{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T04:16:38Z","timestamp":1778300198832,"version":"3.51.4"},"reference-count":10,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2003,5]]},"abstract":"<jats:p> This paper describes experiences and results applying Support Vector Machine (SVM) to a Computer Intrusion Detection (CID) dataset. First, issues in supervised classification are discussed, then the incorporation of anomaly detection enhancing the modeling and prediction of cyber-attacks. SVM methods are seen as competitive with benchmark methods and other studies, and are used as a standard for the anomaly detection investigation. The anomaly detection approaches compare one class SVMs with a thresholded Mahalanobis distance to define support regions. Results compare the performance of the methods and investigate joint performance of classification and anomaly detection. The dataset used is the DARPA\/KDD-99 publicly available dataset of features from network packets, classified into nonattack and four-attack categories. <\/jats:p>","DOI":"10.1142\/s0218001403002459","type":"journal-article","created":{"date-parts":[[2003,5,8]],"date-time":"2003-05-08T04:38:28Z","timestamp":1052368708000},"page":"441-458","source":"Crossref","is-referenced-by-count":22,"title":["COMPUTER INTRUSION DETECTION WITH CLASSIFICATION AND ANOMALY DETECTION, USING SVMs"],"prefix":"10.1142","volume":"17","author":[{"given":"MIKE","family":"FUGATE","sequence":"first","affiliation":[{"name":"NIS-7: Nonproliferation and International Security Safeguards Systems, Los Alamos National Laboratory, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JAMES R.","family":"GATTIKER","sequence":"additional","affiliation":[{"name":"NIS-7: Nonproliferation and International Security Safeguards Systems, Los Alamos National Laboratory, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","volume-title":"Classification and Regression Trees","author":"Breiman L.","year":"1984"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2477-6"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3847-6"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1145\/846183.846199"},{"key":"rf10","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4931.001.0001","volume-title":"Neural Network Learning and Expert Systems","author":"Gallant S. I.","year":"1993"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-21606-5"},{"key":"rf13","volume-title":"Advances in Kernel Methods \u2014 Support Vector Learning","author":"Joachims T.","year":"1999"},{"key":"rf16","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3458-4"},{"key":"rf19","volume-title":"The Nature of Statistical Learning Theory","author":"Vapnik V.","year":"1996"}],"container-title":["International Journal of Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218001403002459","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T12:49:21Z","timestamp":1565182161000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218001403002459"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,5]]},"references-count":10,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2003,5]]}},"alternative-id":["10.1142\/S0218001403002459"],"URL":"https:\/\/doi.org\/10.1142\/s0218001403002459","relation":{},"ISSN":["0218-0014","1793-6381"],"issn-type":[{"value":"0218-0014","type":"print"},{"value":"1793-6381","type":"electronic"}],"subject":[],"published":{"date-parts":[[2003,5]]}}}