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Recently, deep learning methods have shown superior performance compared with traditional machine learning methods. Deep learning methods can learn the raw data directly, but they are faced with expensive computing cost. To solve this problem, a preprocessing method based on multipacket input unit and compression is proposed, which takes m data packets as the input unit to maximize the retention of information and greatly compresses the raw traffic to shorten the data learning and training time. In our proposed method, the CNN network structure is optimized and the weights of some convolution layers are assigned directly by using the Gabor filter. Experimental results on the benchmark data set show that compared with the existing models, the proposed method improves the detection accuracy by 2.49% and reduces the training time by 62.1%. In addition, the experiments show that the proposed compression method has obvious advantages in detection accuracy and computational efficiency compared with the existing compression methods.<\/jats:p>","DOI":"10.1155\/2021\/5533269","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T22:35:07Z","timestamp":1623710107000},"page":"1-13","source":"Crossref","is-referenced-by-count":9,"title":["FCNN: An Efficient Intrusion Detection Method Based on Raw Network Traffic"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-5741-498X","authenticated-orcid":true,"given":"Yue","family":"Wang","sequence":"first","affiliation":[{"name":"Information Technology Institute, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Yiming","family":"Jiang","sequence":"additional","affiliation":[{"name":"Information Technology Institute, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]},{"given":"Julong","family":"Lan","sequence":"additional","affiliation":[{"name":"Information Technology Institute, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}]}],"member":"98","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/comst.2019.2904897"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/tnsm.2019.2899085"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.05.036"},{"key":"4","doi-asserted-by":"crossref","DOI":"10.6028\/NIST.SP.800-94","volume-title":"Guide to Intrusion Detection and Prevention Systems (IDPS)","author":"K. 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