Intrusion Detection System of Imbalanced Network based on Deep Learning Technique: A Study
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Abstract
Intrusion Detection Systems (IDS) play a critical role in safeguarding network security. However, the imbalanced nature of network traffic, where malicious activities are rare compared to normal behavior, poses significant challenges for accurate detection. This study explores the application of deep learning techniques to address the imbalance problem in network-based IDS. It reviews data balancing methods, deep learning architectures, and evaluation metrics effective in enhancing detection performance, particularly for minority attack classes. The study highlights potential solutions and future research directions for building robust and efficient IDS in imbalanced network environments.
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