Supervised Learning-Based Anomaly Detection for Cybersecurity in High-Speed Networks
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V3I1P118Keywords:
Cybersecurity, Anomaly Detection, Network Security, Threat Prevention, Machine Learning, Supervised LearningAbstract
Cybersecurity is of paramount importance due to the increasing dependence on digital communication and the fast growth of high-speed networks. Sophisticated cyberattacks including zero-day exploits, DDoS, ransomware, and encrypted intrusions pose significant threats to network stability, data integrity, and organizational operations. The conventional signature-based intrusion detection technologies are not generally sufficient in identifying these dynamic and sophisticated threats, leading to high false negative rates and response times. In order to overcome this challenge, the proposed study is a supervised learning-based anomaly detection system that is utilized to determine malicious activities in high-speed networks with minimal false alarms. CICIDS2017 was used as a dataset and preprocessing like cleaning and normalizing data and encoding labels and SMOTE to equalize the classes. The next step was to compare XGBoost, an Extreme Gradient Boosting method, against Decision Tree, K-Nearest Neighbours, and Random Forest, the remaining machine learning techniques. Among all the comparison models, the XGBoost model outperformed them all with a maximum F1-score of 99.81, accuracy, precision, and recall. In addition to being robust and extrapolable, the confusion matrix and ROC-AUC evaluations showed that it was effective. Such results suggest that XGBoost can be applied to manage such a complex traffic system and could be useful in real-time and scaled anomaly detection. The proposed solution is an effective, viable and realistic approach to guaranteeing high-speed network environments against more sophisticated cyber threats.
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