematical modeling of the heart rate variability (RR interval variability) and ventricular repolarization variability (QT interval variability) is one of the popular methods to understand the dynamics of ventricular repolarization. Although ECG derived respiration (EDR) was previously suggested as a surrogate of respiration, but the effect of respiratory movement on ventricular repolarization dynamics was not studied. In this study, the importance of considering the effect of respiration and the validity of using EDR as a surrogate of respiration for linear parametric modeling of ventricular repolarization variability is studied in two cases with different physiological and psychological conditions. In the first case study, we used 20 young and 20 old healthy subjects’ ECG and respiration data from Fantasia database at Physionet to analyze a bivariate QT–RR and a trivariate QT–RR–RESP or QT–RR–EDR model structure to study the aging effect on cardiac repolarization variability. In the second study, we used 16 healthy subjects’ data from drivedb (stress detection for automobile drivers) database at Physionet to do the same analysis for different psychological condition (i.e., in stressed and no stress condition). The results of our study showed that model having respiratory information (QT–RR–RESP and QT–RR–EDR) gave significantly better fit value (p < 0.05) than that of found from the QT–RR model. EDR showed statistically similar (p > 0.05) performance as that of respiration as an exogenous model input in describing repolarization variability irrespective of age and different mental conditions. Another finding of our study is that both respiration and EDR-based models can significantly (p < 0.05) differentiate the ventricular repolarization dynamics between healthy subjects of different age groups and with different psychological conditions, whereas models without respiration or EDR cannot distinguish between the groups. These results established the importance of using respiration and the validity of using EDR as a surrogate of respiration in the absence of respiration signal recording in linear parametric modeling of ventricular repolarization variability in healthy subjects.

Modeling and analysis on the propagation dynamics of modern email malware

Due to the critical security threats imposed by email-based malware in recent years, modeling the propagation dynamics of email malware becomes a fundamental technique for predicting its potential damages and developing effective countermeasures. Compared to earlier versions of email malware, modern email malware exhibits two new features, reinfection and self-start. Reinfection refers to the malware behavior that modern email malware sends out malware copies whenever any healthy or infected recipients open the malicious attachment. Self-start refers to the behavior that malware starts to spread whenever compromised computers restart or certain files are visited. In the literature, several models are proposed for email malware propagation, but they did not take into account the above two features and cannot accurately model the propagation dynamics of modern email malware. To address this problem, we derive a novel difference equation based analytical model by introducing a new concept of virtual infected user. The proposed model can precisely present the repetitious spreading process caused by reinfection and self-start and effectively overcome the associated computational challenges. We perform comprehensive empirical and theoretical study to validate the proposed analytical model. The results show our model greatly outperforms previous models in terms of estimation accuracy. © 2013 IEEE.

Bayesian hierarchical modeling of the temporal dynamics of subjective well-being: A 10 year longitudinal analysis

This study demonstrates, for the first time, how Bayesian hierarchical modeling can be applied to yield novel insights into the long-term temporal dynamics of subjective well-being (SWB). Several models were proposed and examined using Bayesian methods. The models were assessed using a sample of Australian adults (. n=. 1081) who provided annual SWB scores on between 5 and 10 occasions. The best fitting models involved a probit transformation, allowed error variance to vary across participants, and did not include a lag parameter. Including a random linear and quadratic effect resulted in only a small improvement over the intercept only model. Examination of individual-level fits suggested that most participants were stable with a small subset exhibiting patterns of systematic change.

Understanding spatial and temporal solute transport : case study : nutrient dynamics in cultivated pastures

Chapter six is a case study of modeling nutrient dynamics in cultivated soils.

Microclimatic modeling of the urban thermal environment of Singapore to mitigate urban heat island

This study investigates the urban heat island effect in Singapore and examines the key factors causing this effect. The possibilities of improving heat extraction rate by optimizing air flow in selected hot spots were explored. The effect of building geometry, façade materials and the location of air-conditioning condensers on the outdoor air temperature was explored using computational fluid dynamics (CFD) simulations. It was found that at very low wind speeds, the effect of façade materials and their colours was very significant and the temperature at the middle of a narrow canyon increased up to 2.5 °C with the façade material having lower albedo. It was also found that strategically placing a few high-rise towers will enhance the air flow inside the canyon thereby reducing the air temperature. Adopting an optimum H/W ratio for the canyons increased the velocity by up to 35% and reduced the corresponding temperature by up to 0.7 °C.

