A Multi-Class Intrusion Detection Model using an Ensemble of Deep Learning Techniques

Abstract

In the ever-evolving landscape of cybersecurity, intrusion detection systems (IDS) play a critical role in identifying and mitigating malicious activities within network environments. Traditional IDS approaches, predominantly based on signature and anomaly detection methods, have demonstrated limitations in detecting novel and sophisticated attacks, especially in a multi-class scenario where multiple types of intrusions coexist. This study explores the development of a robust multi-class intrusion detection model utilizing an ensemble of deep learning paradigms. The proposed model leverages the strengths of various deep learning architectures, including Deep Belief Network (DBN), Convolutional Neural Networks (CNNs), and Long Short-Term Memory - Recurrent Neural Networks (LSTM-RNN). By integrating these diverse models into an ensemble framework, the system aims to enhance detection precision, F1-Score and Recall rates, and improve overall system robustness. The ensemble approach combines the predictive capabilities of individual models, thereby capturing complex patterns and correlations that single models might miss. Comprehensive experiments are conducted on UNSW-NB15 benchmark datasets to evaluate the performance of the ensemble model. Results indicate a significant improvement in multi-class Recall, F1-Score, Precision and detection rates of 0.6 0.655, and 0.605 respectively compared to the traditional intrusion detection methods, such as Decision Trees, Naive Bayes, or Support Vector Machines (SVMs), which are often used as benchmarks in this domain. For instance, Naive Bayes classifiers generally have lower precision, often in the range of 0.50 to 0.55, due to their simplistic assumptions about feature independence, which can lead to misclassifications in multi-class scenarios. Similarly, Decision Trees, although capable of achieving higher recall rates, often fall short on precision (around 0.55 to 0.60) due to overfitting on certain attack types. The study also addresses key challenges such as data imbalance considerations. This research underscores the potential of ensemble deep learning methods in advancing the efficacy of intrusion detection systems. The findings contribute to the ongoing efforts in cybersecurity to develop more resilient and adaptive defense mechanisms against an increasingly sophisticated threat landscape. Future work will focus on optimizing the ensemble model for real-time applications and exploring the integration of advanced techniques like transfer learning and federated learning to further enhance detection capabilities.