Affiliation:
1. Sri Krishna Arts and Science College, Coimbatore, India
2. Dr. Mahalingam College of Engineering and Technology, Pollachi, India
Abstract
Intrusion detection system (IDSs) are important to industries and organizations to solve the problems of networks, and various classifiers are used to classify the activity as malicious or normal. Today, the security has become a decisive part of any industrial and organizational information system. This chapter demonstrates an association rule-mining algorithm for detecting various network intrusions. The KDD dataset is used for experimentation. There are three input features classified as basic features, content features, and traffic features. There are several attacks are present in the dataset which are classified into Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). The proposed method gives significant improvement in the detection rates compared with other methods. Association rule mining algorithm is proposed to evaluate the KDD dataset and dynamic data to improve the efficiency, reduce the false positive rate (FPR) and provides less time for processing.
Reference41 articles.
1. Optimized intrusion detection mechanism using soft computing techniques.;I.Ahmad;Telecommunication Systems,2013
2. A feature reduced intrusion detection system using ANN classifier
3. Identifying False Alarm for Network Intrusion Detection System Using Hybrid Data Mining and Decision Tree
4. Feature deduction and ensemble design of intrusion detection systems
5. An anomaly detection and analysis method for network traffic based on correlation coefficient matrix.;N.Chen;Proceedings of the 2009 International Conference on Scalable Computing and Communications; Eighth International Conference on Embedded Computing,2009