{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:50:00Z","timestamp":1777704600560,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2018,8,4]],"date-time":"2018-08-04T00:00:00Z","timestamp":1533340800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2018,12,24]]},"abstract":"<jats:p>Malware detection have long become a challenge in research. The existing methods rely on malware signature which are proved not to be effective nowadays. The recent researches focus on using probabilistic model such as machine learning to detect the existence of malware. They, however, do not achieve such a good performance. Particularly, machine learning techniques still have an issue of high feature engineering overhead. In this paper, we propose a deep learning method to detect malware based on their malicious behavior. Through experimentation, we show that our method can achieve a very high accuracy rate of 98.75 in F1 measure, compared to state of the art methods.<\/jats:p>","DOI":"10.3233\/jifs-169823","type":"journal-article","created":{"date-parts":[[2018,8,5]],"date-time":"2018-08-05T06:37:40Z","timestamp":1533451060000},"page":"5801-5814","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["An efficient classification of malware behavior using deep neural network"],"prefix":"10.1177","volume":"35","author":[{"given":"Quan Tran","family":"Hai","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hongik University, Sejong, Korea"}]},{"given":"Seong Oun","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Software and Communications Engineering, Hongik University, Sejong, Korea"}]}],"member":"179","published-online":{"date-parts":[[2018,8,4]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Panda labs panalabs report q2 2015: http:\/\/www.pandasecurity.coni\/mediacenter\/src\/uploads\/2014\/07\/Pandalabs-2015-Q2-EN.pdf"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-011-0152-x"},{"key":"e_1_3_1_4_2","doi-asserted-by":"crossref","unstructured":"AndersonB. StorlieC. and LaneT. Improving malware classification: Bridging the static\/dynamic gap in Proceedings of the 5th ACM Workshop on Security and Artificial Intelligence ACM 2012 pp. 3\u201314.","DOI":"10.1145\/2381896.2381900"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74320-0_10"},{"key":"e_1_3_1_6_2","first-page":"8","article-title":"Scalable, beavihor-based malware clustering","volume":"9","author":"Bayer U.","year":"2009","unstructured":"BayerU., ComparettiP.M., HlauschekC., KruegelC. and KirdaE., Scalable, beavihor-based malware clustering, in NDSS, vol. 9, 2009, pp. 8\u201311.","journal-title":"NDSS"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"FirdausiI. ErwinA. NugrohoA.S.Analysis of machine learning techniques used in beavihor-based malware detection in Advances in Computing Control and Telecommunication Technologies (ACT) 2010 Second International Conference on IEEE 2010 pp. 201\u2013203.","DOI":"10.1109\/ACT.2010.33"},{"key":"e_1_3_1_8_2","doi-asserted-by":"crossref","unstructured":"KolterJ.Z. and MaloofM.A. Learning to detect malicious executables in the wild in Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM 2004 pp. 470\u2013478.","DOI":"10.1145\/1014052.1014105"},{"key":"e_1_3_1_9_2","doi-asserted-by":"crossref","unstructured":"KongD. and YanG. Discriminant malware distance learning on structural information for automated malware classification in Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM 2013 pp. 1357\u20131365.","DOI":"10.1145\/2487575.2488219"},{"key":"e_1_3_1_10_2","unstructured":"LeeT. and ModyJ. Beavihoral classification in 15th European Institute for Computer Antivirus Research (EICAR 2006) Annual Conference 2006."},{"key":"e_1_3_1_11_2","doi-asserted-by":"crossref","unstructured":"NariS. and GhorbaniA.A. Automated malware classification based on network beavihor in Computing Networking and Communications (ICNC) 2013 International Conference on IEEE 2013 pp. 642\u2013647.","DOI":"10.1109\/ICCNC.2013.6504162"},{"key":"e_1_3_1_12_2","doi-asserted-by":"crossref","unstructured":"ParkY. ReevesD. MulukutlaV. and SundaravelB. Fast malware classification by automated beavihoral graph matching in Proceedings of the Sixth Annual Workshop on Cyber Security and Information Intelligence Research ACM 2010 p. 45.","DOI":"10.1145\/1852666.1852716"},{"key":"e_1_3_1_13_2","doi-asserted-by":"crossref","unstructured":"RieckK. HolzT.WillemsC. D\u00fcsselP. and LaskovP. Learning and classification of malware behor in International Conference on Detection of Intrusions and Malware and Vulnerability AssessmentSpringer 2008 pp. 108\u2013125.","DOI":"10.1007\/978-3-540-70542-0_6"},{"key":"e_1_3_1_14_2","doi-asserted-by":"crossref","unstructured":"SantosI. LaordenC. and