Cybersecurity has become one of the most critical concerns in modern information systems due to the continuousgrowthof sophisticated cyberattacks targeting computer networks, cloud infrastructures, enterprise systems, and Internet-basedservices.Traditional signature-based Intrusion Detection Systems (IDSs) are often ineffective in detecting unknown attacks, zero-daythreats, and rapidly evolving malicious activities. Machine learning techniques have emerged as intelligent computationalapproaches capable of automatically identifying abnormal network behavior by learning patterns fromhistorical networktrafficdata. This experimental study proposes an Intelligent Intrusion Detection System (IIDS) using machine learning techniquesforcybersecurity applications. The proposed framework integrates network traffic preprocessing, feature selection, supervisedmachine learning classification, intrusion detection, attack categorization, and performance evaluation into a unifiedanalyticalarchitecture. A mathematical framework and algorithmic strategy are developed to evaluate detection accuracy, precision, recall,false alarm rate, computational efficiency, and overall cybersecurity performance. Experimental evaluation demonstratesthatmachine learning algorithms significantly improve intrusion detection capability by accurately distinguishingnormal andmalicious network activities while reducing false-positive rates and computational overhead. The proposed frameworkprovidesvaluable guidance for researchers, cybersecurity professionals, network administrators, and security analysts seekingtodevelopintelligent, scalable, and computationally efficient intrusion detection systems. Keywords: Intrusion Detection System, Machine Learning, Cybersecurity, Network Security, Anomaly Detection, NetworkTrafficAnalysis.