# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 2 Related Works
Another study [25] mentioned developing a model for detecting DDoS attacks in cloud environments using a supervised learning algorithm based on the intrusion detection system (IDS). They proposed two approaches: the filter method represented by learning vector quantization (LVQ) and the dimensionality reduction method defined by principal component analysis (PCA), and the dataset was collected from NSL-KDD. The selected features from each approach were used for classification, and the classifiers were naive Bayes (NB), support vector machine (SVM), and decision tree (DT). Based on the experiment, LVQ, for feature selection with the DT model, identified the attacks more accurately than other methods. The authors evaluated this model using the DDoS-2020 and NSL-KDD datasets. The results of this proposed model achieved high accuracy with the random forest algorithm, reaching 99.38% with the DDoS-2020 dataset and 99.30% with the NSL-KDD dataset. An intrusion detection system (IDS) was proposed [26], implemented, and trained using different deep neural network architectures, including convolutional neural networks, autoencoders, recurrent neural networks, and LSTM. These deep learning models were trained on NSL-KDD training datasets and evaluated on both datasets provided by NSL-KDD for testing. They used conventional ML IDS models with different well-known classification techniques, including decision tree, KNN, random forest, support vector machine, and naive Bayes, to compare with deep neural network models. The authors in [27] developed an intrusion detection system (IDS) utilizing a deep learning (DL) algorithm, specifically a multi-layer perceptron (MLP) neural network, trained and tested on the KDDCup99 dataset. The detection process involved several steps to maximize accuracy and minimize loss. Initially, the KDDCup99 dataset was collected and preprocessed using techniques like one-hot encoding and feature scaling (standardization). The preprocessed data were then used to create a pre-trained MLP model with two layers: an input layer using the RELU activation function and an output layer using the SoftMax activation function. The loss function was cross-entropy, optimized with the Adam optimizer. The output layer has five nodes representing the following attack classes: DoS, probe, R2L, U2R, and normal. The experiments demonstrated that the model achieved an accuracy of 91.4%. The authors of [28] proposed an adaptive ensemble learning model for the field of intrusion detection systems (IDSs). The primary purpose of using an ensemble learning model is to integrate the advantages of each algorithm for different types of data detection and achieve optimal results through ensemble learning. This model used the NSL-KDD dataset and some common machine learning algorithms such as decision trees, random forests, SVMs (support vector machines), logical regression, k-nearest neighbors (KNN), Adaboost, and deep neural networks to train it. Then, an adaptive voting algorithm with different class weights was used to obtain the optimal detection results. They used the NSL-KDD Test+ dataset to verify their approach, and the accuracy of the proposed adaptive voting algorithm was 85.2%. [PERSON], [PERSON], and [PERSON] [29] introduced the PSGCN (phase space graph convolutional network) model, which uses deep learning to analyze complex networks by converting time series data into graph structures. This approach is relevant to IDS-based CNN algorithms in cloud environments, where CNNs analyze network traffic patterns to detect intrusions. The comparison highlights the potential of advanced deep learning models to enhance the accuracy and robustness of complex network analyses. Table 1 summarizes various ML and DL approaches for intrusion detection systems with different datasets. The next table shows that some researchers provide highly efficient results using DL for intrusion detection systems.