## 2 Related Work
### Binary Classification  
Using the Transformer-DNN and Autoencoder-CNN technique for IDS binary classification combines the strengths of advanced contextual feature extraction and spatial pattern recognition. The transformer component excels at capturing contextual relationships in network traffic data, enhancing the ability to discern intricate data dependencies essential for effective classification. The DNN leverages these extracted features to perform accurate final classifications, ensuring robust detection of attack types. Meanwhile, the Autoencoder compresses and reshapes network traffic data, addressing class imbalance and enhancing the representation of data characteristics. These optimized data are then passed to the CNN, which specializes in identifying intricate spatial patterns crucial for distinguishing malicious from legitimate activity. By integrating these methods, this hybrid approach significantly enhances the effectiveness of cyber security defenses against dynamic and evolving threats. The result is an IDS with improved accuracy, reduced false positives and negatives, and superior real-time threat detection capabilities. In ref. [15], the authors proposed a DNN model that achieved a binary classification accuracy of 93.1%. This study explores the development of a versatile and efficient IDS capable of detecting and categorizing unexpected and evolving cyber-attacks. The dynamic nature of networks and the rapid evolution of attacks necessitate the evaluation of multiple datasets over time, using both static and dynamic techniques. This approach aided in identifying the most effective methods for detecting emerging threats, and provided a thorough evaluation of DNN models alongside other traditional machine learning classifiers using several publicly available benchmark malware datasets. The authors in ref. [16] introduced a DNN-based intrusion detection model with a reported accuracy of 99%. The model was applied to a recently available dataset that included packet-based and flow-based data along with additional metadata. The dataset was labeled and imbalanced; it included 79 attributes, with some classes having significantly fewer training samples. The study highlights the challenges of working with imbalanced data and the importance of using deep learning models to address these issues. In ref. [17], the authors recommend the use of principal component analysis (PCA) along with classifiers such as random forest (RF), linear discriminant analysis (LDA), and quadratic discriminant analysis (QDA), to achieve 99.6% accuracy. PCA can be employed as a feature dimensionality reduction method, and the reduced features used to construct various classifiers for IDS development. In ref. [18], the authors introduced a long short-term memory (LSTM) model that achieved 92.2% accuracy in binary classification. This approach incorporates attention mechanisms with LSTM networks to effectively capture both temporal and spatial patterns in network traffic data. The model was tested on the UNSW-NB15 dataset, containing diverse patterns and notable differences between training and testing data, creating a challenging evaluation setting. In ref. [19], a CNN-bi-directional long short-term memory (BiLSTM) model was proposed, attaining 97.90% accuracy in binary classification. This hybrid approach integrated bidirectional LSTM with a lightweight CNN, applying feature selection techniques to optimize model efficiency. The authors of ref. [20] proposed a hybrid model combining LSTM, CNN, and SVM to achieve 98.47% accuracy. Their hybrid semantic deep learning (HSDL) architecture incorporated word2 vec embedding to capture semantic information in network traffic, and AES encryption to enhance cloud storage security. The crossover-based mine blast optimization algorithm (CMBA) was used to select the optimal AES key. Additionally, ref.