# Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection
## 2 Related Works  
In scenarios of extreme class imbalance, one-class classification (OCC) methods have been extensively investigated. These methods exclusively utilize samples of the target class during the training phase to develop discriminative models, which subsequently enable the detection of anomalous samples deviating from the target class during testing. Current research in OCC can be systematically categorized into three primary approaches: traditional machine learning-based, neural network-based, and multi-view OCC approaches. Traditional machine learning-based OCC approaches were proposed previously which aimed to modify classic classification models to adapt to OCC scenarios. For example, [PERSON] et al. [17] proposed a classical one-class classification algorithm based on the principle of a support vector machine (SVM). [PERSON] et al. [18] proposed the support vector data description (SVDD), which learned a decision boundary in the kernel space by minimizing the distance from the normal samples to the boundary. [PERSON] et al. [1] improved the one-class SVM using vector quantization techniques, which enhanced the generalization across different OCC tasks. [PERSON] et al. [19] combined one-class SVM with a long short-term memory network to improve the ability to discern anomalies in time-series data. [PERSON] et al. [20] designed a one-class Bayesian model for detecting HTTP attacks, reducing computational complexity while improving detection accuracy. [PERSON] et al. [21] introduced a clustering-driven SVDD algorithm that constructed multiple spheres in the kernel space via K-means to identify anomalous data. [PERSON] et al. [3] proposed boundary-based fuzzy-SVDD by introducing a local-global center distance to identify boundary-proximate samples, which led to a more accurate classification boundary. Neural network-based OCC approaches have garnered significant research interest in recent years, demonstrating superior detection capabilities in complex data environments. Many one-class deep neural networks have been proposed. For example, [PERSON] et al. [6] presented a bi-directional generative adversarial network (GAN) for latent feature extraction of normal patterns. [PERSON] et al. [7] designed a dual-iteration GAN framework with auxiliary losses to minimize the intra-class variance of normal samples. [PERSON] et al. [5] proposed a calibrated one-class learning method which combines uncertainty modeling and synthetic anomalies to enhance contamination-robust normality boundary learning in anomaly detection. [PERSON] et al. [22] introduced multilayer one-class learning via hierarchical autoencoder optimization, combining layer-wise feature refinement and centroid-aligned training for enhanced anomaly detection across deep representations. [PERSON] et al. [4] proposed a multitask deep one-class convolutional neural network combining an autoencoder for feature learning and a moving-window scanning approach for detection. [PERSON] et al. [8] designed an end-to-end framework of deep transfer one-class classification, which combined adversarial generation of pseudo-negative samples with log-Euclidean manifold alignment of cross-domain positive samples to enhance discrimination and representation learning. [PERSON] et al. [23] embedded time series into a latent space where the Euclidean distances matched the original dissimilarity measures using autoencoder and encoder-only neural networks. Furthermore, a series of one-class flat-structure neural networks were explored based on the extreme learning machine (ELM) [24] and the broad learning system (BLS) [25]. For example, [PERSON] et al. [12] developed a stacked one-class BLS framework to separately extract normal traffic features and detect anomalies. [PERSON] et al. [11] introduced a one-class BLS network with a maximum correntropy and boosting scheme to improve performance. [PERSON] et al. [26] proposed a hierarchical ELM-based approach leveraging maximum mutual information to improve adversarial robustness. [PERSON] et al.