Deep Learning Applications in Threat Detection

Abstract

The rise in sophisticated cyber threats has led to an increased demand for advanced
methods to detect and respond to malicious activities effectively. Deep learning, a subset
of machine learning, has emerged as a transformative technology in cybersecurity,
offering significant improvements in threat detection capabilities. This paper explores
various deep learning techniques applied to cybersecurity, such as convolutional neural
networks (CNNs), recurrent neural networks (RNNs), long short-term memory networks
(LSTMs), and autoencoders. It provides an overview of how these models are employed
to detect malware, network intrusions, phishing attempts, and insider threats with
unprecedented accuracy. The paper also includes case studies and real-world examples to
illustrate the practical applications and benefits of deep learning in threat detection, such
as its ability to learn from vast amounts of data, identify complex patterns, and respond to
evolving threats. Additionally, it addresses the challenges associated with implementing
deep learning models in cybersecurity, such as data quality, model interpretability, and
computational requirements. The discussion extends to future trends, including the
integration of deep learning with other advanced technologies like federated learning,
quantum computing, and explainable AI, highlighting its potential to revolutionize the
field of cybersecurity.