Comparative Study of Data Mining and Machine Learning Approach for Anomaly Detection

The intrusion detection systems (IDSs) have attracted more researchers from last two decades. The much more work has been done in IDS. But still, there are some problems remain unsolved like false positive rate and detection accuracy. The various approaches are used in developing IDS; some of these are data mining, machine learning, statistic-based, and rule-based approaches. In this paper, we compare the data mining and machine learning approach for detection of anomaly. We have also discussed the challenges in the intrusion detection system. In studied approaches, some papers used both data mining and machine learning approach for developing system, called as hybrid approach.