Improving Intrusion Detection Systems by Using Deep Learning Methods on Time Series Data
Asma Ahmed A. Mohammed
Abstract
Intrusion Detection Systems (IDSs) are the cornerstone of cybersecurity, monitoring network traffic to find abnormal suspicious activities. Traditional IDSs usually face challenges in adapting to the cyber threats that evolve day by day, leading to very high false positive rates and missed detections. This study focuses on enhancing the performance of an IDS system by integrating deep learning techniques with time series data. The efficiency of RNN, CNN, and LSTM networks was evaluated in detecting intrusions in real-time. The experimental results showed that hybrid models, especially the CNN+RNN+LSTM combination, performed best with a 0.86 F1 score, 0.92 precision, and 0.79 recall, indicating that hybrid deep learning methods can improve detection accuracy while reducing false alarms, opening a resilient future for cybersecurity.