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

An Extensive Validation of a SIR Epidemic Model to Study the Propagation of Jamming Attacks against IoT Wireless Networks.

This paper describes the utilization of an epidemic approach to study the propagation of jamming attacks, which can affect to different communication layers of all nodes in a variety of Internet of Things (IoT) wireless networks, regardless of the complexity and computing power of the devices. The jamming term considers both the more classical approach of interfering signals focusing on the physical level of the systems, and the cybersecurity approach that includes the attacks generated in upper layers like Medium Access Control (MAC), producing the same effect on the communication channel. In order to study the accuracy of the proposed epidemic model to estimate the propagation of jamming attacks, this paper uses the results of public simulations and experiments. It is of special interest the data obtained from experiments based on protocols such as Multi-Parent Hierarchical Protocol (MPH), Ad-hoc On-demand Distance Vector (AODV), and Dynamic Source Routing (DSR), working over the IEEE 802.15.4 standard. Then, using the formulation of the deterministic epidemiological model Susceptible–Infected–Recovered (SIR), together the abovementioned simulation, it has been seen that the proposed epidemic model could be used to estimate in that kind of IoT networks, the impact of the jamming attack in terms of attack severity and attack persistenceThis research has been partially supported by Ministerio de Economía, Industria y Competitividad (MINECO), Agencia Estatal de Investigación (AEI), and Fondo Europeo de Desarrollo Regional (FEDER, UE) under projects TIN2017-84844-C2-1-R and PGC2018-098813-B-C32

Modelling environmentally-mediated infectious diseases of humans: transmission dynamics of schistosomiasis in China.

Macroparasites of humans are sensitive to a variety of environmental variables, including temperature, rainfall and hydrology, yet current comprehension of these relationships is limited. Given the incomplete mechanistic understanding of environment-disease interactions, mathematical models that describe them have seldom included the effects of time-varying environmental processes on transmission dynamics and where they have been included, simple generic, periodic functions are usually used. Few examples exist where seasonal forcing functions describe the actual processes underlying the environmental drivers of disease dynamics. Transmission of human schistosomes, which involves multiple environmental stages, offers a model for applying our understanding of the environmental determinants of the viability, longevity, infectivity and mobility of these stages to controlling disease in diverse environments. Here, a mathematical model of schistosomiasis transmission is presented which incorporates the effects of environmental variables on transmission. Model dynamics are explored and several key extensions to the model are proposed

Dynamics of a Worm Propagation Model with Quarantine in Wireless Sensor Networks

We study the attacking behavior of possible worms inWireless Sensor Network (WSNs). Using epidemic theory, we propose a susceptible-infectious-quarantine-recovered (SIQR)model to describe dynamics of worms propagation with quarantine in the wireless sensor network. Mathematical analysis shows that dynamics of the spread of worms are determined by the threshold R0. If R0 ≤ 1, the worm-free equilibrium is globally asymptotically stable, and if R0 \u3e 1, the worm-endemic equilibrium is globally asymptotically stable. Lyapunov functional method as well as geometric approach are used for proving the global stability of equilibria. A numerical investigation is carried out to confirm the analytical results. As a result of parameter analysis, some effective strategies for eliminating worms are suggested

Towards automated distributed containment of zero-day network worms

Worms are a serious potential threat to computer network security. The high potential speed of propagation of worms and their ability to self-replicate make them highly infectious. Zero-day worms represent a particularly challenging class of such malware, with the cost of a single worm outbreak estimated to be as high as US$2.6 Billion. In this paper, we present a distributed automated worm detection and containment scheme that is based on the correlation of Domain Name System (DNS) queries and the destination IP address of outgoing TCP SYN and UDP datagrams leaving the network boundary. The proposed countermeasure scheme also utilizes cooperation between different communicating scheme members using a custom protocol, which we term Friends. The absence of a DNS lookup action prior to an outgoing TCP SYN or UDP datagram to a new destination IP addresses is used as a behavioral signature for a rate limiting mechanism while the Friends protocol spreads reports of the event to potentially vulnerable uninfected peer networks within the scheme. To our knowledge, this is the first implementation of such a scheme. We conducted empirical experiments across six class C networks by using a Slammer-like pseudo-worm to evaluate the performance of the proposed scheme. The results show a significant reduction in the worm infection, when the countermeasure scheme is invoked