Big Data in Cybersecurity: Enhancing Threat Detection with AI and ML

Abstract

The growing sophistication and number of cyber threats have made it imperative to incorporate big data analytics, artificial intelligence (AI), and machine learning (ML) in cybersecurity. This study investigates AI-based models for improved threat detection, with emphasis on Random Forest, Support Vector Machines (SVM), Deep Learning, and K-Means Clustering. The research employs a dataset of 500,000 cybersecurity incidents, examining attack patterns, anomaly detection, and fraud prevention systems. Experimental outcomes prove that the Deep Learning model exhibited maximum accuracy at 96.8%, surpassing SVM at 92.3% and Random Forest at 94.1% for the detection of ransomware and intrusion attempts. K-Means Clustering also successfully classified malicious behavior at a detection level of 89.5%. Outcome shows that AI-based methods substantially improve real-time cyber threat mitigation over conventional approaches. In addition, the use of blockchain and big data analytics enhances financial transaction fraud detection by 35% less false positives. AI and ML, the research concludes, provide better accuracy, flexibility, and velocity in cybersecurity uses. Computational cost and adversarial attacks are the challenges that need to be optimized. More interpretable and scalable AI models need to be developed in future studies to improve global cybersecurity resilience.