# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 3 Related Work  
Over recent decades, ML and DL approaches have been widely utilized regarding the security of the network due to their capacity to distinguish data [18; 20; 21; 22]. Previously, researchers have employed a variety of ML- and DL-based techniques for ID. Using the KDDCUP ID dataset, [PERSON] et al. [23] used the K-Nearest Neighbor (KNN) for the identification of network anomalies and assessed the effectiveness of the suggested ID system. [PERSON] et al. [24] used different versions of support vector machine (SVM), including quadratic and linear Gaussians, to evaluate the efficiency of SVM approaches on the NSL-KDD dataset. [PERSON] et al. [25] proposed an integrated ID system employing correlation-based feature selection and the artificial neural network (ANN). Using the datasets of UNSW-NB15 and NSL-KDD ID, the authors conducted an experimental study. [PERSON] et al. [26] proposed a Random Forest (RF)-based ID system, while [PERSON] et al. [27] proposed a system for identification based on multiple conventional machine learning classification algorithms. Prior methodologies applied within the scope of ID, however, had inefficient classification effectiveness, with a greater FP and a poor detection rate (DR) in the ID system. Utilizing non-symmetric deep auto-encoder for network intrusion detection problem, [PERSON] et al. [28] conducted the experiments using the benchmark dataset KDD CUP'99. In another study, a one-dimensional convolutional neural network (1D-CNN) based deep learning system was proposed by the authors [29] for network intrusion detection. The authors used the benchmark CICIDS2017 dataset for conducting the experiments, while [PERSON] et al. [30] proposed network intrusion detection and classification system using AdaBoost-based approach. The authors used a UNSW-NB 15 dataset for network anomaly detection. The experimental findings showed that proposed method effectively detects different forms of network intrusions on computer networks. Deep learning is a subfield of machine learning consisting of concealed layers to determine the features of the deep network. These approaches are more successful than ML [24] owing to their comprehensive structure and capacity to grasp the relevant aspects of the dataset independently and provide an output. Recently, DL has gained popularity and is being applied for ID; studies indicate that DL outperforms traditional methods. [PERSON] et al. [31] employed the DL technique for the anomaly based on flow identification developed on a deep neural network (DNN), and the results of experiments demonstrated that DL may be utilized for anomaly identification in networks. [PERSON] et al. [21] proposed a decentralized intrusion detection solution for cloud environments. First, the Naive Bayes model was applied to identify anomalies for data preprocessing; subsequently, for multi-classification, RF was used to determine the pattern of each attack. Using the CICDDS-001 dataset, experiments were carried out using variables such as false-positive rate (FPR) and precision. [PERSON] et al. [32] introduced an IDS composed of multiple vector classifiers for wireless mesh support. Utilizing genetic-algorithm-based feature selection and SVM classification, the authors chose particular traits to boost efficiency. The system was evaluated using a WMN-generated intrusion dataset and was simulated using a standard intrusion dataset in the network simulator-3 (NS3) simulator. The CICIDS 2017 intrusion dataset was used to assess the model. [PERSON] et al. [33] proposed an ensemble-based approach for network anomaly identification in an intrusion detection system. This method utilizes a combination of learning and forecasting mechanisms to classify anomalies into various classes. Initially, researchers employed the ANOVA F-test considering the strategy of univariate feature selection [34; 35] to determine the performance of features and the link between class labels and data characteristics.