INTELLIGENT INTRUSION DETECTION AND DATA PROTECTION IN INFORMATION SECURITY USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING TECHNIQUES - Abdul Karim Sajid Ali; Aamir Raza; Haroon Arif; Ali Abbas Hussain (Spectrum of Engineering Sciences, 2025)
Cyber threats are evolving rapidly in terms of complexity and prevalence that making traditional intrusion detection systems severely inadequate for effective information security. An advanced Intrusion Detection System (IDS) framework that utilizes Artificial Intelligence and Machine Learning techniques, specifically Random Forest and Support Vector Machine significantly enhances threat detection accuracy within digital environments. This proposed system effectively identifies various types of intrusions, including Denial of Service attacks, brute-force login attempts and previously unknown zero-day exploits by analyzing network traffic patterns. To support this, a complex synthetic dataset that replicates diverse concealed attack patterns alongside seemingly legitimate network activities was created. Essential preprocessing techniques, such as feature normalization were extensively applied, while dimensionality reduction was cautiously employed greatly improving model learning efficiency. data was split into 70 % for training and 30% for testing strategy implemented for training and validating system parameters. Comprehensive performance evaluations were conducted using standard metrics, including Accuracy, Precision and Receiver Operating Characteristic (ROC) Curve values provides a thorough analysis of the model’s detection capabilities. The Random Forest model achieved outstanding results with an Accuracy of 88.67% and Precision of 87.32%, while Recall was recorded at 88.57%. The F1-Score was approximately 87.94%, and the Area Under Curve (AUC) was impressively high at 96.47%. The Support Vector Machine (SVM) model also performed well, reaching an Accuracy of 86.33% and an AUC of 92.24%, demonstrates its effectiveness even in resource-limited environments. ROC curves further validate the system's ability to distinguish between legitimate and malicious activities effectively. Proactive cybersecurity strategies are strongly supported by the integration of advanced machine learning models into IDS, which operate in real-time under rigorous conditions. The framework's adaptability and high accuracy provide scalable enterprise-level network security solutions paving the way for future developments driven by deep learning.