Practical Real-Time Intrusion Detection Using Machine Learning Approaches
The growing prevalence of network attacks is a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. In this paper, the authors propose a real-time intrusion detection approach using a supervised machine learning technique. Their approach is simple and efficient, and can be used with many machine learning techniques. They applied different well-known machine learning techniques to evaluate the performance of their IDS approach. Their experimental results show that the Decision Tree technique can outperform the other techniques. Therefore, they further developed a Real-Time Intrusion Detection System (RT-IDS) using the Decision Tree technique to classify on-line network data as normal or attack data.