AN ADAPTIVE DEEP LEARNING APPROACH FOR REAL-TIME INTRUSION DETECTION

Abstract

In an increasingly digitized world, safeguarding network environments against diverse cyber threats has become imperative. This paper presents an Adaptive Deep Learning Framework for Real-Time Network Intrusion Detection, developed to enhance the detection and classification of intrusions while accommodating the evolving nature of network traffic. The proposed framework integrates a Stacked Convolutional Autoencoder for effective feature extraction and dimensionality reduction, coupled with a Gated Convolution mechanism to capture temporal dependencies within data streams. To address challenges such as class imbalance and the scarcity of labeled data, we employ a Conditional Generative Adversarial Network (CGAN) for synthetic data generation, enriching the training dataset and improving model robustness. CGAN is employed to generate synthetic data for minority classes, thereby augmenting the training dataset and improving the detection capabilities of the model. This approach significantly enhances the robustness of our system against various types of attacks, including zero-day vulnerabilities and polymorphic threats Our experimental results demonstrate that the proposed system achieves a high detection rate with significantly reduced false positives, thereby ensuring a reliable defense against unauthorized access and other cyber threats. This research contributes to the field of cybersecurity by providing an adaptive solution that not only enhances detection performance but also enables real-time analysis, making it suitable for deployment in dynamic network environments.