Improving Network Intrusion Detection with Convolutional Neural Networks and Data Balancing Techniques

In order to maintain network security and detect intrusion attacks, Intrusion Detection Systems (IDS) have become an important technology. Due to the growing usage of the internet, intrusion attacks have become more frequent and can lead to the theft, alteration, or deletion of user data. IDS systems provide an active approach to network security by differentiating between normal and intrusion traffic, which needs to be blocked to protect the network. IDS systems have a wide range of applications in academic and business communities. Researchers have employed deep learning methods for network traffic classification, and conventional neural networks are popular due to their accuracy and versatility in handling various types of data. IoT systems, which are made up of connected devices, are also vulnerable to intrusion attacks, emphasizing the importance of IDS systems. To detect intrusion patterns, researchers have used the NSL-KDD dataset and employed a CNN neural network, which is an imbalanced dataset, meaning that the distribution of classes in the dataset is uneven. The proposed approach includes preprocessing techniques using oversampling by SMOT technique to improve the performance of the CNN-based IDS. The CNN network showed an excellent accuracy rate of 99.87%, and the accuracy rate further increased to 90.12% by incorporating the DLNID model to detect intrusion traffic. This demonstrates the potential of IDS systems and deep learning techniques for maintaining network security and protecting against intrusion attacks.