ADVANCED CYBERSECURITY: DETECTION OF ANOMALIES AND CYBER ATTACKS USING HYBRID MACHINE LEARNING MODEL

Abstract

Automated systems can now identify different forms of anomalies in network traffic patterns and threats simultaneously because of the sophisticated techniques employed in modern cyber security systems. This research work devised an intelligent detection method using Long Short-Term Memory (LSTM) and the efficacious machine learning extreme gradient boosting (XGBoost) algorithm to enhance cyber threat detection accuracy. Using the synthetic minority over-sampling technique (SMOTE), the model enhances its performance by creating additional synthetic minority data points, thus, balancing the dataset and reducing bias. The model learns to capture highly complex non-linear relationships in the data which improves overall performance across different attack scenarios. The model design was tested with real network traffic and was found to have an impressive 98% accuracy. The obtained accuracy of this solution demonstrates its value in real world applications of cybersecurity since it enables the rapid identification of zero day and advanced persistent threats among many other cyber-attacks without loss of precision in the process. Likewise, our proposed approach also addresses the data imbalance issues and improves the model’s ability to accurately and sensitively detect anomalies.