## 2 Related Work
### Binary Classification
[2], a deep neural network (DNN) model achieves an outstanding 99.99% accuracy in binary classification, underscoring the synergy between deep learning techniques and big data in enhancing IDS effectiveness. The research utilizes three classifiers, random forest, gradient boosting tree (GBT), and a deep feed-forward neural network, to classify network traffic, while employing a homogeneity measure to extract the most relevant features from the datasets. In ref. [23], a DNN model is proposed, achieving 93.1% accuracy for binary classification, with a focus on developing a robust and adaptable IDS capable of detecting both known and emerging cyber threats. Recognizing the ever-evolving nature of network environments and the rapid emergence of new attack vectors, the study evaluates various datasets using both static and dynamic analysis techniques to identify optimal methods for detecting novel threats. It compares the performance of DNN models with traditional machine learning classifiers on a variety of publicly available malware datasets. The authors in ref. [24] present a DNN-based IDS model that achieves a 99% accuracy rate, applied to a newly constructed dataset containing both packet-based and flow-based data, as well as associated metadata. Despite the dataset's imbalance and the inclusion of 79 attributes, some representing classes with minimal training samples, the research highlights the capacity of deep learning to mitigate the issues inherent in imbalanced datasets. Meanwhile, ref. [25] introduces a stacked auto encoder (SAE) model, achieving a remarkable 99.92% accuracy. The study outlines an innovative IDS framework comprising five core components: data preprocessing, auto encoder compression, database storage, classification, and feedback. By compressing the preprocessed data to extract lower-dimensional features, the auto encoder enables more efficient classification while storing the compressed data in a database for future forensic analysis, post-attack evaluations, and model retraining. In ref. [26], a long short-term memory (LSTM) model is proposed, achieving 92.2% accuracy in binary classification by incorporating attention mechanisms to enhance the capture of both temporal and spatial features in network traffic data. This model is tested on the UNSW-NB15 dataset,which offers diverse patterns and significant disparities between training and testing sets, making it an ideal challenge for evaluating model performance. In ref. [27], the authors propose a hybrid CNN-BiLSTM model, which achieves 97.90% accuracy in binary classification. This model combines bidirectional LSTMs with a lightweight CNN architecture and utilizes feature selection methods to reduce complexity while maintaining robust detection performance. Similarly, in ref. [28], a random forest model is presented, achieving 98.6% accuracy in detecting network attacks on the UNSW-NB15 dataset. This comprehensive study employs advanced machine learning and deep learning techniques to create a highly effective attack detection strategy. Finally, in ref. [2], another DNN model reaches 99.16% accuracy in classifying network traffic, utilizing five-fold cross-validation and incorporating ensemble learning methods. The model leverages the Apache Spark MLlib alongside the Keras deep learning framework, illustrating the powerful capabilities of deep learning and big data technologies in addressing complex network security challenges. In ref. [29], a LSTM-based recurrent neural network (RNN) was trained as a category classifier, utilizing a dataset with 122 features. This model achieved a test accuracy of 82.68%, demonstrating its ability to manage complex classification tasks. The authors in ref. [30] addressed the issue of class imbalance by combining a CNN with a Bidirectional LSTM (BiLSTM) and integrating ADASYN, resulting in a notable accuracy of 90.73% on the test set. In ref. [31], performance enhancement was achieved by optimizing an auto encoder network for anomaly detection, yielding a test accuracy of 90.61%. Meanwhile, ref.