Name
Abstract | ||
|---|---|---|
The objective of this paper is to construct a lightweight Intrusion Detection System (IDS) aimed at detecting anomalies in networks. The crucial part of building lightweight IDS depends on preprocessing of network data, identifying important features and in the design of efficient learning algorithm that classify normal and anomalous patterns. Therefore in this work, the design of IDS is investigated from these three perspectives. The goals of this paper are (i) removing redundant instances that causes the learning algorithm to be unbiased (ii) identifying suitable subset of features by employing a wrapper based feature selection algorithm (iii) realizing proposed IDS with neurotree to achieve better detection accuracy. The lightweight IDS has been developed by using a wrapper based feature selection algorithm that maximizes the specificity and sensitivity of the IDS as well as by employing a neural ensemble decision tree iterative procedure to evolve optimal features. An extensive experimental evaluation of the proposed approach with a family of six decision tree classifiers namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern has been introduced. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.eswa.2011.06.013 | Expert Syst. Appl. |
Keywords | Field | DocType |
lightweight intrusion detection system,representative tree model,feature selection algorithm,wrapper approach,light weight intrusion detection,anomalous pattern,lightweight ids,decision tree,anomalous network pattern,random tree,random forest,efficient learning algorithm,genetic algorithm,intrusion detection system,neural network | Random tree,Decision tree,Data mining,Computer science,Decision tree model,Artificial intelligence,Random forest,ID3 algorithm,Intrusion detection system,Machine learning,Decision stump,Incremental decision tree | Journal |
Volume | Issue | ISSN |
39 | 1 | 0957-4174 |
Citations | PageRank | References |
55 | 1.78 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Siva S. Sivatha Sindhu | 1 | 82 | 6.12 |
| S. Geetha | 2 | 118 | 14.73 |
| A. Kannan | 3 | 195 | 25.98 |