## 2 Related Work
### Binary Classification
[32] introduced a multi-CNN model with discrete data preprocessing steps, which effectively classified attacks on the test set, achieving an accuracy of 83%. In ref. [33], advancements in IDS for cloud environments were explored by developing and evaluating two cutting-edge deep neural network models. The first model was a multi-layer perceptron (MLP) trained using backpropagation (BP), while the second incorporated particle swarm optimization (PSO) into the MLP training process. Both models demonstrated a significant improvement in IDS performance and efficiency, achieving an impressive accuracy of 98.97%. This underscores their effectiveness in both intrusion detection and prevention. In ref. [34], the efficacy of deep learning algorithms for network intrusion detection was evaluated by comparing frameworks such as Keras, TensorFlow, Theano, fast.ai, and PyTorch. The researchers employed an MLP model and achieved a test accuracy of 98.68% in identifying network intrusion traffic and classifying various attack types, utilizing the CSE-CIC-IDS2018 dataset for validation. Similarly, [35] presented an innovative IDS model that employed a custom-designed recurrent convolutional neural network (RCNN) optimized through the ant lion optimization algorithm, achieving a test accuracy of 94%. This approach significantly improved IDS performance, particularly in detecting and mitigating network intrusions. In ref. [36], a deep learning framework was proposed to enhance IDS by utilizing a denoising auto encoder (DAE) as the central component of the methodology. The DAE was trained using a layer-wise greedy approach to prevent overfitting and avoid local optima, achieving a robust test accuracy of 96.53%. This strategy ensured higher reliability in detecting network intrusions. In ref. [37], a hidden naive Bayes (HNB) classifier was introduced, specifically tailored to counter denial of service (DoS) attacks. By relaxing the traditional naive Bayes assumption of conditional independence, the model incorporated discretization and feature selection techniques, achieving a test accuracy of 97%. This approach not only enhanced performance but also minimized processing time by prioritizing the most relevant features. Lastly, in ref. [38], the authors developed a novel classifier by employing a cascade of boosting-based artificial neural networks (ANNs) on two prominent intrusion detection datasets. Their method, which achieved a test accuracy of 98.25%, significantly improved upon the traditional one-vs-remaining strategy by introducing an additional example filtering step, ultimately boosting the model's overall effectiveness. In ref. [39], the authors present the design of an IDS tailored for IIoT networks, employing the RF model for classification. The methodology incorporates PCC to identify and select critical features, alongside IF to detect outliers. Both PCC and IF are applied both independently and in an interchangeable sequence, allowing PCC to process features that are then refined by IF, and alternatively, IF processes initial data for PCC to further optimize feature selection. This iterative application enhances the robustness and precision of the IDS model. In ref. [10], the paper addresses the identified limitation by proposing and assessing standardized feature sets for network intrusion detection systems (NIDS) that are derived from the NetFlow network metadata collection protocol and system. We conduct a thorough evaluation and comparison of two distinct variants of NetFlow-based feature sets, one comprising 12 features and the other encompassing 43 features. For our analysis, we transformed four widely utilized NIDS datasets into new versions that incorporate our proposed NetFlow-based feature sets. Utilizing an Extra Tree classifier, we systematically compared the classification performance of these NetFlow-based feature sets against the proprietary feature sets originally provided with the datasets.