Statistical inference framework for source detection of contagion processes on arbitrary network structures

In this paper we introduce a statistical inference framework for estimating the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on a maximum likelihood estimation of a partial epidemic realization and involves large scale simulation of contagion spreading processes from the set of potential source locations. We present a number of different likelihood estimators that are used to determine the conditional probabilities associated to observing partial epidemic realization with particular source location candidates. This statistical inference framework is also applicable for arbitrary compartment contagion spreading processes on networks. We compare estimation accuracy of these approaches in a number of computational experiments performed with the SIR (susceptible-infected-recovered), SI (susceptible-infected) and ISS (ignorant-spreading-stifler) contagion spreading models on synthetic and real-world complex networks

Who let the Cat out of the Bag? Internet Data Leakage and its Implications for Privacy Law and Policy in Hong Kong

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Specialized Genetic Algorithm Based Simulation Tool Designed For Malware Evolution Forecasting

From the security point of view malware evolution forecasting is very important, since it provides an opportunity to predict malware epidemic outbreaks, develop effective countermeasure techniques and evaluate information security level. Genetic algorithm approach for mobile malware evolution forecasting already proved its effectiveness. There exists a number of simulation tools based on the Genetic algorithms, that could be used for malware forecasting, but their main disadvantages from the user’s point of view is that they are too complicated and can not fully represent the security entity parameter set. In this article we describe the specialized evolution forecasting simulation tool developed for security entities, such as different types of malware, which is capable of providing intuitive graphical interface for users and ensure high calculation performance. Tool applicability for the evolution forecasting tasks is proved by providing mobile malware evolution forecasting results and comparing them with the results we obtained in 2010 by means of MATLAB

Malicious botnet survivability mechanism evolution forecasting by means of a genetic algorithm

Botnets are considered to be among the most dangerous modern malware types and the biggest current threats to global IT infrastructure. Botnets are rapidly evolving, and therefore forecasting their survivability strategies is important for the development of countermeasure techniques. The article propose the botnet-oriented genetic algorithm based model framework, which aimed at forecasting botnet survivability mechanisms. The model may be used as a framework for forecasting the evolution of other characteristics. The efficiency of different survivability mechanisms is evaluated by applying the proposed fitness function. The model application area also covers scientific botnet research and modelling tasks. Article in English. Kenkėjiškų botnet tinklų išgyvenamumo mechanizmų evoliucijos prognozavimas genetinio algoritmo priemonėmis Santrauka. Botnet tinklai pripažįstami kaip vieni pavojingiausių šiuolaikinių kenksmingų programų ir vertinami kaip viena iš didžiausių grėsmių tarptautinei IT infrastruktūrai. Botnettinklai greitai evoliucionuoja, todėl jų savisaugos mechanizmų evoliucijos prognozavimas yra svarbus planuojant ir kuriant kontrpriemones. Šiame straipsnyje pateikiamas genetiniu algoritmu pagrįstas modelis, skirtas Botnet tinklų savisaugos mechanizmų evoliucijai prognozuoti, kuris taip pat gali būti naudojamas kaip pagrindas kitų Botnet tinklų savybių evoliucijai modeliuoti. Skirtingi savisaugos mechanizmai vertinami taikant siūlomą tinkamumo funkciją. Raktiniai žodžiai: Botnet; genetinis algoritmas; prognozė; savisauga; evoliucija; modeli

CREATING PROTOTYPE VIRUS - DESTROYING FILES AND TEXTS ON ANY COMPUTER

When we study how viruses work and prevent them, we've developed a very simple application where we can see a prototype of a virus and virus function, as well as neutralizing a file if we want to break it down its structure at the level of the bits Purpose-Understand how a virus works by programming it in a high programming language. In our case, the C # programming language with the Visual Studio program that uses the .Net Framework. With the Windows Form Application module, the same application we are creating can also use it to neutralize a sentence if we know it is infected by interfering with the file we set up itself and by disrupting the system his Binary

A Baseline Numeric Analysis of Network Telescope Data for Network Incident Discovery

This paper investigates the value of Network Telescope data as a mechanism for network incident discovery by considering data summa-rization, simple heuristic identification and deviations from previously observed traffic distributions. It is important to note that the traffic ob-served is obtained from a Network Telescope and thus does not expe-rience the same fluctuations or vagaries experienced by normal traffic. The datasets used for this analysis were obtained from a Network Tele-scope for the time period August 2005 to September 2009 which had been allocated a Class-C network address block at Rhodes University. The nature of the datasets were considered in terms of simple statistical measures obtained through data summarization which greatly reduced the processing and observation required to determine whether an inci-dent had occurred. However, this raised issues relating to the time in-terval used for identification of an incident. A brief discussion into statis-tical summaries of Network Telescope data as" good" security metrics is provided. The summaries derived were then used to seek for signs of anomalous network activity. Anomalous activity detected was then rec-onciled by considering incidents that had occurred in the same or simi-lar time interval. Incidents identified included Conficker, Win32. RinBot, DDoS and Norton Netware vulnerabilities. Detection techniques includ-ed identification of rapid growth in packet count, packet size deviations, changes in the composition of the traffic expressed as a ratio of its constituents and changes in the modality of the data. Discussion into the appropriateness of this sort of manual analysis is provided and suggestions towards an automated solution are discussed

