# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 3 Material & Methods  
Figure 3 presents a clear overview of the proposed approach. The proposed approach starts with the NSL-KDD dataset, which forms the foundation of the intrusion detection system. Several pre-processing steps are used to prepare the data for modeling. Data cleaning to remove inconsistencies or errors, one-hot encoding to represent category fea \begin{table}
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**Ref.** & **Algorithm/Dataset** & **Method Used** & **Methodology** & **Findings** \\ \hline \multirow{8}{*}{[12]} & Dataset for NSL–KDD. & \multirow{8}{*}{Supervised Learning} & \multirow{8}{*}{Confusion matrix, LSTM, RNN, DLIND, Adaptive Synthetic Sampling (ADASYN), and SMOTE data augmentation} & \multirow{8}{*}{89.45\% F1 score and 91.4\% accuracy} \\  & KDDTest+ had 21,543 traffic & & & \\  & samples, while the training & & & \\  & set had 125,983 traffic & & & \\  & samples. In addition, its training set included 19 & & & \\  & different kinds of attack & & & \\  & datasets from KDD Cup & \multirow{2}{*}{Supervised Learning} & \multirow{2}{*}{ML, MLP, LSVM, LSTM, and DL} & \multirow{2}{*}{Accuracy of 80.12\% accuracy: RF—99.5\%, ModelP, Fover, and Similarity Matrix} & \multirow{2}{*}{90.8\% F1 score and 91.4\% accuracy} \\  & Dataset NSL–KDD & \multirow{2}{*}{Supervised Learning} & \multirow{2}{*}{ML, MLP, LSVM, LSTM, and DL} & \multirow{2}{*}{Accuracy of 82.34\% and a multi-classification accuracy of 80.12\% accuracy of 80.12\% accuracy: RF—99.5\%, ModelP, Fover, and Similarity Matrix} & \multirow{2}{*}{Accuracy of 82.34\% and a multi-classification accuracy of 80.12\% accuracy: RF—99.5\%, ModelP, Fover, and Similarity Matrix} & \multirow{2}{*}{Accuracy of 80.12\% accuracy: RF—99.5\%, ModelP, Fover, and Similarity Matrix} & \multirow{2}{*}{Accuracy of 80.12\% accuracy: RF—99.5\%, ModelP, Fover, and Similarity Matrix} & \multirow{2}{*}{Accuracy of 80.12\% accuracy: RF—99.5\%, ModelP, Fover, and Similarity Matrix} \\  & Datasets NSL–KDD and UNSW-NB15. & \multirow{2}{*}{Supervised Learning} & \multirow{2}{*}{CNN, BiLSTM, DL, TensorFlow, Python, SMOTE method} & \multirow{2}{*}{Classification accuracy: 83.65\% and 78.13\%} \\  & There were 82,337 test connection records and & & & \\  & & & \\  & records. & & \\  & Datasets from IDS, including & \multirow{2}{*}{ML, ANN, and Classification Confusion Matrix} & \multirow{2}{*}{85\% Siamese Network Accuracy} \\  & and NSL–KDD & & & \\  & NSL–KDD, CICIDS 2017, and KDD Cup 1999 & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & \end{tabular}
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Table 1: List of past paper references, including methodology used and results. tures, normalization to bring numerical features to a uniform scale, and target balancing to address class imbalance are all examples. Visualization techniques such as t-SNE and Principal Component Analysis (PCA) are used to obtain insights into the distribution of attacks within the dataset. These visualizations aid in comprehending the patterns and relationships between various kinds of attacks. Three methodologies are used for the classification task: binary classification, four-class classification, and all-class classification. Each technique attempts to categorize network traffic occurrences, separating regular traffic from various sorts of attacks. This enables more granular data analysis and the detection of specific assault types. XGBoost, a gradient boosting technique, is used to discover the most significant characteristics for classification. It evaluates the characteristics according to their importance and selects the top features that significantly contribute to the classification process. During the model building and training phase, two approaches are used: an MLP (Multi-Layer Perceptron) model and a hybrid model that combines a CNN and an LSTM. The MLP model is a standard feed-forward neural network, whereas the hybrid model uses CNN for feature extraction and LSTM for capturing temporal dependencies in data.