Abstract: Intrusion Detection Systems (IDS) are critical for mitigating evolving cybersecurity threats. This study investigates the integration of Machine Learning (ML) and Deep Learning (DL) techniques to enhance IDS efficiency. A dual-panel IDS is developed, incorporating an attack detection module for user uploads and an admin panel for model training and testing. The system leverages multiple classification algorithms, including Support Vector Machine (SVM), Random Forest, XGBoost, AdaBoost, and Decision Tree, to improve intrusion detection accuracy. A dynamic model selection mechanism is implemented to optimize algorithm performance at runtime, complemented by graphical visualizations for comprehensive threat analysis. Various IDS datasets are evaluated to assess detection effectiveness, addressing challenges such as computational complexity and real-time traffic management. Experimental results indicate an accuracy range of 92% to 96%, with Random Forest and Decision Tree performing optimally based on dataset characteristics. This research contributes to the advancement of IDS by improving detection reliability, reducing false positives, and enhancing system scalability, ultimately strengthening cybersecurity defenses.
Keywords: IDS, ML, DL, Network Security, Random Forest, SVM, Cybersecurity, Anomaly Detection, False Positives, Scalability, Accuracy, XGBoost, Decision Tree.
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DOI:
10.17148/IJARCCE.2025.14660
