Stream Lit-Powered framework for Real-time network traffic monitoring with Python in Agriculture

Abstract

Considering the escalating prevalence of cyber threats, data breaches and unauthorized access incidents, the implementation of real-time network monitoring has become imperative for the enhancement of cyber-security protocols in different sectors. The objective of this research was to strengthen cyber-security by delivering real-time traffic visualization with packet classification (TCP, UDP, ICMP) units and automated anomaly detection capacity. The integration of digital technologies in agriculture has increased the reliance on real-time data exchange for market intelligence, precision farming and value chain management. Thus the study developed a real-time network traffic monitoring dashboard utilizing Python and Streamlit, which effectively captures, analyzes and visualizes network traffic to facilitate improved threat detection. The methodology involved Network monitoring, analysis process, real time visualization, anomaly detection and logging which involved online real time monitoring and offline storage repository for archival and analysis where significant research deficiency is identified in the realms of scalability and adaptive anomaly detection. With the growing adoption of Internet of Things (IoT) devices, drones and cloud-based platforms, network traffic monitoring has become critical to ensure reliability, security and efficiency in agricultural operations. Leveraging open-source Python libraries, the framework provides a user-friendly dashboard with low computational overhead, making it accessible to rural and resourceconstrained settings. Results demonstrate the feasibility of real-time monitoring for enhancing data security, minimizing downtime and improving the resilience of digital agriculture systems. Moreover, the consoles architecture will be optimized to handle even the largest networks and highest traffic volumes ensuring it remains effective and efficient, even in the most demanding
environments such as monitoring of agricultural value chains efficiency.