AI-Driven Stochastic Modeling and Neural Network-Based Defense Against Worm Propagation in Wireless Sensor Networks

The paper introduces a new architecture of artificial intelligence that can help reduce the spread of worms within the Wireless Sensor Networks (WSNs) which can help solve the major issue of network security and communication systems. An initial value problem (IVP) solver based on Python is used to produce numerical datasets that are used as inputs to a Levenberg Marquardt feedforward neural network algorithm (LMFNNA) integrated with machine learning methods.