An Efficient Intrusion Detection Technique For Imbalanced Network Traffic Using Deep Learning

Abstract

In today's interconnected world, ensuring the security of network systems is of paramount importance. With the proliferation of network attacks, traditional intrusion detection techniques often struggle to effectively identify malicious activities, particularly in scenarios with imbalanced network traffic. In this context, this paper proposes a novel intrusion detection technique leveraging the power of deep learning to address the challenges posed by imbalanced network traffic. The proposed technique harnesses the capabilities of deep learning algorithms, specifically convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to learn intricate patterns and relationships within network data. By employing a carefully designed architecture, the model can effectively distinguish between normal and anomalous network behavior, even in scenarios where the class distribution is highly imbalanced. Experimental evaluations conducted on benchmark datasets demonstrate the efficacy of the proposed technique in detecting intrusions accurately and efficiently, outperforming traditional intrusion detection methods. Furthermore, the proposed approach exhibits robustness against various types of attacks and maintains high detection rates even in the presence of evolving threats. Intrusion detection systems (IDS) play a critical role in safeguarding computer networks from malicious activities and unauthorized access. However, traditional IDS approaches often struggle to effectively detect intrusions in scenarios characterized by imbalanced traffic, where normal network traffic significantly outweighs malicious activities. In this paper, we propose a novel intrusion detection technique leveraging deep learning algorithms to address the challenges posed by imbalanced traffic environments. Furthermore, we incorporate advanced feature extraction mechanisms to enhance the discriminative power of the model, capturing both high-level semantic information and fine-grained network characteristics.