Name
Title | ||
|---|---|---|
Evolving optimised decision rules for intrusion detection using particle swarm paradigm |
Abstract | ||
|---|---|---|
The aim of this article is to construct a practical intrusion detection system IDS that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based 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 is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining KDD data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1080/00207721.2011.577244 | Int. J. Systems Science |
Keywords | Field | DocType |
compose training set,representative tree model,optimised decision rule,network traffic pattern,effective network traffic feature,detection accuracy,optimised decision tree,particle swarm paradigm,anomalous network pattern,random tree,decision tree classifier,intrusion detection,decision tree,decision rule,particle swarm,intrusion detection system,machine learning,rule based,classification,random forest | Decision rule,Data mining,Decision tree,Computer science,Decision tree model,Artificial intelligence,Random forest,Intrusion detection system,Machine learning,Decision tree learning,Incremental decision tree,Decision stump | Journal |
Volume | Issue | ISSN |
43 | 12 | 0020-7721 |
Citations | PageRank | References |
4 | 0.48 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| SivaS. Sivatha Sindhu | 1 | 4 | 0.48 |
| S. Geetha | 2 | 118 | 14.73 |
| A. Kannan | 3 | 195 | 25.98 |