## 1 Introduction
Deep learning models, such as artificial neural networks (ANNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), leverage multiple layers of artificial neurons to autonomously learn hierarchical representations from large-scale, high-dimensional datasets [19, 20, 21]. These models have demonstrated remarkable success across various domains, including image recognition, natural language processing, and speech recognition, due to their ability to capture complex patterns and feature dependencies. In the context of intrusion detection, deep learning models eliminate the need for manual feature engineering by learning representations directly from raw network traffic data [22]. This enables them to effectively identify both known and novel attack patterns with greater accuracy and adaptability. Furthermore, deep learning models can analyze temporal dependencies in sequential data, making them particularly well suited for detecting evolving attack patterns in real-time network monitoring. As a result, deep learning holds significant promise in advancing IDS technologies and enhancing cybersecurity resilience in modern network environments [23]. This research paper aims to assess the effectiveness of deep learning algorithms in detecting network intrusions. Specifically, the study focuses on training artificial neural networks, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), using large volumes of network traffic data to distinguish between normal and abnormal behavior patterns. CNNs are particularly effective in analyzing spatial patterns in data, while RNNs excel at capturing temporal dependencies in sequential data [24]. By utilizing the unique strengths of these deep learning models, this research aims to develop more accurate and robust intrusion detection systems. The performance of deep learning methods will be compared to traditional machine learning techniques [25], such as support vector machines and decision trees, to evaluate their relative effectiveness in detecting network intrusions [26]. This comparison will involve analyzing various performance metrics, including detection accuracy, false positive rate, and false negative rate, to determine the overall efficiency of each approach. A key novelty of this study lies in its comparative analysis of multiple machine learning and deep learning models, offering a comprehensive understanding of their strengths and weaknesses in intrusion detection. Furthermore, this research employs the synthetic minority over-sampling technique (SMOTE) for data balancing, addressing the class imbalance issue that often hinders the performance of machine learning models. By augmenting the dataset, SMOTE enhances the models' ability to detect minority class intrusions, improving overall detection accuracy. Another key contribution of this study is its evaluation of computational efficiency and scalability, which are crucial for deploying deep learning-based intrusion detection systems in real-world environments. The successful implementation of deep learning algorithms [27] in intrusion detection systems [28] could lead to more advanced and accurate security solutions, enhancing the overall cybersecurity landscape. By better protecting computer networks from potential threats, this research has the potential to contribute to the development of safer and more secure digital environments for individuals, businesses, and governments alike. In addition, the insights gained from this research could inform the development of novel deep learning-based methods for other cybersecurity applications, such as malware detection [29], vulnerability assessment [30], and spam filtering [31]. Moreover, this research project will discuss the scalability and efficiency of deep learning-based intrusion detection systems [32] in real-world scenarios.