Revolutionizing Malware Detection Techniques by using Predictive AI

In response to the ever-growing complexity and sophistication of cyber threats, organizations are increasingly turning to advanced technologies such as Artificial Intelligence (AI) to bolster their cybersecurity defenses. This major research paper delves into the development and implementation of AI-enabled Threat Intelligence (AI-TI) systems, with a focus on Random Forest, K-Nearest Neighbors (KNN), and XGBoost. The research methodology encompasses a multifaceted approach, beginning with the collection and preprocessing of extensive datasets comprising internal network logs, security incident reports, and open-source threat intelligence feeds. These datasets serve as the foundation for training and evaluating the AI-TI systems powered by the selected algorithms. Through empirical experimentation and performance evaluation, the efficacy of each algorithm in detecting and mitigating cyber threats is rigorously assessed. Key performance metrics such as detection accuracy, false positive rates, and response times are analyzed to ascertain the optimal configuration and deployment strategy for AI-TI systems. By harnessing the capabilities of Random Forest, KNN, and XGBoost algorithms, organizations can enhance their cyber resilience and effectively combat the evolving threat landscape.