Automated Incident Response using Deep Learning

Abstract

Cybersecurity threats are increasing in sophistication, requiring a shift from traditional manual incident response (IR) systems to automated approaches that can react more quickly and efficiently. This paper investigates the role of deep learning in automating incident response systems (AIRS), focusing on how advanced neural networks can enhance the detection, classification, and mitigation of cyberattacks in real-time. By leveraging deep learning architectures such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, we conduct experiments on the NSL-KDD dataset to analyze their performance. Our results indicate that deep learning models significantly outperform traditional machine learning approaches, providing faster and more accurate responses to cyber incidents. This research highlights the potential of deep learning in redefining the landscape of cybersecurity through efficient, automated systems.