%0 Journal Article %1 noauthororeditor %A Anurag Jain, BhupendraVerma %A Rana, J. L. %D 2016 %J Informatics Engineering, an International Journal (IEIJ) %K (IDS), (SVM). (TP), False Intrusion Machine Positive(FP), Support True Vector detection positive system %N 01 %P 01-11 %R DOI : 10.5121/ieij.2016.4101 %T CLASSIFIER SELECTION MODELS FOR INTRUSION DETECTION SYSTEM (IDS) %U http://aircconline.com/ieij/V4N1/4116ieij01.pdf %V 04 %X Any abnormal activity can be assumed to be anomalies intrusion. In the literature several techniques and algorithms have been discussed for anomaly detection. In the most of cases true positive and false positive parameters have been used to compare their performance. However, depending upon the application a wrong true positive or wrong false positive may have severe detrimental effects. This necessitates inclusion of cost sensitive parameters in the performance. Moreover the most common testing dataset KDD-CUP-99 has huge size of data which intern require certain amount of pre-processing. Our work in this paper starts with enumerating the necessity of cost sensitive analysis with some real life examples

@article{noauthororeditor, abstract = {Any abnormal activity can be assumed to be anomalies intrusion. In the literature several techniques and algorithms have been discussed for anomaly detection. In the most of cases true positive and false positive parameters have been used to compare their performance. However, depending upon the application a wrong true positive or wrong false positive may have severe detrimental effects. This necessitates inclusion of cost sensitive parameters in the performance. Moreover the most common testing dataset KDD-CUP-99 has huge size of data which intern require certain amount of pre-processing. Our work in this paper starts with enumerating the necessity of cost sensitive analysis with some real life examples}, added-at = {2018-08-27T07:54:19.000+0200}, author = {Anurag Jain, BhupendraVerma and Rana, J. L.}, biburl = {https://www.bibsonomy.org/bibtex/2a4c01feba799c8915ae9ef2dc735a9af/ieij1}, doi = {DOI : 10.5121/ieij.2016.4101}, interhash = {204bebf5ff4abd33e36dd894d6e5d17f}, intrahash = {a4c01feba799c8915ae9ef2dc735a9af}, issn = {ISSN : 2349 - 2198}, journal = {Informatics Engineering, an International Journal (IEIJ)}, keywords = {(IDS), (SVM). (TP), False Intrusion Machine Positive(FP), Support True Vector detection positive system}, language = {english}, month = mar, number = 01, pages = {01-11}, timestamp = {2018-08-27T07:54:19.000+0200}, title = {CLASSIFIER SELECTION MODELS FOR INTRUSION DETECTION SYSTEM (IDS)}, url = {http://aircconline.com/ieij/V4N1/4116ieij01.pdf}, volume = 04, year = 2016 }