# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 2 Related Works
An intrusion detection system (IDS) was developed in [9] based on the deep learning algorithm known as the deep forward neural network (DFNN). The dataset used in developing the model is the CICDDOS2019 dataset, which contains the most recent DDoS attacks. The proposed model was optimized using the Bat optimization algorithm and had high evaluationmetrics compared to RNN, LSTM, and ensemble models. The model achieved an accuracy of 98.87%. [PERSON] and [PERSON] [20] suggested an approach of an intrusion detection system based on mixing two deep learning algorithms, convolutional neural network (CNN) and long short-term memory (LSTM), known as the hybrid method. This approach first used CNN layers for feature extraction from the input automatically and then used the LSTM for predicting sequence, and it has seven layers to achieve high performance and accuracy when compared with each CNN and LSTM individually. Therefore, evaluation criteria were applied to the NSL-KDD dataset. The evaluation of the results of four criteria, which were accuracy, precision, recall, and F1-score, showed that the achieved accuracy was 99.20%. The authors [21] proposed a deep learning approach for intrusion detection using a recurrent neural network (RNN-IDS). They used the NSL-KDD dataset to evaluate their performance in detecting network intrusion. The performance of this model is in binary classification, which identifies network traffic behavior as normal or anomalous, and multiclass classification, which classifies network traffic into five categories, which are normal or any one of the other four attack types: user-to-root (U2R), probe (Probing), denial of service (DOS), and root to local (R2L). The results compared with other traditional machine learning methods in terms of accuracy showed that RNN-IDS outperformed them. The authors [22] constructed a novel network intrusion system (NIDS) based on convolutional neural networks (CNNs). They trained deep learning based on detection models using extracted features and original network traffic. This model achieved a detection accuracy of 96.55% using the feature dataset of CICID 2017. The results showed that this CNN-based model outperforms SVM and DBN in accuracy. The authors in [23] presented an efficient method to detect DDoS attacks using anomaly detection by the DL algorithm, which was long short-term memory (LSTM). The LSTM is a specialized form of a recurrent neural network (RNN) that solves the problem of vanishing and exploding gradients with decreased training time and increased accuracy. This algorithm was trained and tested on the CICIDS2017 dataset, consisting of numerous attack types. The paper focused on four layers: data collection, feature identification, model training, and execution of the classification model. The proposed training model contains one input layer of LSTM with 77 neurons and a tanh activation function. Then, it has 12 hidden layers, 5 of which are LSTM, and 7 others are fully connected layers. Finally, the output layer has two fully connected neurons with a sigmoid activation function. These 14 recurrent neural network model layers provided an accuracy of 96.25%. [PERSON] and [PERSON] [24] presented a deep learning approach for an intrusion detection system (IDS) based on a deep autoencoder (DAE), which is one of the most well-known deep learning models. The architecture of this model consists of four autoencoders, where the output of each autoencoder is used as input to the next autoencoder. In addition, they used unsupervised feature learning in the training phase, which is a greedy layer-wise pre-training that involves training each layer of the neural network as a separate autoencoder and that helps to improve the deep model performance and supervised fine-tuning. After training four autoencoders, they used the SoftMax classifier to classify the attack classes from the input dataset. They performed numerous experiments on the KDD-CUP'99 dataset, and the proposed approach achieved a detection accuracy of 94.71%.