Mitigation-Based Approach for Self-Propagating Worms in Wireless Sensor Network

Abstract

An attacker can copy malicious code from one memory location to another to spread a worm attack,
resulting in malicious code stored in a contagious memory region. In the event of malware repeating
the same process to the neighbouring sensor nodes, the memory efficiency of the infected node can
affect the propagation dynamics of the worm attack. However, the existing worm propagation
models do not consider the memory efficiency of the infected nodes while mitigating worm
propagation. Consequently, this work proposed a Susceptible-Infectious-Abandon-Quarantine
(SIAQ) model to mitigate worm propagation based on memory efficiency. To achieve this, the
SIAQ model inspired by the epidemic model can mitigate the worm propagation by isolating
infected memory-efficient nodes from the wireless sensor network (WSN). In this regard, the
infected memory-efficient nodes are subjected to a sleeping mode. In a sleeping mode, the infected
memory-efficient nodes cannot further interact with the other nodes in a WSN. Finally, the basic
reproduction number is obtained to serve as a benchmark in determining the model's performance
on the infection peak value. Based on the numerical simulation conducted, the result of the proposed
SIAQ model outperforms the previous SIQR model at about 40% in mitigating worm propagation
at the worm-endemic equilibrium state. Consequently, the proposed model can serve as a basis for
assisting in planning, design, and defence of such networks from the investigator's point of view.