Intrusion Detection Systems using Adaptive Regression Splines Export

@book{citeulike:407678, abstract = {Past few years have witnessed a growing recognition of soft computing technologies for the construction of intelligent and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given.}, author = {Mukkamala, Srinivas and Sung, Andrew H. and Abraham, Ajith and Ramos, Vitorino}, citeulike-article-id = {407678}, citeulike-linkout-0 = {http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_52.html}, keywords = {adaptive\_algorithms, adatptive\_computation, data-mining, detection, intrusion-detection-systems, pattern\_recognition, regression, splines}, posted-at = {2005-11-24 18:42:15}, priority = {0}, title = {Intrusion Detection Systems using Adaptive Regression Splines}, url = {http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_52.html} }

TY - BOOK ID - citeulike:407678 L3 - citeulike-article-id:407678 N2 - Past few years have witnessed a growing recognition of soft computing technologies for the construction of intelligent and reliable intrusion detection systems. Due to increasing incidents of cyber attacks, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet it remains an elusive goal and a great challenge. In this paper, we report a performance analysis between Multivariate Adaptive Regression Splines (MARS), neural networks and support vector machines. The MARS procedure builds flexible regression models by fitting separate splines to distinct intervals of the predictor variables. A brief comparison of different neural network learning algorithms is also given. TI - Intrusion Detection Systems using Adaptive Regression Splines KW - adaptive_algorithms KW - adatptive_computation KW - data-mining KW - detection KW - intrusion-detection-systems KW - pattern_recognition KW - regression KW - splines AU - Mukkamala, Srinivas AU - Sung, Andrew AU - Abraham, Ajith AU - Ramos, Vitorino UR - http://alfa.ist.utl.pt/~cvrm/staff/vramos/ref_52.html ER -