Using Neuro-Fuzzy Approach to Reduce False Positive Alerts

One of the major problems of Intrusion Detection Systems (IDS) at the present is the high rate of false alerts that the systems produce. These alerts cause problems to human analysts to repeatedly and intensively analyze the false alerts to initiate appropriate actions. We demonstrate the advantages of using a hybrid neuro-fuzzy approach to reduce the number of false alarms. The neuro-fuzzy approach was experimented with different background knowledge sets in DARPA 1999 network traffic dataset. The approach was evaluated and compared with RIPPER algorithm. The results shows that the neuro- fuzzy approach significantly reduces the number of false alarms more than the RIPPER algorithm and requires less background knowledge sets.