## 2 Related Work
[37] first transformed the original network data into four-channel (Red, Green, Blue, and Alpha) images, which were later divided into a training and a testing set. The obtained images were classified based on the ResNet50 model and evaluated on the UNSW_NB15 and BOUN_Ddos datasets. The experimental results demonstrate that the proposed model has a high accuracy in detecting anomalous attacks. In response to the ongoing and changing malicious threats in the current cyber environment, [PERSON] [38] proposes a hybrid intrusion detection system based on deep learning (HRCNNIDS). The authors use convolutional neural networks to capture regional features and temporal features through recurrent neural networks and evaluate the proposed system based on CSE-CIC-IDS2018 dataset. The experimental results show that the method has a high detection rate for malicious attacks. In recent years, the number and complexity of attacks on network environments have continued to rise, and [PERSON] et al. [39] proposed an unsupervised anomaly detection method for network intrusion detection work. They combined subspace clustering (SSC) and a class of support vector machines (OCSVM) to achieve better performance on the NSL-KDD dataset. [PERSON] et al. [40] introduced blockchain data transfer technology to the field of intrusion detection to both secure data and improve detection efficiency. Their proposed model uses sensors to collect data, after which intrusion detection is performed through deep belief networks. In addition, the model ensures privacy and security by sharing the created model. The authors evaluated the proposed model through NSL-KDD 2015 and CIDDS-001 datasets and achieved better results. To address the existing problem of imbalanced intrusion detection data samples, [PERSON] et al. [41] proposed a host-based intrusion detection algorithm. They first generate balanced samples from high-level imbalanced datasets in the preprocessing stage by an improved random sampling mechanism. Then the datasets are filtered by an improved multi-class feature selection mechanism. Finally, a merged tree construction algorithm based on the C4.5-based detector is built. The experimental results show that their proposed algorithm achieves a very high detection accuracy. In their study, [PERSON] et al. [42] fully considered the impact of sampling techniques on model performance and compared two feature selection techniques based on information gain and correlation, and finally proposed the multi-stage optimization of an intrusion detection system based on machine learning. In this system, they first sample the dataset by the SMOTE algorithm, followed by feature selection, and finally optimize the parameters of the model by the optimization algorithm. Experiments through evaluation on the UNSW_NB15 dataset and CIC-IDS2017 dataset demonstrate that the proposed model significantly improves the detection accuracy while reducing the training sample size and feature set size. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Dataset** & **Methods** & **Evaluation Metrics** & **Accuracy** \\ \hline
[43] & KDDCUP1999 & CSWC-SVM & precision, accuracy & Obtained satisfactory results \\
[44] & UNSW\_NB15 & FSL-SCNN & precision, recall & Obtained satisfactory results \\  & & & F1-score, FAR & \\
[45] & UNSW\_NB15 & VLSTM & precision, recall & \\  & & & F1-score, FAR, AUC & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._A study [43] introduces sample-weighted and class-weighted algorithms into support vector machines to solve the problems faced by intrusion detection. Experimental results show that the algorithm can achieve the advantages of short time consumption, high recognition accuracy, low false alarm rate and high classification accuracy in different situations. [PERSON] et al. [44] propose a few-shot learning model based on a Siamese convolutional neural network for intrusion detection tasks.