Experimental Analysis of Deep Learning-Based Intrusion Detection for Intelligent Network Security

Abstract

The rapid growth of Internet-based applications, cloud computing, wireless communication, and distributed network infrastructures has significantly increased the complexity and frequency of cyberattacks. Traditional Intrusion Detection Systems (IDS) based on signature matching and rule-based techniques are often ineffective against zero-day attacks, polymorphic malware, and sophisticated network intrusions. Consequently, intelligent intrusion detection methods based on artificial intelligence and deep learning have emerged as promising solutions for improving network security. Deep learning models possess superior feature learning capabilities that enable automatic extraction of complex traffic patterns without extensive manual feature engineering, thereby improving intrusion detection accuracy and reducing false alarm rates. This study presents an experimental analysis of deep learning-based intrusion detection techniques for intelligent network security. The proposed framework integrates data preprocessing, feature selection, deep neural network architectures, and intelligent classification mechanisms to detect network intrusions efficiently. The study investigates the performance of several deep learning architectures, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), and Autoencoders (AE), for identifying both known and unknown network attacks. The experimental framework evaluates detection accuracy, precision, recall, F1-score, false positive rate, computational efficiency, and scalability using benchmark intrusion detection datasets commonly employed in cybersecurity research.