The Intersection Of AI And Cybersecurity: Leveraging Machine Learning Algorithms For Real-Time Detection And Mitigation Of Cyber Threats

Abstract

This research focuses on studying several machine learning techniques with application to real-time cyber threat detection, such as anomaly detection, supervised and unsupervised learning, and deep learning models. The evidence of the continually growing volume and complexity of cyber threats means that organizations across the world are facing a major challenge. The conventional protective measures could not fully address the real-time threat and leave systems open to attack. Artificial intelligence and machine learning are innovative technologies that have the potential to improve cybersecurity models through robotic means for threat identification and neutralization. Comparing with traditional approaches, the use of ML algorithms also allows the organizations not only to detect the threats but to predict and prevent them in a faster and more efficient way. The author examines uses of these algorithms in multiple security areas, including network security and end-point protection. Several successful applications of these models from industries and academic sources are presented. The methodology consists in comparing algorithm performance in real-time situations, where specific attention is paid to the detection rate, percentage of false positives, and processing time. Machine learning algorithms have the potential of revolutionizing the cybersecurity field as a result of early and precise danger identification. However, issues like data privacy, high computational costs, and the ability of the cyber attackers remain a problem. Based on the findings of this study, it is highly recommended that future work employs a multi-method ML approach, supplemented by human monitoring. More studies performed to improve the accuracy of the field along with strengthening cybersecurity from a constantly emerging variety of threats.