INTELLIGENT CYBER THREAT PREDICTION USING ML AND GENERATIVE AI APPROACHES

Abstract

Cyberattacks are a threat to enterprises, financial institutions, and digital infrastructure due to the frequent and sophisticated nature of hackers. This research examines a variety of hacking prediction techniques, including traditional machine learning and contemporary generative AI models. Random forests, support vector machines, and decision trees were implemented to assess historical network data in order to identify malicious activity. While these systems are capable of recognizing established attack signatures, they may fail to recognize emerging threats. Transformer-based models and generative adversarial networks are two recent advancements in generative AI. These technologies allow computers to understand intricate behavioral patterns, simulate plausible attack scenarios, and predict new cyberthreats. Generative AI employs predictive models, adaptive learning, and comprehensive data analysis to identify anomalous activity, attack patterns, and preventative security measures. In terms of assault prediction system accuracy, scalability, and expertise, generative AI outperforms traditional machine learning, according to the study.