Modeling of enzyme–substrate complexes for the metalloproteases MMP-3, ADAM-9 and ADAM-10

The matrix metalloproteases (MMPs) and the ADAMs (A Disintegrin And Metalloprotease domain) are proteolytic enzyme families containing a catalytic zinc ion, that are implicated in a variety of normal and pathological processes involving tissue remodeling and cancer. Synthetic MMP inhibitors have been designed for applications in pathological situations. However, a greater understanding of substrate binding and the catalytic mechanism is required so that more effective and selective inhibitors may be developed for both experimental and clinical purposes. By modeling a natural substrate spanning P4-P4‘ in complex with the catalytic domains, we aim to compare substrate-specificities between Stromelysin-1 (MMP-3), ADAM-9 and ADAM–10, with the aid of molecular dynamics simulations. Our results show that the substrate retains a favourable antiparallel beta-sheet conformation on the P-side in addition to the well-known orientation of the P'-region of the scissile bond, and that the primary substrate selectivity is dominated by the sidechains in the S1' pocket and the S2/S3 region. ADAM-9 has a hydrophobic residue as the central determinant in the S1' pocket, while ADAM-10 has an amphiphilic residue, which suggests a different primary specificity. The S2/S3 pocket is largely hydrophobic in all three enzymes. Inspired by our molecular dynamics calculations and supported by a large body of literature, we propose a novel, hypothetical, catalytic mechanism where the Zn-ion polarizes the oxygens from the catalytic glutamate to form a nucleophile, leading to a tetrahedral oxyanion anhydride transition state.

A microscopic competition model and its dynamics analysis on network attacks

Modeling network traffic has been a critical task in the development of Internet. Attacks and defense are prevalent in the current Internet. Traditional network models such as Poisson-related models do not consider the competition behaviors between the attack and defense parties. In this paper, we present a microscopic competition model to analyze the dynamics among the nodes, benign or malicious, connected to a router, which compete for the bandwidth. The dynamics analysis demonstrates that the model can well describe the competition behavior among normal users and attackers. Based on this model, an anomaly attack detection method is presented. The method is based on the adaptive resonance theory, which is used to learn the model by normal traffic data. The evaluation shows that it can effectively detect the network attacks.

Modeling bacterial growth in drinking water : effect of nutrients

This article presents a model of growth of naturally occurring heterotrophic bacteria in the bulk water phase in the absence of disinfectant. The model considers growth with carbon, phosphorus, and nitrogen balance, death and lysis of bacteria, and conversion of less biodegradable organic carbon to assimilable organic carbon. Experimental data from two raw and two treated waters were used to test the model. The model describes the increase of live and dead bacterial cells in the water phase, and its output closely matches the experimental data. Such a model has the ability to characterize water nutrient status as well as to predict behavior of indigenous heterotrophic bacteria. The ability to predict bacterial population dynamics with respect to nutrients is beneficial for water treatment optimization. The model, based on microbiological measurements, helps to characterize treated water quality and project performance in terms of water quality into a distribution system.

Preserving the environment and traditional communities : a land use change modeling approach

Human populations can cause serious damage to the natural environment. This, however, depends on the type of society and its size. Many traditional communities have a balanced relation with the environment, using practices for managing the soil, water and natural resources in order to satisfy their needs that are compatible to the general goals of environmental preservation.

The most usual approach to environmental conservation in the world sees human beings as intruders, potentially destroyers of the nature and, as a consequence, generally requires local population to be expelled from the protected regions. This situation has generated social conflicts because many protected areas, particularly in developing countries, are inhabited by indigenous or other traditional communities.

The disagreement about expelling or maintaining traditional communities in environmental conservation areas is strengthened by the lack of diagnostics on which changes are produced or suffered by communities in the region where they live. This paper presents a methodology developed to analyse land use dynamics in region with environmental conservation and traditional communities. We seek a better understanding of the way traditional communities use their space, the spatial pattern of land uses, which factors drive land use change, which impacts can be seen in those regions and identify the effects of conservation policies on land use dynamics.

The application of the method to the National Park of Superagui, Brazil, has successfully performed characterisation, analysis and simulation of land use dynamics in a region of environmental importance. Testing different scenarios has suggested that the adoption of a less restrictive policy for environmental conservation would have resulted in less social conflict with the same environmental efficiency than the established current policy.

Data-driven computation of contact dynamics during two-point manipulation of deformable objects

This study represents a preliminary step towards data-driven computation of contact dynamics during manipulation of deformable objects at two points of contact. A modeling approach is proposed that characterizes the individual interaction at both points and the mutual effects of the two interactions on each other via a set of parameters. Both global as well as local coordinate systems are tested for encoding the contact mechanics. Artificial neural networks are trained on simulated data to capture the object behavior. A comparison of test data with the output of the trained system reveals a mean squared error percentage between 1% and 3% for simple interactions.