Deep Learning for Anomaly Detection in High-Dimensional Data: Applications in Cybersecurity
Keywords:
deep learning, anomaly detectionAbstract
The advent of big data has posed significant challenges in various fields, particularly in cybersecurity, where the volume and complexity of data make it increasingly difficult to identify anomalies indicative of potential threats. This paper explores the application of deep learning techniques for anomaly detection in high-dimensional datasets, focusing on their effectiveness in real-time threat identification and breach prevention in cybersecurity. We discuss various deep learning architectures, including autoencoders, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), that have shown promise in handling high-dimensional data. The paper further examines the limitations of traditional methods and highlights how deep learning approaches can enhance detection accuracy. By reviewing recent studies and case applications, this research aims to provide insights into the evolving landscape of cybersecurity and the critical role of deep learning in safeguarding sensitive information.
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