## 2 Related Works
The CSE-CIC-IDS 2018 and Tensorflow systems were used as the software library and benchmark dataset, respectively. The evaluation relied upon critical performance indicators such as accuracy, detection, and false alarm rates [23], comprehensively assessing the system's capabilities. The outcomes indicate that deep neural networks can detect intrusion in software-defined networks. This approach demonstrated good results on the latest network attack datasets, leveraging the specific characteristics of Software Defined Network (SDN), such as using the network as a sensor through OpenFlow. Utilizing machine learning techniques for traffic analysis in SDN can enhance the efficiency of network resource allocation. Future work will focus on further analysis of intrusion detection methods and developing an Intrusion Detection System (IDS) using machine learning methods [23]. In ref. [24], the authors developed an ID system using Spark. Moreover, the Convolutional Autoencoder (Conv-AE) deep learning approach efficiently learned feature representations from the CICIDS 2018 dataset. The proposed system outperformed traditional security approaches regarding attack detection rate, accuracy, and computation complexity. The research suggests [24] that this approach can be extended to other fields, such as real-time anomaly and network misuse detection, using deep learning as an attribute extraction tool. In order to detect network threats, Ref. [25] suggested the use of three models (LSTM, Apache et al., and Random Forest) that use the random forest approach to reduce dimensionality. The application of oversampling and undersampling strategies stemmed from the dataset's inherent imbalance. Apache Spark had the fastest training time across all classes compared to DL models. Future work could involve semisupervised learning and expand beyond signature-based Intrusion Detection Systems. One study reported that the Long Short-Term Memory (LSTM) algorithm attained a maximum accuracy of 99 percent [26]. Modern cybersecurity requires detecting network breaches, and researchers have used machine learning to detect and stop attacks [27]. In network intrusion detection, more attention has recently been drawn to deep learning. However, present evaluations utilizing outdated datasets must address class imbalance issues, which may produce biased conclusions. In [27], the authors offer insightful information by resolving this imbalance and assessing Deep Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory Networks on a balanced dataset. With the lowest False Alarm Rate (2.615%), \(F1\)-Score (83.799%), and excellent accuracy (84.312%), the Deep Neural Network demonstrated the most outstanding performance [27]. Network Intrusion Detection Systems rely on efficient algorithms and methods for real-time assault detection [28]. One study proposed a preprocessing technique that significantly reduced traffic analysis time and achieved high success rates. Experimental results showed that the ExtraTree algorithm obtained a 99.0% detection rate for binary classification and an 82.96% reduction in processing time per sample. With a 64.43% decrease in processing time per sample, the Random Forest method produced a 98.5% identification rate in the multiclass detection scenario. These results indicate similar categorization rates to previous research, albeit with much shorter test durations [28]. Excellent accuracies have been obtained in the mentioned studies. However, the main problem of these studies is that, to obtain excellent accuracies for detecting intrusion in computer networks, a large number of features are used, or if the number of features is small, excellent accuracies have yet to be obtained. The other issue is that in cases where good accuracies have been obtained, either the model-building times--which is an essential criterion in the case of intrusion detection--have yet to be mentioned or the model-building times are too high.