Threat Detection Driven by Artificial Intelligence: Enhancing Cybersecurity with Machine Learning Algorithms

Abstract

This paper aims to explore the applications of artificial intelligence (AI) and machine learning (ML) in the field of cybersecurity, particularly in the development of end-to-end solutions for threat detection. By analyzing the current challenges in cybersecurity and the limitations of traditional threat detection methods, this paper seeks to demonstrate how AI/ML technologies can enhance the efficiency, accuracy, and automation levels of threat detection. The paper begins by introducing the core concepts of cybersecurity threat detection, including traditional methods such as signature-based detection, behavior-based detection, and rule-based detection systems. It then elaborates on the applications of machine learning in anomaly detection, malware detection, network traffic analysis, intrusion detection systems (IDS) and intrusion prevention systems (IPS), as well as user behavior analytics (UBA). Following this, the paper discusses the importance of data preprocessing and feature engineering in threat detection and their practical applications, including data cleaning, feature selection, and extraction. Finally, the paper explores the training and evaluation of models, the deployment of models, and the challenges they face, along with future trends in AI-driven threat detection. The research results indicate that AI/ML technologies can significantly improve the accuracy and efficiency of threat detection, particularly in handling unknown threats and automating detection processes. Through the application of various machine learning algorithms, such as anomaly detection, malware detection, and network traffic analysis, systems can better identify and respond to various network threats. However, AI-driven threat detection still faces challenges related to data quality, algorithm performance, and system implementation.