## 2 Related Work
### Multi-Class Classification
Another study [2] reports a DNN model achieving 97.01% accuracy with five-fold cross-validation and Apache Spark for distributed computing. The work of ref. [47] showcases an ANN model with a notable 99.59% accuracy, employing a holistic dataset approach to enhance deep learning performance. Similarly, an RF model in ref. [48] achieves 97.37% accuracy by addressing dataset imbalance and dimensionality through feature clustering techniques. In ref. [28], an RF model extends its attack detection methodology to the UNSW-NB15 dataset, reaching 98.3% accuracy, while an RNN model in ref. [49] achieves 94% accuracy, utilizing recursive feature elimination to improve classification across various attack categories. Furthermore, [50] introduces a multilayer CNN combined with LSTM networks, achieving 99.5% accuracy, and [51] presents a method utilizing sparse stacked auto encoders, achieving 98.5% accuracy through a three-stage process. Finally, ref. [52] introduces an LSTM model with a commendable accuracy of 96.9%. In ref. [34], the research investigates the effectiveness of several deep learning frameworks for network intrusion detection, comparing notable options such as Keras, TensorFlow, Theano, fast.ai, and PyTorch, along with MLP integration. The study reports an impressive accuracy of 98.31% in identifying and classifying network intrusion traffic and various attack types using the CSE-CIC-IDS2018 dataset. Additionally, the study in ref. [53] underscores the critical importance of cyber security in safeguarding network infrastructures against vulnerabilities and intrusions, highlighting advancements in machine learning and deep learning techniques that facilitate early detection and prevention of attacks through advanced self-learning and feature extraction methods. Leveraging the CSE-CIC-IDS2018 dataset, which encompasses normal network behaviors and diverse attacks, an LSTM model achieved an outstanding detection accuracy of 99%. In ref. [54], the authors evaluate a DNN model that demonstrates approximately 90% accuracy, emphasizing its capability to effectively identify network intrusions. The work presented in ref. [55] introduces a dynamic network anomaly detection system aimed at bolstering network security through deep learning techniques, specifically a deep neural network based on LSTM integrated with an attention mechanism (AM) to enhance performance; this approach addresses class imbalance in the CSE-CIC-IDS2018 dataset using SMOTE and an enhanced loss function, resulting in 96.2% accuracy. The study detailed in ref. [38] proposes an advanced classifier development technique that employs a cascade of boosting-based ANNs to construct a highly effective multi-class classifier, utilizing a one-vs-remaining strategy refined with example filtering, ultimately achieving an impressive accuracy of 99.36%. Furthermore, in ref. [23], the authors focus on developing a DNN for a flexible and effective IDS capable of detecting and classifying novel cyber-attacks. Recognizing the dynamic nature of network behaviors and attack strategies, they emphasize the necessity of evaluating datasets generated through both static and dynamic methods over time; the proposed DNN model achieves robust performance with an accuracy of 93.5%, demonstrating its adaptability for real-time threat detection. Lastly, the authors in ref. [56] execute a multi-class classification experiment for network intrusion detection utilizing the KDD-CUP 99 and NSL-KDD datasets, employing a CNN to achieve a remarkable accuracy of 98.2%, thereby showcasing its efficacy in accurately identifying various network attack types. In ref. [10], the study seeks to address the identified limitation by proposing and systematically evaluating standardized feature sets for NIDS that are derived from the NetFlow network metadata collection protocol and system. We conduct a detailed assessment of two distinct variants of NetFlow-based feature sets, one comprising 12 features and the other encompassing 43 features.