Explainable AI Meets Synthetic Data: A Deep Learning Framework for Detecting Network Intrusion in NextG Network Infrastructure
In today’s digitally driven world, network security has become a top accountability as cyberattacks become more sophisticated, especially within emerging NextG network infrastructures. Advanced threats, including as zero-day exploits, polymorphic malware, and large-scale distributed denia...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11069291/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | In today’s digitally driven world, network security has become a top accountability as cyberattacks become more sophisticated, especially within emerging NextG network infrastructures. Advanced threats, including as zero-day exploits, polymorphic malware, and large-scale distributed denial-of-service (DDoS) attacks, have surpassed traditional Network Intrusion Detection Systems (NIDS), which frequently use out-of-date signature-based methodologies. These conventional methods not only struggle to detect unknown attack patterns but are also hindered by the issue of imbalanced datasets, where minority attack classes are underrepresented and frequently overlooked. To address these challenges, we proposes an innovative NIDS framework tailored for NextG networks that combines Generative Adversarial Networks (GANs) with Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) models. The framework utilizes GANs to generate synthetic samples for minority classes, ensuring a balanced dataset and enhancing the detection of underrepresented attack types. The CNN and LSTM models, applied independently, leverage their respective strengths to extract spatial and temporal features from network traffic, achieving robust classification accuracy. Furthermore, we integrate Local Interpretable Model-Agnostic Explanations (LIME) to make model predictions transparent, increasing trust and usability for practical deployment. Our framework is evaluated on the NF-CSE-CIC-IDS2018 dataset and achieves outstanding results. The LSTM model attains a detection accuracy of 99.67%, while CNN achieves 97.45%. On this GitHub Repository: <uri>https://github.com/i-am-junayed/XAI-Intrusion-Detection-System</uri>, the entire data analysis and prediction method is available for use by anyone. Here, both models perform exceptionally well in detecting minority attack classes, with the LSTM showing superior consistency across all metrics. This framework’s high accuracy, explainability, and adaptability make it a critical tool for securing dynamic and high-speed NextG networks against evolving cyber threats. |
|---|---|
| ISSN: | 2169-3536 |