ARCHIVES

The increasing sophistication of cyber-attacks and evolving digital threats pose significant risks to organizations and individuals in the modern technological landscape. Traditional rule-based detection systems are limited in scalability, adaptability, and responsiveness, making them insufficient to counter advanced persistent threats (APTs), zero-day exploits, and insider attacks. This research presents an AI-driven Threat Detection System that leverages machine learning and deep learning models to identify, classify, and mitigate cyber threats in real time. The proposed methodology incorporates supervised and unsupervised learning techniques, anomaly detection algorithms, and natural language processing (NLP) for threat intelligence analysis. A layered architecture is implemented, integrating data preprocessing, feature extraction, model training, and real-time monitoring modules. Experiments conducted on benchmark cybersecurity datasets demonstrate improved detection accuracy, lower false-positive rates, and enhanced adaptability to unseen threats compared to conventional approaches. The system has applications in network security, intrusion detection systems (IDS), malware classification, and fraud prevention, making it a robust and scalable solution for future cybersecurity infrastructures.