{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T21:30:42Z","timestamp":1778621442852,"version":"3.51.4"},"reference-count":41,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,3,26]],"date-time":"2021-03-26T00:00:00Z","timestamp":1616716800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071056"],"award-info":[{"award-number":["62071056"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,3,26]]},"abstract":"<jats:p>With the increase of Internet visits and connections, it is becoming essential and arduous to protect the networks and different devices of the Internet of Things (IoT) from malicious attacks. The intrusion detection systems (IDSs) based on supervised machine learning (ML) methods require a large number of labeled samples. However, the number of abnormal behaviors is far less than that of normal behaviors, let alone that the shots of malicious behavior samples which can be intercepted as training dataset are actually limited. Consequently, it is a key research topic to conduct the anomaly detection for the small number of abnormal behavior samples. This paper proposes an anomaly detection model with a few abnormal samples to solve the problem in few-shot detection based on convolutional neural networks (CNN) and autoencoder (AE). This model mainly consists of the CNN-based supervised pretraining module and the AE-based data reconstruction module. Only a few abnormal samples are utilized to the pretrain module to build the structure of extracting deep features. The data reconstruction module simply chooses the deep features of normal samples as training data. There also exist some effective attention mechanisms in the pretraining module. Through the pretraining of small samples, the accuracy of abnormal detection is improved compared with merely training normal samples with AE. The simulation results prove that this solution can solve the above problems occurring in network behavior anomaly detection. In comparison to the original AE model and other clustering methods, the proposed model advances the detection results in a visible way.<\/jats:p>","DOI":"10.1155\/2021\/6659022","type":"journal-article","created":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T18:50:16Z","timestamp":1616871016000},"page":"1-13","source":"Crossref","is-referenced-by-count":37,"title":["Deep-Feature-Based Autoencoder Network for Few-Shot Malicious Traffic Detection"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2896-4595","authenticated-orcid":true,"given":"Mingshu","family":"He","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3490-963X","authenticated-orcid":true,"given":"Xiaojuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5538-6671","authenticated-orcid":true,"given":"Junhua","family":"Zhou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Intelligent Manufacturing System Technology, Beijing Institute of Electronic System Engineering, Beijing 100854, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0089-3980","authenticated-orcid":true,"given":"Yuanyuan","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm SE-10044, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4855-2464","authenticated-orcid":true,"given":"Lei","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7383-0852","authenticated-orcid":true,"given":"Xinlei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","article-title":"IoT: number of connected devices worldwide 2012\u20132025, Statista","author":"Statista Research Department","year":"2019"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.10.016"},{"key":"3","article-title":"Machine learning for computer security detection systems: practical feedback and solutions","author":"A. Beaugnon","year":"2018"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1145\/1151659.1159952"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1109\/surv.2013.100613.00161"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s11128-020-02882-4"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.32604\/jcs.2020.010548"},{"key":"8","article-title":"FlowScan: a network traffic flow reporting and visualization tool","author":"D. Plonka"},{"key":"9","article-title":"Collecting network-level packets into a data structure in response to an abnormal condition","author":"E. Kenigsberg","year":"2012"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/mnet.2014.6863129"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.18178\/jacn.2017.5.2.241"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1109\/icoin.2017.7899588"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.26483\/ijarcs.v10i2.6395"},{"issue":"6","key":"14","first-page":"446","article-title":"A study on NSL-KDD dataset for intrusion detection system based on classification algorithms","volume":"4","author":"L. Dhanabal","year":"2015","journal-title":"International Journal of Advanced Research in Computer and Communication Engineering"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-13-6001-5_43"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-020-00318-5"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-018-9564-y"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jisa.2020.102564"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2908225"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.3390\/fi12100167"},{"key":"21","article-title":"Intrusion detection evaluation dataset (CIC-IDS2017)","author":"I. Sharafaldin"},{"key":"22","first-page":"21","article-title":"A deep learning approach for network intrusion detection system","author":"A. Javaid"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2020.09802"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/iccsnt47585.2019.8962493"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2019.06115"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2020.010091"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1109\/twc.2017.2769644"},{"issue":"2","key":"28","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1109\/JIOT.2018.2878435","article-title":"Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor\u2013critic deep reinforcement learning","volume":"6","author":"Y. Wei","year":"2018","journal-title":"IEEE Internet of Things Journal"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2019.05.013"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1109\/iceeot.2016.7755181"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2917532"},{"key":"33","first-page":"187","article-title":"Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications","author":"H. Xu"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1109\/icc40277.2020.9148632"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"37","first-page":"2776","article-title":"SPCNet: spatial preserve and content-aware network for human pose estimation","author":"Y. Xiao"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.1177\/1475921718800363"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1109\/icassp.2013.6639346"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1109\/iccre.2017.7935070"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00745"}],"container-title":["Security and Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6659022.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6659022.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6659022.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,27]],"date-time":"2021-03-27T18:50:19Z","timestamp":1616871019000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/scn\/2021\/6659022\/"}},"subtitle":[],"editor":[{"given":"Liguo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,3,26]]},"references-count":41,"alternative-id":["6659022","6659022"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6659022","relation":{},"ISSN":["1939-0122","1939-0114"],"issn-type":[{"value":"1939-0122","type":"electronic"},{"value":"1939-0114","type":"print"}],"subject":[],"published":{"date-parts":[[2021,3,26]]}}}