NHANCING NETWORK SECURITY THROUGH INTEGRATED DEEP LEARNING ARCHITECTURES AND ATTENTION MECHANISMS

Abstract

With the widespread integration of the Internet into our daily life, ensuring network security has become crucial for applications like online retail, auctions, and file processing. By examining network process logs, intrusion detection and classification are crucial for spotting threats. The issue of network infiltration is made worse by the increasing volume and complexity of contemporary network traffic data, making traditional intrusion prevention methods insufficient. Therefore, low false alarm rates and effective intrusion detection systems are essential. In order to increase efficacy and efficiency, the model uses Convolution Neural Network-Long Short Time Memory (CNN-LSTM) for feature extraction and classification. The attention mechanism is used to choose the most discriminative features. Metrics including accuracy, precision, recall, and the F1-score are used to assess the model's performance. These metrics reveal how well the model detects intrusions (true positives), prevents harmful traffic from being mistakenly labelled as normal (true negatives), and classifies data overall. About 99.9% accuracy is attained by the model, with a precision of 0.98, recall of 1.0, and F1-score of 0.99. These results reflect its ability to effectively identify both normal traffic and intrusion attempts. The high accuracy underscores the model’s strong performance in distinguishing malicious from benign activities. This work contributed to cybersecurity by presenting an innovative solution to intrusion detection challenges. It highlights the importance of balanced datasets and advanced deep learning architectures to improve detection capabilities. The results highlight how well the model can handle the intricacies of contemporary network security risks.