## 2 Related Work  
Network security intrusion detection is a relatively broad area of research. Existing models used in the field of intrusion detection include Convolutional Neural Networks, Recurrent Neural Networks, machine learning, and hybrid models. Scholars have used a variety of different approaches to address the problems of low detection accuracy and difficulty in detecting a few classes of samples in the field of intrusion detection. Convolutional neural networks are primarily used in tasks related to image and video analysis, such as image classification, face recognition, target recognition, image processing and so on. Furthermore, in recent years it has also been widely used in the field of intrusion detection. A recurrent neural network is mainly used in various tasks of connected handwriting recognition and speech recognition. It is also commonly used in the field of intrusion detection due to its effectiveness in processing time series data. In terms of improving detection accuracy: [PERSON], [PERSON] et al. used a combination of particle swarm optimization algorithms, ant colony algorithms and genetic algorithms for feature selection to reduce the feature size of the training data, followed by a secondary classification method to detect abnormal behavior in the network [29]. [PERSON], [PERSON] et al. combined a traditional learning classifier system with a convolutional neural network for the detection of anomalous behavior, and the proposed system has adaptive and learning capabilities [30]. [PERSON], [PERSON] et al. first performed feature selection via the SFSDT model, followed by classification via recurrent neural networks, achieving better results on both the NSL-KDD dataset and the ISCX dataset [31]. [PERSON], [PERSON] et al. proposed an intrusion detection system based on CNN and a weight-dropped long short-term memory network, and achieved more satisfactory results [32]. In terms of addressing the class imbalance: [PERSON], [PERSON] et al. compared existing sampling methods and found that EasyEnsemble performed better in resolving sample imbalance [33]. [PERSON], [PERSON] et al. divided the dataset into hard and easy sets by ENN, and reduced the imbalance of the original dataset by processing the samples in the hard set through the K-Means algorithm [34]. [PERSON], [PERSON] et al. identified anomalous traffic with good accuracy by an improved density peak clustering algorithm [35]. The recent work related to network intrusion detection using optimization algorithms, deep learning algorithms, and machine learning algorithms is given in Table 1. \begin{table}
\begin{tabular}{c c c c c} \hline
**Ref.** & **Dataset** & **Methods** & **Evaluation Metrics** & **Accuracy** \\ \hline
[36] & N-BaIoT & LGBA-NN & precision, recall & \multirow{2}{*}{Gained 90\% accuracy} \\  & UNSW\_NB15 & & F1-score, support & \\  & Boun Ddos & & & confusion matrix & 99.7\% of the Ddos attack \\
[38] & CSE-CIC-IDS2018 & HRCNNIDS & precision, recall & \multirow{2}{*}{Gained 97.75\% accuracy} \\  & NSL-KDD & SSC-OCSVM & & & recall, FAR, ROC \\  & NSL-KDD 2015 & & & confusion matrix & \\  & CIDDS-001 & & & precision, recall & \\
[41] & NSL-KDD & Proposed DBN & & F1-score, accuracy & \\  & NSL-KDD & & & & \\  & CICIDS2017 & & SRRS + IIFS-MC(ALL) + CTC & & 99.96\% of NSL-KDD \\  & CICIDS2017 & & & & 99.95\% of CICIDS2017 \\
[42] & UNSW-NB15 & & NIDS-s & & \\  & & & & & \\ \end{tabular}
\end{table} 
Table 1: A brief description of the recent related works on network intrusion detection, using several optimization, deep, and machine learning algorithms. To effectively select feature subsets and hyperparameters, [PERSON] et al. [36] proposed a local-global optimal neural network Bat algorithm (LGBA-NN). They tested on the N-BaloT dataset and compared with several recent advanced methods, such as the particle swarm optimization (PSO) algorithm and Bat algorithm. The experiments demonstrate that the LGBA-NN algorithm has a significant improvement in the detection accuracy of multi-class botnets up to 90%. [PERSON] et al.