Large-Scale and Robust Intrusion Detection Model Combining Improved Deep Belief Network With Feature-Weighted SVM

Abstract

In a large-scale data environment, the “curse of dimensionality” of high-dimensional feature spaces and the large amount of noisy data make the efficiency and accuracy of intrusion detection systems (IDSs) significantly decrease. To address these challenges, the underlying algorithm can not only reduce dimensionality, but also remove some redundant and irrelevant noise data from the massive data. Accordingly, herein, an IDS combining deep belief network (DBN) with feature-weighted support vector machines (WSVM) is proposed. First, an adaptive learning rate strategy is applied to promote the training performance of the IDBN, which is used for learning deep features from raw data for reducing dimensionality. Second, the particle swarm optimization algorithm is used to optimize the SVM, followed by the determination of the weights of deep features and the best parameters of the Gaussian kernel, resulting in WSVM which can remove weakly related and redundant features from all IDBN-extracted features. The NSL-KDD dataset was used to validate the IDBN-WSVM model. In particular, the model performance was studied and compared to a model comprising a non-weighted SVM and other machine learning methods. Experimental results demonstrate that IDBN-WSVM is well-suited for designing high-precision classification models. The proposed improved model achieves accuracies of 85.73% and 82.36% in binary- and five-category classification experiments, respectively, which is better than or near state-of-the-art method. The IDBN-WSVM model not only saves training time and testing time on large-scale datasets, but also is more robust and has better performance of generalization than traditional methods, which provides a new research method that achieves high accuracy in intrusion detection tasks.

Keywords

• Intrusion detection
• feature redundancy
• deep belief network
• feature extraction
• NSL-KDD