Deep Learning Network Intrusion Detection With the Conv1d-Lstm Model: Integrating Cnn and Lstm for Superior Performance

Abstract

The escalating frequency and complexity of cyber-attacks present significant threats to corporate networks, resulting in financial losses, reputational harm, and possible data breaches. Traditional Intrusion Detection Systems (IDS), which rely on predefined signatures and rules, have proven inadequate in addressing these advanced threats due to high rates of both false positives and negatives. This inadequacy necessitates the development of more advanced intrusion detection methods. This thesis introduces a novel AI-based intrusion detection model leveraging deep learning techniques to enhance corporate network security. The proposed model utilizes convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze network traffic data from the comprehensive CSE-CIC-IDS-2018 dataset, which encompasses a wide array of attack types and provides a realistic representation of modern network traffic. By capturing complex patterns and temporal dependencies in the data, these deep learning algorithms are particularly effective in detecting sophisticated intrusion attempts A key contribution of this research is the development of a hybrid detection approach that fuses convolutional neural network and recurrent neural network algorithm. This hybrid model enhances detection accuracy and reduces false alarms. The model's performance is rigorously evaluated using metrics such as precision, recall, and F1 score, demonstrating superior detection capabilities with a 99.97% in precision, 99.95% in recall, 99.97% in accuracy, and 99.96% in F1-score outperforming the other models. To address challenges related to data quality, the study incorporates extensive data preprocessing steps, including feature selection, encoding, and scaling. The high computational demands of training deep learning models are mitigated using cloud-based resources. Furthermore, visualization strategies are used to enhance the model's interpretability, offering a glimpse into its decision-making process. The findings of this research have significant implications for network security administrators, researchers, educators, and policymakers. Network security administrators can apply these insights to enhance their defensive strategies against cyber threats. Researchers and educators can leverage the advanced methodologies presented in this study, while policymakers can utilize the findings to inform the development of more effective network security policies and standards. This research advances the field of cybersecurity by proposing and evaluating a novel AI-based intrusion detection model. It underscores the critical importance of integrating advanced AI methodologies into IDS frameworks to protect corporate networks from evolving cyber threats. By improving the accuracy and reliability of intrusion detection systems, this study contributes to the overall security of digital operations in organizations worldwide, highlighting the transformative potential of AI in contemporary cybersecurity.