Intrusion Detection System Using New Ensemble Boosting Approach

Security is a big issue for all networks in today's enterprise environment. Hackers and intruders have made many successful attempts to bring down high profile company networks and web services. Intrusion Detection System (IDS) is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. The main reason for using data mining classification methods for Intrusion Detection System is due to the enormous volume of existing and newly appearing network data that require processing. Data mining is the best option for dandling such type of data. This paper presents the new idea of applying data mining classification techniques to intrusion detection systems to maximize the effectiveness in identifying attacks, thereby helping the users to construct more secure information systems. This paper uses ensemble boosting approach with adaptive sliding window for intrusion detection. The ensemble method is advantageous over single classifier.