{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T04:59:21Z","timestamp":1725512361530},"publisher-location":"Berlin, Heidelberg","reference-count":15,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540681243"},{"type":"electronic","value":"9783540681250"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.1007\/978-3-540-68125-0_58","type":"book-chapter","created":{"date-parts":[[2008,5,10]],"date-time":"2008-05-10T03:40:05Z","timestamp":1210390805000},"page":"626-633","source":"Crossref","is-referenced-by-count":3,"title":["PAID: Packet Analysis for Anomaly Intrusion Detection"],"prefix":"10.1007","author":[{"given":"Kuo-Chen","family":"Lee","sequence":"first","affiliation":[]},{"given":"Jason","family":"Chang","sequence":"additional","affiliation":[]},{"given":"Ming-Syan","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"58_CR1","doi-asserted-by":"crossref","unstructured":"Cardenas, A., Baras, J.S.: A Framework for the Evaluation of Intrusion Detection Systems. In: IEEE Symposium on Security and Privacy (May 2006)","DOI":"10.1109\/SP.2006.2"},{"key":"58_CR2","doi-asserted-by":"crossref","unstructured":"Lakhina, A., Crovella, M., Diot, C.: Mining anomalies using traffic feature distributions. In: SIGCOMM (2005)","DOI":"10.1145\/1080091.1080118"},{"key":"58_CR3","doi-asserted-by":"crossref","unstructured":"Kruegel, C., Toth, T., Kirda, E.: Service Specific Anomaly Detection for Network Intrusion Detection. In: Symposium on Applied Computing (SAC), Span (March 2002)","DOI":"10.1145\/508791.508835"},{"key":"58_CR4","unstructured":"Jensen, F.V.: Introduction to Bayesien networks. UCL Press (1996)"},{"key":"58_CR5","doi-asserted-by":"crossref","unstructured":"Gu, G., Fogla, P., Dagon, D., Lee, W., Skoric, B.: Towards an Information-Theoretic Framework for Analyzing Intrusion Detection Systems. In: Proc. European Symposium Research Computer Security (September 2006)","DOI":"10.1007\/11863908_32"},{"key":"58_CR6","unstructured":"Javits, H.S., Valdes, A.: The NIDES statistical component: Description and justification. Technical report, SRI International, Computer Science Laboratory (1993)"},{"key":"58_CR7","unstructured":"Hoagland, J.: SPADE, Silican Defense (2000), \n                    \n                      http:\/\/www.silicondefense.com\/software\/spice"},{"key":"58_CR8","unstructured":"KDD99 cup dataset (2006), \n                    \n                      http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html"},{"key":"58_CR9","unstructured":"Massachusetts Institute of Technology Lincoln Laboratory, 1998 darpa intrusion detection evaluation dataset overview (2005), \n                    \n                      http:\/\/www.ll.mit.edu\/IST\/ideval\/"},{"key":"58_CR10","unstructured":"Martin Roesch. Snort - lightweight intrusion detection for networks (2007), \n                    \n                      http:\/\/www.snort.org"},{"key":"58_CR11","doi-asserted-by":"crossref","unstructured":"Mahoney, M., Chan, P.K.: Learning Nonstationary Models of Normal Network Traffic for Detecting Novel Attacks. In: Proc. ACM SIGKDD (2002)","DOI":"10.1145\/775047.775102"},{"key":"58_CR12","doi-asserted-by":"crossref","unstructured":"Mahoney, M.: Network Traffic Anomaly Detection Based on Packet Bytes. In: Proc. ACM-SAC (2003)","DOI":"10.1145\/952532.952601"},{"key":"58_CR13","doi-asserted-by":"crossref","unstructured":"Goldman, R.: A Stochastic Model for Intrusions. In: Symposium on Recent Advances in Intrusion Detection (RAID) (2002)","DOI":"10.1007\/3-540-36084-0_11"},{"key":"58_CR14","unstructured":"Puttini, R., Marrakchi, Z., Me, L.: Bayesian Classification Model for Real-Time Intrusion Detection. In: 22th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (2002)"},{"key":"58_CR15","doi-asserted-by":"crossref","unstructured":"Mukkamala, S., Janoski, G., Sung, A.H.: Intrusion Detection Using Neural Networks and Support Vector Machines. In: Proc. IEEE Int\u2019l Joint Conf. on Neural Networks (2002)","DOI":"10.1109\/IJCNN.2002.1007774"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-540-68125-0_58.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:36:53Z","timestamp":1620002213000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-540-68125-0_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[null]]},"ISBN":["9783540681243","9783540681250"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-540-68125-0_58","relation":{},"subject":[]}}