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The method employs tcpdump packets and extracts multiple features from the packet headers. The tcpdump packets are transferred into documents based on the features. A topic model is trained using only attack-free traffic in order to learn the behavior patterns of normal traffic. Then, the test traffic is analyzed against the learned behavior patterns to measure the extent to which the test traffic resembles the normal traffic. A threshold is defined in the training phase as the minimum likelihood of a host. In the test phase, when a host\u2019s test traffic has a likelihood lower than the host\u2019s threshold, the traffic is labeled as an intrusion. The intrusion detection system is validated using DARPA 1999 dataset. Experiment shows that our method is suitable to protect the security of MEC.<\/jats:p>","DOI":"10.1155\/2020\/8860418","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T23:32:49Z","timestamp":1598398369000},"page":"1-12","source":"Crossref","is-referenced-by-count":7,"title":["Packet-Based Intrusion Detection Using Bayesian Topic Models in Mobile Edge Computing"],"prefix":"10.1155","volume":"2020","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9421-9358","authenticated-orcid":true,"given":"Xuefei","family":"Cao","sequence":"first","affiliation":[{"name":"School of Cyber Engineering, Xidian University, No. 2 South Taibai Rd., Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1778-4943","authenticated-orcid":true,"given":"Yulong","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University, No. 2 South Taibai Rd., Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Chen","sequence":"additional","affiliation":[{"name":"National Lab of Radar Signal Process, Xidian University, No. 2 South Taibai Rd., Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2017.2705720"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsm.2019.2939221"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/tnsm.2012.031512.110146"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2020.2982186"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.2987994"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/tifs.2018.2868614"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/tnsm.2012.051712.110124"},{"key":"8","first-page":"993","volume":"3","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0307752101"},{"issue":"6-7","key":"12","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.cose.2011.05.007","volume":"30","year":"2011","journal-title":"Computers & Security"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1109\/tc.2013.13"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/tdsc.2008.69"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1145\/382912.382914"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/s1389-1286(99)00112-7"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2005.05.003"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2006.07.018"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2012.07.009"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1016\/j.protcy.2012.05.017"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.11.007"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.05.017"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.01.009"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-017-9567-1"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2006.10.146"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2007.05.002"},{"first-page":"142","volume-title":"Intrusion detection and classification with autoencoded deep neural network","year":"2019","key":"32"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.12.009"},{"first-page":"14","volume-title":"A Fuzzy logic based network intrusion detection system for predicting the TCP SYN flooding attack","year":"2017","key":"35"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2006.01.023"},{"key":"40","first-page":"473","volume-title":"Packet header anomaly detection using statistical analysis. 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