Chapter 2: SecureNet: Intelligent Intrusion Detection Using Hybrid Ensemble-Based Deep Learning
Keywords:
Intrusion Detection System, Deep Learning, Hybrid Ensemble, Supervised Learning, Anomaly-Based Detection, Autoencoder, LSTM, CNN, MLP, Network Security.Abstract
Due to the widespread growth of large computer networks, the complexity and magnitude of cyber threats have also increased. Efficient detection of intrusions is important for network security. Conventional signature-based and static machine learning IDSs are often inadequate for handling dynamic traffic patterns and evolving attack styles across a variety of environments at runtime. To get through these challenges, the research presents a hybrid deep learning ensemble-based intelligent intrusion detection framework. The framework includes several deep learning architectures such as Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Autoencoder and Latent Autoencoder, enabling both supervised classification and anomaly-based detection. In the proposed approach, two models are inferred: a supervised learning-based model using MLP, CNN, and LSTM, and an anomaly-based model using Autoencoder and Latent Autoencoder. Their performance is further compared to understand the working of both models. Each model learns various properties of network traffic behavior, i.e., statistical properties, spatial patterns and temporal activity, as well as latent anomaly representations. An ensemble approach that combines the outputs of these models is used to improve the overall decision trustworthiness when tested across a number of intrusion detection datasets. Robustness against traffic variations and attack behaviours is improved with a standardised preprocessing pipeline and multi-dataset training. We test the framework on benchmark datasets such as NSL-KDD, CICIDS-2017, and UNSW-NB15. Desired outcomes would be a reduction in false alarm rates and also improved generalization across multiple datasets. The novelty of our approach is that the proposed hybrid deep learning model integrates latent feature learning with adaptive ensemble reweighting to facilitate scalable, efficient, and reliable intrusion detection in complex network scenarios.
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