Optimal Control of a Delay-Varying Computer Virus Propagation Model

By incorporating the objective of keeping a low number of infected nodes and a high number of recovered nodes at a lower cost into a known computer virus model (the delay-varying SIRC model) extended by introducing quarantine, a novel model is described by means of the optimal control strategy and theoretically analyzed. Through the comparison of simulation results, it is shown that the propagation of computer virus with varying latency period can be suppressed effectively by the optimal control strategy

Global Dynamics and Optimal Control of a Viral Infection Model with Generic Nonlinear Infection Rate

This paper is devoted to exploring the combined impact of a generic nonlinear infection rate and infected removable storage media on viral spread. For that purpose, a novel dynamical model with an external compartment is proposed, and the explanations of the main model assumptions (especially the generic nonlinear infection rate) are also examined. The existence and global stability of the unique equilibrium of the model are fully investigated, from which it can be seen that computer virus would persist. On this basis, a next-best approach to controlling the level of infected computers is suggested, and the theoretical analysis of optimal control of the model is also performed. Additionally, some numerical examples are given to illustrate the main results

Markovian and stochastic differential equation based approaches to computer virus propagation dynamics and some models for survival distributions

This dissertation is divided in two Parts. The first Part explores probabilistic modeling of propagation of computer \u27malware\u27 (generally referred to as \u27virus\u27) across a network of computers, and investigates modeling improvements achieved by introducing a random latency period during which an infected computer in the network is unable to infect others. In the second Part, two approaches for modeling life distributions in univariate and bivariate setups are developed. In Part I, homogeneous and non-homogeneous stochastic susceptible-exposed-infectious- recovered (SEIR) models are specifically explored for the propagation of computer virus over the Internet by borrowing ideas from mathematical epidemiology. Large computer networks such as the Internet have become essential in today\u27s technological societies and even critical to the financial viability of the national and the global economy. However, the easy access and widespread use of the Internet makes it a prime target for malicious activities, such as introduction of computer viruses, which pose a major threat to large computer networks. Since an understanding of the underlying dynamics of their propagation is essential in efforts to control them, a fair amount of research attention has been devoted to model the propagation of computer viruses, starting from basic deterministic models with ordinary differential equations (ODEs) through stochastic models of increasing realism. In the spirit of exploring more realistic probability models that seek to explain the time dependent transient behavior of computer virus propagation by exploiting the essential stochastic nature of contacts and communications among computers, the present study introduces a new refinement in such efforts to consider the suitability and use of the stochastic SEIR model of mathematical epidemiology in the context of computer viruses propagation. We adapt the stochastic SEIR model to the study of computer viruses prevalence by incorporating the idea of a latent period during which computer is in an \u27exposed state\u27 in the sense that the computer is infected but cannot yet infect other computers until the latency is over. The transition parameters of the SEIR model are estimated using real computer viruses data. We develop the maximum likelihood (MLE) and Bayesian estimators for the SEIR model parameters, and apply them to the \u27Code Red worm\u27 data. Since network structure can be a possibly important factor in virus propagation, multi-group stochastic SEIR models for the spreading of computer virus in heterogeneous networks are explored next. For the multi-group stochastic SEIR model using Markovian approach, the method of maximum likelihood estimation for model parameters of interest are derived. The method of least squares is used to estimate the model parameters of interest in the multi-group stochastic SEIR-SDE model, based on stochastic differential equations. The models and methodologies are applied to Code Red worm data. Simulations based on different models proposed in this dissertation and deterministic/ stochastic models available in the literature are conducted and compared. Based on such comparisons, we conclude that (i) stochastic models using SEIR framework appear to be relatively much superior than previous models of computer virus propagation - even up to its saturation level, and (ii) there is no appreciable difference between homogeneous and heterogeneous (multi-group) models. The \u27no difference\u27 finding of course may possibly be influenced by the criterion used to assign computers in the overall network to different groups. In our study, the grouping of computers in the total network into subgroups or, clusters were based on their geographical location only, since no other grouping criterion were available in the Code Red worm data. Part II covers two approaches for modeling life distributions in univariate and bivariate setups. In the univariate case, a new partial order based on the idea of \u27star-shaped functions\u27 is introduced and explored. In the bivariate context; a class of models for joint lifetime distributions that extends the idea of univariate proportional hazards in a suitable way to the bivariate case is proposed. The expectation-maximization (EM) method is used to estimate the model parameters of interest. For the purpose of illustration, the bivariate proportional hazard model and the method of parameter estimation are applied to two real data sets