{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T09:18:12Z","timestamp":1648977492368},"reference-count":5,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2008,6]]},"abstract":"<jats:p> A typical data mining approach to network intrusion detection mandates a training dataset of network events labeled as either normal or a particular attack category. Such a training dataset is usually very large since there are many events to track. This is particularly the case in a WLAN where the number of devices communicating with the WLAN can be large and with adhoc connectivity. The large size of the unlabeled training dataset creates a problem for the domain expert who is asked to label the records toward creating a training dataset. We present an effective approach by which the number of network records the expert has to examine is a relatively small proportion of the given training dataset. A clustering algorithm is used to form relatively coherent groups which the expert examines as an entity to label records as one of four classes: Red (definite intrusion), Yellow (possibly intrusion), Blue (probably normal), and Green (definite normal). Subsequently, an ensemble classifier-based data cleansing approach is used to detect records that were likely mislabeled by the expert. The proposed approach is investigated with a case study of a large real-world WLAN. In addition, ensemble classifier-based intrusion detection models built using the labeled training dataset demonstrate the effectiveness of the labeling process with good generalization accuracy over multiple test datasets. <\/jats:p>","DOI":"10.1142\/s0218213008004035","type":"journal-article","created":{"date-parts":[[2008,6,24]],"date-time":"2008-06-24T05:38:40Z","timestamp":1214285920000},"page":"521-537","source":"Crossref","is-referenced-by-count":2,"title":["LOW-EFFORT LABELING OF NETWORK EVENTS FOR INTRUSION DETECTION IN WLANS"],"prefix":"10.1142","volume":"17","author":[{"given":"TAGHI M.","family":"KHOSHGOFTAAR","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Florida Atlantic University, 777 Glades Rd., Boca Raton, FL 33431, USA"}]},{"given":"NAEEM","family":"SELIYA","sequence":"additional","affiliation":[{"name":"Computer and Information Science, University of Michigan\u2013Dearborn, 4901 Evergreen Rd., Dearborn, MI 48128, USA"}]},{"given":"CHRIS","family":"SEIFFERT","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Florida Atlantic University, 777 Glades Rd., Boca Raton, FL 33431, USA"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf2","volume":"6","author":"Kumar V.","journal-title":"IEEE Distributed Systems"},{"key":"rf10","doi-asserted-by":"crossref","first-page":"309","DOI":"10.3233\/IDA-2005-9306","volume":"9","author":"Khoshgoftaar T. M.","journal-title":"Intelligent Data Analysis: An International Journal"},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007330508534"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1080\/10556780108805809"},{"key":"rf20","volume-title":"Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations","author":"Witten I. H.","year":"1999"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213008004035","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T13:10:06Z","timestamp":1565183406000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213008004035"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,6]]},"references-count":5,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2008,6]]}},"alternative-id":["10.1142\/S0218213008004035"],"URL":"https:\/\/doi.org\/10.1142\/s0218213008004035","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,6]]}}}