BringasP.G. Collective classification for unknown malware detection in Security and Cryptography (SECRYPT) 2011 Proceedings of the International Conference on IEEE 2011 pp. 251\u2013256.","DOI":"10.5220\/0003452802510256"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-19934-9_53"},{"key":"e_1_3_1_16_2","doi-asserted-by":"crossref","unstructured":"SchultzM.G. EskinE. ZadokF. and StolfoS.J. Data mining methods for detection of new malicious executables in Security and Privacy 2001 S&P 2001 Proceedings 2001 IEEE Symposium on IEEE 2001 pp. 38\u201319.","DOI":"10.1109\/SECPRI.2001.924286"},{"issue":"6","key":"e_1_3_1_17_2","first-page":"48","article-title":"Detecting internet worms using data mining techniques","volume":"6","author":"Siddiqui M.","year":"2008","unstructured":"SiddiquiM., WangM.C. and LeeJ., Detecting internet worms using data mining techniques, Journal of Systemics, Cybernetics and Informatics, 6(6) (2008), 48\u201353.","journal-title":"Journal of Systemics, Cybernetics and Informatics"},{"key":"e_1_3_1_18_2","doi-asserted-by":"crossref","unstructured":"TianR. BattenL.M. and VersteegS. Function length as a tool for malware classification in Malicious and Unwanted Software 2008 MALWARE 2008 3rd International Conference on IEEE 2008 pp. 69\u201376.","DOI":"10.1109\/MALWARE.2008.4690860"},{"key":"e_1_3_1_19_2","doi-asserted-by":"crossref","unstructured":"ZolkipliM.F. and JantanA. An approach for malware behavior identification and classification in Computer Research and Development (ICCRD) 2011 3rd International Conference on vol. 1. IEEE 2011 pp. 191\u2013194.","DOI":"10.1109\/ICCRD.2011.5764001"},{"key":"e_1_3_1_20_2","unstructured":"ZhangX. ZhaoJ. and LeCunY. Character-level convolu-tional networks for text classification in Advances in Neural Information Processing Systems 2015 pp. 649\u2013657."},{"key":"e_1_3_1_21_2","unstructured":"JozefowiczR. VinyalsO. SchusterM. ShazeerN. and WuY. Exploring the limits of language modeling arXiv preprint arXiv:l602.02410 2016."},{"key":"e_1_3_1_22_2","unstructured":"ZarembaW. SutskeverI. and VinyalsO. Recurrent neural network regularization arXiv preprint arXiv: 1409.2329 2014."},{"key":"e_1_3_1_23_2","unstructured":"Apache kafka http:\/\/kafka.apache.org\/."},{"key":"e_1_3_1_24_2","unstructured":"Apache flume http:\/\/hortonworks.com\/apache\/flume\/."},{"key":"e_1_3_1_25_2","unstructured":"Apache spark http:\/\/spark.apache.org\/."},{"key":"e_1_3_1_26_2","unstructured":"Apache cassandra http:\/\/cassandra.apache.org\/."},{"key":"e_1_3_1_27_2","unstructured":"Parish - anli analysis tool https:\/\/github.coni\/aOrtega\/parish\/."},{"key":"e_1_3_1_28_2","unstructured":"ZhangX. and LeCunY. Text understanding from scratch arXiv preprint arXiv: 1502.01710 2015."},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/64.511768"},{"key":"e_1_3_1_30_2","first-page":"26","article-title":"Scaling to very very large corpora for natural language disambiguation","author":"Banko M.","year":"2001","unstructured":"BankoM. and BrillE., Scaling to very very large corpora for natural language disambiguation, in Proceedings of the 39th Annual Meeting on Association for Computational Linguistics Association for Computational Linguistics, 2001, pp. 26\u201333.","journal-title":"Proceedings of the 39th Annual Meeting on Association for Computational Linguistics Association for Computational Linguistics"},{"key":"e_1_3_1_31_2","doi-asserted-by":"crossref","unstructured":"DahlG.E. StokesJ.W. DengL. and YuD. Large-scale malware classification using random projections and neural networks in Acoustics Speech and Signal Processing (ICASSP) 2013 IEEE International Conference on IEEE 2013 pp. 3422\u20133426.","DOI":"10.1109\/ICASSP.2013.6638293"},{"key":"e_1_3_1_32_2","doi-asserted-by":"crossref","unstructured":"SaxeJ. and BerlinK. Deep neural network based malware detection using two dimensional binary program features in Malicious and Unwanted Software (MALWARE) 2015 10th International Conference on IEEE 2015 pp. 11\u201320.","DOI":"10.1109\/MALWARE.2015.7413680"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-40667-1_20"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169823","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169823","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169823","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:34Z","timestamp":1777455694000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169823"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8,4]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2018,12,24]]}},"alternative-id":["10.3233\/JIFS-169823"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169823","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,8,4]]}}}