# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 2 Related Work  
Many deep-learning methods have been used to detect and stop DDoSA. In this study, hybrid models that combine methods and algorithms enhanced DDoS attack detection. Many researchers have evaluated deep-learning methods for DDoS attack detection. In a CSE-CIC-IDS2018 research study, simple neural networks beat CNNs and RNNs. Studies simulated brute force, DoS, penetration, DDoS [14], the Heartbleed vulnerability, and botnet incidents. RNN and CNN models are often overfitted, resulting in more false positives and negatives. The core neural network obtained 82% accuracy and 42% precision. Investigations were conducted to assess the performance of several algorithms, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), and K-Nearest Neighbor (KNN) [15]. We used feature selection techniques to improve training, interpretation, and model simplicity. The KNN classifier and wrapper feature selection were integrated to obtain the highest accuracy of 98.3% in recognizing DDoSAs [15]. Deep learning and feature selection must be combined to minimize processing time and increase detection effectiveness. A comparison with different DL methods led to the recommendation of utilizing GRU for DDoSA identification. On the CICIDS17 and NSL-KDD19 datasets, the GRU-RNN model performed very well with an accuracy of 89% and 91%, respectively [16]. A separate study found that the CNN-RF model attained an accuracy rate of 99.63% and the RF-MLP model of 99.58% on the same dataset. Many DL hybrid approaches reliably provide the desired results. Using deep learning algorithms in hybridization techniques has improved intrusion detection systems (IDS) and DDoS detection. These approaches handle difficulties such as dynamic network traffic data, the need for several classifiers to recognize different attacks, and changing attack patterns. A genetic approach, wrapper feature selection, and K-means clustering may be used to reduce data dimensionality. Using a support vector machine to analyze secret data improves detection accuracy. A different solution uses the Bat algorithm and PCA to identify features, while blockchain and machine learning handle datasets and detect network intrusions [17]. Multi-algorithm hybrid deep learning models have excelled. Research combining Random Forest (RF) and Convolutional NN (CNN) algorithms improved DDoS detection in IoT networks, achieving a 99.63% accuracy rate [18]. Another method used CNN and information entropy to detect DDoSA by distinguishing between valid and malicious network data. Combining CNN algorithms' accuracy with information entropy's simplicity, our hybrid model obtained 98.98% detection accuracy [19]. It has also been shown that deep learning algorithms work with various security methods. Stacked Autoencoders (SAE) may enhance DDoS detection accuracy with Snort IDS, achieving a 95% accuracy rate. A deep belief network (DBN) and CNN hybrid model have also shown promising results in DDoS attack detection. These models achieved a 96.7% detection rate and a 1.2% false positive rate on a simulated dataset [20]. The authors of [21] created a hybrid ensemble technique-based intrusion detection system for the Internet of Things. Using data privacy measures via homomorphic encryption, the authors addressed the limitations of centralized deployment. Three support vector machine (SVM) models were created, considering the characteristics of bidirectional, unidirectional, and packet network traffic. The essential features of the network traffic were chosen using a genetic algorithm (GA) to train these support vector machine (SVM) models. The computing resources needed for training data were reduced when the dimensionality of network traffic was reduced. An SVM model puts data into a gradient-boosting model, which is used for attack prediction. However, the model's parameters' values are unknown. Despite the lack of IoT DDoS attacks in the MQTT dataset, the solution was evaluated using it.