# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 2 Literature Review
in [35], a hybrid DL approach was utilized to increase accuracy and arrive at a better and more helpful direction. Two different DL classifier types were employed in these methods. The authors combined a recurrent neural network and a gated recurrent unit to create a hybrid approach known as GRU-RNN. It was based on the NSL-KDD dataset and employed six features to train the classifier [17]. The hybrid approach method showed superiority over the prior way and demonstrated accuracy gains of 89%. Its implementation in the SDN working environment was also made simple and adaptable. Building intrusion detection systems for SDN were the aim of a separate study in [36]. The researchers used ML and DL systems to compare the results. Six different types of attacks were classified using a gentle approach. The approach used a DL algorithm (GRU); the algorithm outperformed ML classifiers in terms of accuracy and performance. More than one type of dataset was used in training and comparison; the approach was very successful, indicating the possibility of applying DL in NIDS to SDN with excellent efficiency. This study's author [37] presented a hybrid ML system to boost the system's accuracy (decision trees with SVM techniques). The many different kinds of known assaults were categorized using the decision tree method [38; 39]. The normal data were categorized using the SVM algorithm. This model was developed using data from the NSL-KDD dataset. This system was accurate 96.4 percent of the time. A GA and SVM were suggested by [PERSON] et al. [40] to be used to find the intrusion packets. Select features were combined using SVM and GA. Researchers utilized SVM to address classification and regression issues. The detection accuracy for the KDD Cup 1999 dataset used in this experiment was 97.3% [41]. The researchers created a network-detecting method using the NSL-KDD dataset and an RNN methodology [42]. The accuracy of binary classification in this work was 83.28%, and that of multiclass classification was 81.29%. A CNN was used as the suggested method in [17] to identify network breaches. The KDD Cup 1999 dataset was used to generate new datasets, and test data for the CNN-IDS model were two-dimensionalized. This model had a detection rate of 97.7%. Researchers in [43] used ANN for network IDS with the KDD Cup 1999 dataset. The data were normalized using the min/max approach, and the number of attributes was minimized using PCA [44]. In this study, ANN was designed using FFNN and LM backpropagation with mean squared error as the loss function. This model was accurate 97.97% of the time. DNN with NSL-KDD datasets was utilized in the system proposed in [45]. They suggested using label encoders, min-max normalization, and an auto-encoder network for pre-processing and training the DL layers. Five categories were used to develop this model. The DOS assault had the highest accuracy detection rate in this area, at 97.7%, versus 89.8% for the probe. The KDD Cup 1999 dataset was used to analyze and validate the DNN-based AI IDS proposed by the researchers in [46]. Four hidden layers were utilized to build the neural network, with the Adam optimizer used for back-propagation training and the ReLU mechanism employed as the convolution operation in forwarding [47; 48]. The accuracy of the classification, which could either be normal or an attack, was 99.08%. In [38], a DNN system using a DARPA 1999 dataset was suggested. The output layer only had two neurons, since ReLU was the hidden layer's non-linear activation function. There was a 93% accuracy rate. DNN is employed in the method proposed in this paper to identify network intrusion packets, since it is both pertinent and efficient. This algorithm gives each feature in the input layer the proper weights, using those weights to make judgments. As a result, it is more appropriate for novel signatures than rule-based ones. The one-hot encoder method and z-score normalization were both used in the pre-processing phase of the data in this paper.