Epidemic models have been used in recent times to model the dynamics of malicious codes in wireless sensor network (WSN). This is due to its open nature which provides an easy target for malware attacks aimed at disrupting the activities of the network or at worse, causing total failure of the network. The Susceptible-Exposed-Infectious-Quarantined-Recovered–Susceptible with a Vaccination compartment (SEIQR-V) model by Mishra and Tyagi is one of such models that characterize worm dynamics in WSN. However, a critical analysis of this model and WSN epidemic literature shows that it is absent essential factors such as communication range and distribution density. Therefore, we modify the SEIQR-V model to include these factors and to generate better reproduction ratios for the introduction of an infectious sensor into a susceptible sensor population. The symbolic solutions of the equilibriums were derived for two topological expressions culled from WSN literature. A suitable numerical method was used to solve, simulate and validate the modified model. Simulation results show the effect of our modifications.

Cyber security is of topical concern in the computing industry and in organizations that require ICT infrastructure for business-related activities. Theft or disrupting the flow of data and information can cause devastating damage to an institution’s reputation and this may lead to huge financial losses. More mayhem can be perpetrated by malicious codes such as worms to organizations that use wireless sensor networks for collecting and transmitting data and information. To tackle this issue of cyber security, researchers have used epidemiological models (such as SEIR and SEIR-V) to gain insight into malicious code propagation. However, topological concerns and its impact in worm propagation haven’t been thoroughly studied. Here, we modify older models by applying a different expression for sensor deployment area; we intend to highlight the spatial parameters that may allow for the extinction of worms in wireless sensor networks amidst countermeasures deployed by network managers.

Sensor networks are appealing targets for malicious attacks that invade the network with the aim of depleting the confidentiality, availability and integrity (CIA) features/parameters of neighboring sensor nodes. This is due to its open communication, minimal resources and its deployment in un-trusted, unguarded and unfriendly terrains. To restrict illegitimate users or malicious attackers (such as worms) network analysts have suggested network access control (NAC). Specifically, we apply NAC to wireless sensor network epidemic models in order to investigate distribution density, transmission range and sensor area/field. Our analyses involved analytical expressions of two sensor fields gleaned from literature. Additionally, we explored the possibilities of infectivity of sensor nodes at the exposed class using the two expressions for sensor field topologies. We also derived the reproduction ratios and solutions at several equilibrium points for the models. It is our hope that that our work herein would impact sensor deployment decisions for organizations that utilize wireless sensor networks for meaningful daily activities.

Now, it is unarguable that cyber threats arising from malicious codes such as worms possesses the ability to cause losses, damages and disruptions to industries that utilize ICT infrastructure for meaningful daily work. More so for wireless sensor networks (WSN) which thrive on open air communications. As a result epidemic models are used to study propagation patterns of these malicious codes, although they favor horizontal transmissions. Specifically, the literature dealing with the analysis of worms that are both vertically and horizontally (transmitted) is not extensive. Therefore, we propose the Vulnerable–Latent–Breaking Out–Temporarily Immune–Inoculation (VLBTV-I) epidemic model to investigate both horizontal and vertical worm transmission in wireless sensor networks. We derived the solutions of the equilibriums as well as the epidemic threshold for two topological expressions (gleaned from literature). Furthermore, we employed the Runge-Kutta-Fehlberg order 4 and 5 method to solve, simulate and validate our proposed models. Critically, we analyzed the impact of both vertical and horizontal transmissions on the latent and breaking out compartments using several simulations experiments.

This paper revisits malicious object propagation in networks using epidemic theory in such a manner that it proposes the (Pre-quarantining) of nodes in networks. This is a concept that is known by experience to be a standard disease control procedure that involves screening and quarantining of immigrants to a population. As preliminary investigation we propose the Q-SEIRS model and the more advanced Q-SEIRS-V model for malicious objects’ spread in networks. This Pre-quarantine concept addresses and implements the “assume guilty till proven innocent” slogan of the cyber world by providing a mechanism for pre-screening, isolation and treatment for incoming infected nodes. The treated nodes from the pre-quarantine compartment are sent to the recovered compartment while the free nodes join the network population. The paper also derived the reproduction number, equilibria, as well as local stability of the proposed model. Numerical methods are employed to solve the system of equations and MATLAB is used to simulate the system so as to visualize the dynamical behavior of the models. It is seen that pre-screening/pre-quarantining improves the recovery rate in relative terms.