ANALISIS DINAMIKA MODEL EPIDEMI SEIQR-SI PENYEBARAN WORM BEBASIS WI-FI PADA SMARTPHONE

Artikel ini membahas model matematika SEIQR-SI penyebaran worm berbasis Wi-Fi pada smartphone. Worm berbasis Wi-Fi termasuk perangkat lunak yang mampu mereplikasi dirinya untuk mencoba memecahkan kata sandi setiap router Wi-Fi baru yang ditemuinya tanpa bantuan manusia. Analisis model dilakukan dengan menentukan titik kesetimbangan beserta kestabilannya. Hasil analisis menunjukkan bahwa model SEIQR-SI memiliki dua titik kesetimbangan yaitu titik kesetimbangan bebas worm dan titik kesetimbangan endemik. Titik setimbang bebas worm stabil asimtotik lokal jika , sedangkan titik setimbang endemik stabil asimtotik lokal jika . Pada bagian akhir diberikan simulasi secara numerik yang menunjukkan peningkatan laju karantina oleh Wi-Fi base station pada worm dapat menekan jumlah node smartphone dan Wi-Fi yang terinfeksi worm

A novel dynamics model of fault propagation and equilibrium analysis in complex dynamical communication network

International audienceTo describe failure propagation dynamics in complex dynamical communication networks, we propose an efficient and compartmental standard-exception-failure propagation dynamics model based on the method of modeling disease propagation in social networks. Mathematical formulas are derived and differential equations are solved to analyze the equilibrium of the propagation dynamics. Stability is evaluated in terms of the balance factor G and it is shown that equilibrium where the number of nodes in different states does not change, is globally asymptotically stable if G≥1. The theoretical results derived are verified by numerical simulations. We also investigate the effect of some network parameters, e.g. node density and node movement speed, on the failure propagation dynamics in complex dynamical communication networks to gain insights for effective measures of control of the scale and duration of the failure propagation in complex dynamical communication networks

Spread of Malicious Objects in Computer Network: A Fuzzy Approach

We propose an e-epidemic fuzzy SEIQRS (Susceptible-Exposed-Infectious-Quarantine- Recovered-Susceptible) model for the transmission of malicious codes in a computer network. We have simulated the result for various parameters and analyzed the stability of the model. The efficiency of antivirus software and crashing of the nodes due to attack of malicious code is analyzed. Furthermore, initial simulation results illustrate the behavior of different classes for minimizing the infection in a computer network. It also reflects the positive impact of anti-virus software on malicious code propagation in a computer network. The basic reproduction number R0 f and its formulation is also discussed

Global Stability of Worms in Computer Network

An attempt has been made to show the impact of non-linearity of the worms through SIRS (susceptible – infectious – recovered - susceptible) and SEIRS (susceptible – exposed – infectious – recovered - susceptible) e-epidemic models in computer network. A very general form of non-linear incidence rate has been considered satisfying the worm propagating behavior in computer network. The concavity conditions with a non-linear incidence rate and under the constant population size assumption are shown to be stable. Such systems have either a unique and stable endemic equilibrium state or no endemic equilibrium state at all; in the latter case, the worm infection-free equilibrium is stable

Towards the epidemiological modeling of computer viruses

Epidemic dynamics of computer viruses is an emerging discipline aiming to understand the way that computer viruses spread on networks. This paper is intended to establish a series of rational epidemic models of computer viruses. First, a close inspection of some common characteristics shared by all typical computer viruses clearly reveals the flaws of previous models. Then, a generic epidemic model of viruses, which is named as the SLBS model, is proposed. Finally, diverse generalizations of the SLBS model are suggested. We believe this work opens a door to the full understanding of how computer viruses prevail on the Internet