## 2 Related Works
Applying synthetic minority oversampling to categorize minority classes of assaults more accurately in the future and running more tests on new data from various network environments using the CIC-IDS-2017 dataset are two potential enhancements [17]. In a survey, [PERSON], [PERSON], [PERSON], [PERSON] et al. [18] investigated deep learning approaches in anomaly-based network intrusion detection. The authors presented a comprehensive outline of anomaly detection techniques, encompassing data reduction, dimension reduction, classification, and deep learning approaches. The survey [18] covered prior research on deep learning techniques for network anomaly detection, emphasizing its advantages and possible drawbacks. Additionally, the authors conducted local experiments and discovered encouraging outcomes when analyzing network traffic using a Fully Convolutional Network (FCN) model. Regarding anomaly detection accuracy, the FCN model outperformed traditional machine learning methods like Support Vector Machine (SVM), Random Forest, and Adaboosting. Intrusion Detection Systems, or IDSs, are crucial for computer security because they identify and halt malicious activity in computer networks. An enhanced IDS that incorporates two-level classifier ensembles and hybrid feature selection is presented in [19]. The hybrid feature selection method uses the Ant Colony Algorithm, Genetic Algorithm, and Particle Swarm Optimization to minimize the feature size of training datasets (UNSW-NB15 and NSL-KDD). They selected features based on how well a Reduced Error Pruning Tree (REPT) classifier performs in classification. REPT is a two-level classifier ensemble that uses bagging meta-learners and rotation forests. The suggested classifier outperformed recently developed classification algorithms, displaying 85.8% accuracy, 86.8% sensitivity, and an 88.0% detection rate on the NSL-KDD dataset. The UNSW-NB15 dataset showed similar improvements. We further validated the experimental results using a two-step statistical significance test, which improved the usefulness of the suggested classifier in the ongoing IDS study. The intelligent Intrusion Detection System described in [20] dramatically enhanced detection performance using an Artificial Neural Network (ANN) model. The ANN-based classifier showed 100% sensitivity on the test dataset with high precision in minimizing false positives. The authors [20] integrated an offline method with a current dataset to find patterns in web application attacks. Aims for future studies include applying this method to the most recent dataset from the Canadian Institute for Cybersecurity (CI-RA-CIC-DoDHBrw-2020), implementing further real-time optimizations, and integrating it into an online Intrusion Detection System for testing with real-time network data. The performance of machine learning techniques, such as Board Learning System (BLS), Radial Basis Function Network (RBF-BLS), and BLS with cascades of mapped features and enhancement nodes, was assessed. The scrutiny encompassed an in-depth examination of malicious intrusions and anomalies within communication networks. The studies used subsets of the CICIDS 2017 and CSECIC-IDS 2018 datasets containing DoS attacks. Even with fewer pertinent features, the models' performances were comparable, as shown in [21]. Most models attained accuracies and F-Scores above 90%, even though more significant numbers of mapped features and enhancement nodes increased memory needs and training times. Compared to non-incremental BLS, incremental BLS had a noticeably reduced training time [21]. In [22], a thorough examination of deep learning techniques for intrusion detection was performed. Seven deep learning models--deep neural networks, convolutional neural networks, restricted Boltzmann machines, recurrent neural networks, deep belief networks, deep Boltzmann machines, and deep Auto Encoders--were evaluated for their binary and multiclass classification performances.