Use of Artificial Neural Networks to Detect and Prevent Cybersecurity Threats

Abstract

This research paper explores the application of artificial neural networks (ANNs) in detecting and preventing cybersecurity threats. The increasing complexity and frequency of cyberattacks necessitate advanced threat detection and prevention techniques. ANNs, with their ability to learn from data and adapt to new patterns, offer a promising solution to this challenge. This study investigates various ANN architectures for different cybersecurity applications, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). The study presents experimental results demonstrating the effectiveness of ANNs in detecting malware, identifying network intrusions, and preventing phishing attacks. The paper also discusses the challenges and limitations of using ANNs in cybersecurity and proposes future directions for research in this field.