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Design and Implementation of an Improved Denial of Service (DoS) Detection System using Association Rule
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 3 - Number 7 |
| Year of Publication: 2015 |
| Authors: Olasehinde Olayemi, Olayemi Olufunke, Aliyu E. Olubunmi |
10.5120/cae2015651993
|
Olasehinde Olayemi, Olayemi Olufunke, Aliyu E. Olubunmi . Design and Implementation of an Improved Denial of Service (DoS) Detection System using Association Rule. Communications on Applied Electronics. 3, 7 ( December 2015), 24-29. DOI=10.5120/cae2015651993
Abstract
The need for effective and efficient Denial of Service (DoS) Detection System cannot be overemphasized. This position is as a result of a serious threat to the availability of internet services that limit and block legitimate users access by exhausting victim server’s resources or saturating stub networks access links to the internet services instead of subverting services. Hence the need for a supervised data learning techniques known as association rule mining which has the advantage of generating explainable rules was used to build a classifier for detecting some denial of service attacks, carry out a case study on International Knowledge Discovery and data Mining (KDD ’99) tools, intrusion detection dataset for benchmarking the design of the intrusion detection systems. The average classification rate for unpruned rules is 63.16% while that of the pruned rules is 96.6%. The result revealed that pruned rule sets have better classification performance than the unpruned rule set.
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