Next-Generation Cybersecurity Threat Detection: Integration with Artificial Intelligence

Abstract

With the rapid advancement of information technology, cybersecurity threats have evolved into a significant challenge in modern society. Traditional detection methods often fail to address new and complex threats, especially large-scale and persistent intrusions. Artificial Intelligence (AI), particularly machine learning and deep learning, has shown great promise in enhancing threat detection capabilities. This paper explores the integration of AI technologies into cybersecurity, reviewing current approaches including supervised, unsupervised, and semi-supervised learning, as well as deep learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The paper evaluates various AI-based models for threat detection and compares their performance using standard datasets. Additionally, challenges in improving the real-time capability, interpretability, and generalization of AI models are discussed. Finally, the paper outlines future research directions, emphasizing the need for more diverse and larger datasets, and the potential of reinforcement learning and multimodal learning in building intelligent cybersecurity defense systems.