{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T12:46:59Z","timestamp":1762865219482},"reference-count":5,"publisher":"World Scientific Pub Co Pte Ltd","issue":"06","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2006,12]]},"abstract":"<jats:p> Traditional host-based anomaly detection systems model normal behavior of applications by analyzing system call sequences. The current sequence is then examined (using the model) for anomalous behavior, which could correspond to attacks. Though these techniques have been shown to be quite effective, a key element is missing \u2013 the inclusion and utilization of the system call arguments. Recent research shows that sequence-based systems are prone to evasion. We propose an idea of learning different representations for system call arguments. Results indicate that this information can be effectively used for detecting more attacks than traditional sequence-based techniques, with reasonable storage and computational overhead. <\/jats:p>","DOI":"10.1142\/s0218213006003028","type":"journal-article","created":{"date-parts":[[2006,12,15]],"date-time":"2006-12-15T11:50:55Z","timestamp":1166183455000},"page":"875-892","source":"Crossref","is-referenced-by-count":23,"title":["ON THE LEARNING OF SYSTEM CALL ATTRIBUTES FOR HOST-BASED ANOMALY DETECTION"],"prefix":"10.1142","volume":"15","author":[{"given":"GAURAV","family":"TANDON","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, Florida Institute of Technology, 150 W. University Blvd., Melbourne, Florida 32901, USA"}]},{"given":"PHILIP K.","family":"CHAN","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, Florida Institute of Technology, 150 W. University Blvd., Melbourne, Florida 32901, USA"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/14.1.55"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-39945-3_8"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36084-0_4"},{"key":"rf12","doi-asserted-by":"publisher","DOI":"10.1109\/18.87000"},{"key":"rf13","doi-asserted-by":"publisher","DOI":"10.1016\/S1389-1286(00)00139-0"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213006003028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T16:55:10Z","timestamp":1565196910000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213006003028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006,12]]},"references-count":5,"journal-issue":{"issue":"06","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2006,12]]}},"alternative-id":["10.1142\/S0218213006003028"],"URL":"https:\/\/doi.org\/10.1142\/s0218213006003028","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2006,12]]}}}