Harnessing Convolutional Neural Networks for Cybersecurity: Enhancing Threat Detection with Adaptive Machine Learning

Abstract

As cybersecurity threats grow in complexity, traditional detection methods struggle to keep pace with evolving attacks. Convolutional Neural Networks (CNNs), renowned for their success in image recognition, have emerged as powerful tools for enhancing threat detection in cybersecurity. This paper explores the application of CNNs to identify and mitigate various cyber threats, including malware, network intrusions, and phishing attempts. By leveraging CNNs' ability to extract complex patterns from raw data, we propose an adaptive machine learning framework capable of continuous learning and real-time threat identification. Our model improves detection accuracy by dynamically adjusting to new attack vectors, minimizing false positives, and reducing the time required for threat response. Through experimental evaluation, we demonstrate that CNN-based systems outperform conventional methods in both detection speed and accuracy. This study highlights the potential of integrating CNNs into cybersecurity infrastructures to build more resilient and intelligent defense mechanisms.