Machine Learning Algorithm as a Firewall Decision and Reinforcement in Market Segmentation and Big Data

Abstract

In the modern digital landscape, vast amounts of data are generated daily, challenging traditional analytical approaches in both data security and market segmentation. This paper presents a machine learning (ML) algorithm designed to serve a dual purpose: as a decision-support firewall system and as a reinforcement tool for market segmentation within big data environments. By integrating supervised and unsupervised learning models, the proposed algorithm effectively detects anomalies and potential security threats while also identifying distinct customer segments with high precision. The firewall decision-making component utilizes predictive models to detect malicious activity in real-time, enhancing cybersecurity by proactively learning from previous threat patterns. Concurrently, the reinforcement learning component analyses customer behaviour and preferences, dynamically adapting segmentation strategies to maximize marketing effectiveness. The dual implementation of ML in these domains demonstrates significant potential in improving both data security and personalized marketing outcomes. Experimental results indicate enhanced firewall accuracy and a refined segmentation process, suggesting that the proposed ML model provides a comprehensive solution for the challenges posed by big data in cybersecurity and market segmentation. s. Keywords Machine Learning; Firewall Decision; Reinforcement Learning; Market Segmentation; Big Data