There are significant cybersecurity challenges that face wireless sensor networks (WSNs) as a result of their decentralized nature and limited resources although they are highly important in most fields. Traditional security mechanisms frequently fail to cope with the changing and diverse conditions in WSNs. To reduce data transfer but maintain WSNs sensor saturation and data security, this work proposes a prediction-based data fusion and sensing strategy. The suggested method called the ARIMA-SK-EELM system which is made up of Autoregressive Integrated Moving Average (ARIMA), Stable Kernel-Enhanced Extreme Learning Machine (SK-EELM), and threefish algorithm (TFA). In the procedure on data sensing and fusion, ARIMA predicts initially from a few data elements, SK-EELM for precise accuracy on initial expected value similar to actual value while TFA is used during transmissions for both encoded and decoded data. This paper introduces an ARIMA-SK-EELM model with high predictability, low interferences, strong scalability, and secrecy. The results of simulation show that this technique suggested can be effective in reducing unnecessary transfers by accurate forecasting.

Wireless Sensor Networks (WSNs), Cybersecurity, Prediction-based Data Gathering, Autoregressive Integrated Moving Average-Stable Kernel-Enhanced Extreme Learning Machine (ARIMA-SK-EELM), Data Security, Threefish Algorithm (TFA)