# Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection
## 4 Experiments
### Comparison of OCC Methods  
In this experiment, IDF-MOCBLSAE is compared with several state-of-the-art OCC methods, including the stacked one-class broad learning system (ST-OCBLS) [12], multilayer one-class extreme learning machine (ML-OCELM) [10], deep one-class classification based on SVDD (DeepSVDD) [35], outlier analysis based on deep autoencoder (DeepAE) [36], and the Hybrid Ensemble Broad Learning System (SMC-OCBLS) [11]. Among them, ST-OCBLS is a stacked autoencoder variant of OCBLS, enhancing detection efficiency and accuracy through hierarchical feature learning. ML-OCELM, another stacked AE-based extension of OCELM, leverages the integration of deep autoencoders and kernel learning frameworks to improve performance on complex data and multi-class classification tasks. Deep-SVDD, on the other hand, is a deep neural network adaptation of SVDD [18], designed to strengthen its applicability as a one-class classifier. DeepAE employs a deeper architecture to enhance learning of the features for OCC tasks. SMC-OCBLS is a one-class BLS network with a maximum correntropy and boosting scheme to improve the performance. All views are concatenated into a complete feature space and the comparative OCC methods are directly applied for intrusion detection.  
The comparison results in terms of accuracy and F1-score are shown in Table 3 and Table 4, respectively, with the best-performing results highlighted in bold. It can be observed that IDF-MOCBLSAE achieves superior performance on 6 out of 10 tasks in terms of accuracy and 7 out of 10 tasks in terms of the F1-score. This performance advantage can be attributed to two key factors: (1) Existing state-of-the-art OCC methods simply concatenate multi-view features without effective collaborative learning mechanisms; (2) IDF-MOCBLSAE enhances overall detection performance by simultaneously preserving the inter-view cluster structures while minimizing intra-view reconstruction errors.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Task** & **IDF-MOCBLSAE** & **SMC-OCBLS** & **ST-OCBLS** & **ML-OCELM** & **DeepAE** & **DeepSVDD** \\ \hline KDD1 & **0.9771** & 0.9163 & 0.8531 & 0.7993 & 0.5726 & 0.9163 \\ KDD2 & 0.9309 & 0.9623 & 0.9515 & 0.8671 & **0.9707** & 0.9623 \\ KDD3 & **0.9757** & 0.9281 & 0.9221 & 0.9384 & 0.1691 & 0.9281 \\ KD4 & **0.9676** & 0.9419 & 0.9205 & 0.7883 & 0.3019 & 0.9419 \\ KD5 & **0.9428** & 0.8694 & 0.8515 & 0.8759 & 0.3945 & 0.8694 \\ UNSW1 & **0.3121** & 0.2221 & 0.22306 & 0.1552 & 0.1102 & 0.2221 \\ UNSW2 & **0.8987** & 0.8357 & 0.7889 & 0.7643 & 0.3425 & 0.8357 \\ UNSW3 & 0.7679 & **0.7856** & 0.7677 & 0.4965 & 0.4319 & 0.5493 \\ UNSW4 & **0.4784** & 0.4252 & 0.4101 & 0.2761 & 0.3602 & 0.3824 \\ UNSW5 & 0.8581 & **0.9831** & 0.9375 & 0.6581 & 0.8226 & 0.6898 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison results in terms of F1-score where the bold values indicate the best performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Task** & **IDF-MOCBLSAE** & **SMC-OCBLS** & **ST-OCBLS** & **ML-OCELM** & **DeepAE** & **DeepSVDD** \\ \hline KDD1 & **0.9552** & 0.8804 & 0.8018 & 0.7624 & 0.5799 & 0.6261 \\ KDD2 & 0.8711 & 0.9478 & 0.9326 & 0.8425 & **0.9601** & 0.8801 \\ KDD3 & **0.9525** & 0.8964 & 0.8873 & 0.9143 & 0.3616 & 0.7525 \\ KDD4 & **0.9381** & 0.9181 & 0.8891 & 0.7691 & 0.4234 & 0.604 \\ KDD5 & **0.8858** & 0.8001 & 0.7682 & 0.8119 & 0.4728 & 0.7921 \\ UNSW1 & **0.8482** & 0.8481 & 0.8415 & 0.8456 & 0.7699 & 0.7774 \\ UNSW2 & 0.9223 & **0.9455** & 0.9303 & 0.9224 & 0.8559 & 0.8681 \\ UNSW3 & 0.7139 & **0.8639** & 0.8531 & 0.7451 & 0.6817 & 0.7326 \\ UNSW4 & **0.8146** & 0.8108 & 0.8023 & 0.7868 & 0.7489 & 0.7545 \\ UNSW5 & 0.8881 & **0.9831** & 0.9404 & 0.7614 & 0.8328 & 0.7433 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison results in terms of accuracy where the bold values indicate the best performance.