Improving Intrusion Detection Accuracy Using Deep Learning Algorithms in Enterprise Network Systems
Keywords:
Intrusion Detection System, Deep Learning, Enterprise Network Security, Cyber Threats, Neural Networks, Anomaly DetectionAbstract
In modern enterprise networks, the proliferation of sophisticated cyber threats has necessitated intelligent and adaptive security systems. This study explores how deep learning algorithms can enhance intrusion detection system (IDS) accuracy within enterprise networks. By leveraging neural architectures like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid models, this paper evaluates improvements in anomaly detection rates and reduced false positives. A comparative analysis is presented using benchmark datasets. The results demonstrate significant advancements over traditional machine learning techniques, underscoring deep learning’s role in bolstering enterprise network security infrastructure.
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Copyright (c) 2025 Tolulope Udeze (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
