Integrating Fuzzy Logic and Deep Learning for Effective Network Attack Detection with Fuzzified Deep Convolutional Neural Network

In the current digital landscape, network security has become a critical concern due to the widespread use of computer networks in various domains and applications. The Internet and Intranet have revolutionized people’s lives and work, providing unprecedented convenience. To address the evolving threat landscape, researchers have been actively exploring innovative approaches for intrusion detection. These advancements aim to enhance the ability to detect and mitigate network intrusions, ensuring the integrity and security of computer networks. This chapter presents a novel approach for network attack detection using a Fuzzified Deep Convolutional Neural Network (FDCNN). The proposed method leverages the power of deep learning and fuzzy logic to enhance the accuracy and robustness of network attack detection systems. The FDCNN model integrates fuzzification techniques with a deep convolutional neural network architecture. To evaluate the performance of the proposed FDCNN model, extensive experiments are conducted using benchmark network attack datasets. The model’s performance is assessed based on various evaluation metrics, including accuracy, precision, recall, and F1 score. The results demonstrate that the FDCNN model outperforms traditional machine learning approaches in detecting network attacks and achieving superior accuracy and detection rates.