{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T23:10:20Z","timestamp":1736377820343,"version":"3.32.0"},"publisher-location":"Berlin, Heidelberg","reference-count":20,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540344827"},{"type":"electronic","value":"9783540344834"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2006]]},"DOI":"10.1007\/11760191_33","type":"book-chapter","created":{"date-parts":[[2006,5,11]],"date-time":"2006-05-11T14:00:36Z","timestamp":1147356036000},"page":"224-230","source":"Crossref","is-referenced-by-count":14,"title":["Building Lightweight Intrusion Detection System Based on Random Forest"],"prefix":"10.1007","author":[{"given":"Dong Seong","family":"Kim","sequence":"first","affiliation":[]},{"given":"Sang Min","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Jong Sou","family":"Park","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"33_CR1","volume-title":"Classification and Regression Trees","author":"L. Breiman","year":"1984","unstructured":"Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman and Hall, New York (1984)"},{"issue":"1","key":"33_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L. Breiman","year":"2001","unstructured":"Breiman, L.: Random forest. Machine Learning\u00a045(1), 5\u201332 (2001)","journal-title":"Machine Learning"},{"key":"33_CR3","volume-title":"Pattern Classification","author":"R.O. Duda","year":"2001","unstructured":"Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2001)","edition":"2"},{"key":"33_CR4","unstructured":"Fox, K.L., Henning, R.R., Reed, J.H., Simonian, R.P.: A Neural Network Approach Towards Intrusion Detection. In: Proc. of the 13th National Computer Security Conf., Washington, DC (1990)"},{"key":"33_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1007\/3-540-45665-1_15","volume-title":"Pattern Recognition with Support Vector Machines","author":"M. Fugate","year":"2002","unstructured":"Fugate, M., Gattiker, J.R.: Anomaly Detection Enhanced Classification in Computer Intrusion Detection. In: Lee, S.-W., Verri, A. (eds.) SVM 2002. LNCS, vol.\u00a02388, pp. 186\u2013197. Springer, Heidelberg (2002)"},{"key":"33_CR6","unstructured":"Hu, W., Liao, Y., Vemuri, V.R.: Robust Support Vector Machines for Anomaly Detection in Computer Security. In: Proc. of Int. Conf. on Machine Learning and Applications 2003, pp. 168\u2013174. CSREA Press (2003)"},{"key":"33_CR7","unstructured":"KDD Cup 1999 Data, http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/kddcup99.html"},{"key":"33_CR8","unstructured":"KDD-CUP-99 Task Description, http:\/\/kdd.ics.uci.edu\/databases\/kddcup99\/task.html"},{"key":"33_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/11427469_67","volume-title":"Advances in Neural Networks \u2013 ISNN 2005","author":"D. Kim","year":"2005","unstructured":"Kim, D., Nguyen, H.-N., Ohn, S.-Y., Park, J.: Fusions of GA and SVM for Anomaly Detection in Intrusion Detection System. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol.\u00a03498, pp. 415\u2013420. Springer, Heidelberg (2005)"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Kruegel, C., Valeur, F.: Stateful Intrusion Detection for High-Speed Networks. In: Proc. of the IEEE Symposium on Research on Security and Privacy, pp. 285\u2013293 (2002)","DOI":"10.1109\/SECPRI.2002.1004378"},{"key":"33_CR11","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/S0925-2312(03)00431-4","volume":"55","author":"D. Meyer","year":"2003","unstructured":"Meyer, D., Leisch, F., Hornik, K.: The Support Vector Machine under Test. Neurocomputing\u00a055, 169\u2013186 (2003)","journal-title":"Neurocomputing"},{"key":"33_CR12","unstructured":"Nguyen, B.V.: An Application of Support Vector Machines to Anomaly Detection. Research in Computer Science-Support Vector Machine, report (2002)"},{"key":"33_CR13","first-page":"334","volume-title":"Proc. of the 36th Hawaii Int. Conf. on System Sciences","author":"D. Ourston","year":"2002","unstructured":"Ourston, D., Matzner, S., Stump, W., Hopkins, B.: Applications of Hidden Markov Models to Detecting Multi-Stage Network Attacks. In: Proc. of the 36th Hawaii Int. Conf. on System Sciences, pp. 334\u2013343. IEEE Computer Society Press, Los Alamitos (2002)"},{"key":"33_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/11599548_24","volume-title":"Information Security and Cryptology","author":"J. Park","year":"2005","unstructured":"Park, J., Shazzad, S.K.M., Kim, D.: Toward Modeling Lightweight Intrusion Detection System through Correlation-Based Hybrid Feature Selection. In: Feng, D., Lin, D., Yung, M. (eds.) CISC 2005. LNCS, vol.\u00a03822, pp. 279\u2013289. Springer, Heidelberg (2005)"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Sabhnani, M., Serpen, G.: On Failure of Machine Learning Algorithms for Detecting Misuse in KDD Intrusion Detection Data Set. Intelligent Analysis (2004)","DOI":"10.3233\/IDA-2004-8406"},{"key":"33_CR16","unstructured":"SNORT, http:\/\/www.snort.org"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Song, H., Lockwood, J.W.: Efficient Packet Classification for Network Intrusion Detection using FPGA. In: Schmit, H., Wilton, S.J.E. (eds.) Proc. of the ACM\/SIGDA 13th Int. Symposium on Field-Programmable Gate Arrays. FPGA, pp. 238\u2013245 (2005)","DOI":"10.1145\/1046192.1046223"},{"key":"33_CR18","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/SAINT.2003.1183050","volume-title":"Proc. of the 2003 Int. Symposium on Applications and the Internet Technology","author":"A.H. Sung","year":"2003","unstructured":"Sung, A.H., Mukkamala, S.: Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks. In: Proc. of the 2003 Int. Symposium on Applications and the Internet Technology, pp. 209\u2013216. IEEE Computer Society Press, Los Alamitos (2003)"},{"key":"33_CR19","unstructured":"The R Project for Statistical Computing, http:\/\/www.r-project.org\/"},{"key":"33_CR20","series-title":"Lecture Notes in Computer Science","first-page":"458","volume-title":"Adaptive and Natural Computing Algorithms","author":"S. Mukkamala","year":"2007","unstructured":"Mukkamala, S., Sung, A.H., Ribeiro, B.M.: Model Selection for Kernel Based Intrusion Detection Systems. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007. LNCS, vol.\u00a04431, pp. 458\u2013461. Springer, Heidelberg (2007)"}],"container-title":["Lecture Notes in Computer Science","Advances in Neural Networks - ISNN 2006"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/11760191_33.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,8]],"date-time":"2025-01-08T22:32:37Z","timestamp":1736375557000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/11760191_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2006]]},"ISBN":["9783540344827","9783540344834"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/11760191_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2006]]}}}