The rapid growth of Internet-based applications, cloud computing, wireless communication, and distributednetworkinfrastructures has significantly increased the complexity and frequency of cyberattacks. Traditional Intrusion DetectionSystems(IDS)based on signature matching and rule-based techniques are often ineffective against zero-day attacks, polymorphicmalware,andsophisticated network intrusions. Consequently, intelligent intrusion detection methods based on artificial intelligence anddeeplearninghave emerged as promising solutions for improving network security. Deep learning models possess superior feature learningcapabilitiesthat enable automatic extraction of complex traffic patterns without extensive manual feature engineering, thereby improvingintrusiondetection accuracy and reducing false alarm rates. This study presents an experimental analysis of deep learning-based intrusiondetectiontechniques for intelligent network security. The proposed framework integrates data preprocessing, feature selection, deepneural networkarchitectures, and intelligent classification mechanisms to detect network intrusions efficiently. The study investigates the performanceofseveral deep learning architectures, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), RecurrentNeuralNetworks (RNN), Deep Belief Networks (DBN), and Autoencoders (AE), for identifying both known and unknown networkattacks.Theexperimental framework evaluates detection accuracy, precision, recall, F1-score, false positive rate, computational efficiency,andscalability using benchmark intrusion detection datasets commonly employed in cybersecurity researchKeywords: Intrusion Detection System, Deep Learning, Intelligent Network Security, Cybersecurity, Deep Neural Network.