Addressing Modern Cybersecurity Challenges: A Hybrid Machine Learning and Deep Learning Approach for Network Intrusion Detection

The rapid increase in the number of Internet of Things (IoT) devices, coupled with a rise in sophisticated cyberattacks, demands robust intrusion detection systems. This study presents a holistic, intelligent intrusion detection system. It uses a combined method that integrates machine learning (ML) and deep learning (DL) techniques to improve the protection of contemporary information technology (IT) systems. Unlike traditional signature-based or single-model methods, this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification. This combination provides a more nuanced and adaptable defense. The research utilizes the NF-UQ-NIDS-v2 dataset, a recent, comprehensive benchmark for evaluating network intrusion detection systems (NIDS). Our methodological framework employs advanced artificial intelligence techniques. Specifically, we use ensemble learning algorithms (Random Forest, Gradient Boosting, AdaBoost, and XGBoost) for binary classification. Deep learning architectures are also employed to address the complexities of multi-class classification, allowing for fine-grained identification of intrusion types. To mitigate class imbalance, a common problem in multi-class intrusion detection that biases model performance, we use oversampling and data augmentation. These techniques ensure equitable class representation. The results demonstrate the efficacy of the proposed hybrid ML-DL system. It achieves significant improvements in intrusion detection accuracy and reliability. This research contributes substantively to cybersecurity by providing a more robust and adaptable intrusion detection solution.