{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:09:34Z","timestamp":1770883774361,"version":"3.50.1"},"publisher-location":"New York, New York, USA","reference-count":18,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1145\/3368926.3369733","type":"proceedings-article","created":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T13:30:11Z","timestamp":1576848611000},"page":"514-521","source":"Crossref","is-referenced-by-count":14,"title":["Toward a Deep Learning Approach for Detecting PHP Webshell"],"prefix":"10.1145","author":[{"given":"Ngoc-Hoa","family":"Nguyen","sequence":"first","affiliation":[{"name":"VNU University of Engineering and Technology, Hanoi, Vietnam"}]},{"given":"Viet-Ha","family":"Le","sequence":"additional","affiliation":[{"name":"Office of the Government, Hanoi, Vietnam"}]},{"given":"Van-On","family":"Phung","sequence":"additional","affiliation":[{"name":"Office of the Government, Hanoi, Vietnam"}]},{"given":"Phuong-Hanh","family":"Du","sequence":"additional","affiliation":[{"name":"VNU University of Engineering and Technology, Hanoi, Vietnam"}]}],"member":"320","reference":[{"key":"key-10.1145\/3368926.3369733-1","unstructured":"2019. PHP Malware Finder. https:\/\/github.com\/nbs-system\/php-malware-finder."},{"key":"key-10.1145\/3368926.3369733-2","unstructured":"2019. Web Technology Surveys. http:\/\/w3techs.com\/technologies\/overview\/programming_language\/all\/."},{"key":"key-10.1145\/3368926.3369733-3","doi-asserted-by":"crossref","unstructured":"G. P. Bherde and M. A. Pund. 2016. Recent attack prevention techniques in web service applications. In 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). 1174--1180. https:\/\/doi.org\/10.1109\/ICACDOT.2016.7877771","DOI":"10.1109\/ICACDOT.2016.7877771"},{"key":"key-10.1145\/3368926.3369733-4","doi-asserted-by":"crossref","unstructured":"Daniel Bilar. 2007. Malware detection through opcode sequence analysis using machine learning. Int. J. Electronic Security and Digital Forensics 1 (2007). https:\/\/doi.org\/10.1504\/IJESDF.2007.016865","DOI":"10.1504\/IJESDF.2007.016865"},{"key":"key-10.1145\/3368926.3369733-5","unstructured":"Simen Rune Bragen. 2015. Opcodes as predictor for malware. VDP::Mathematics and natural science: 400::Information and communication science: 420::Security and vulnerability: 424 1 (01 2015)."},{"key":"key-10.1145\/3368926.3369733-6","doi-asserted-by":"crossref","unstructured":"H. Cui, D. Huang, Y. Fang, L. Liu, and C. Huang. 2018. Webshell Detection Based on Random ForestâĂrŞGradient Boosting Decision Tree Algorithm. In 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC). 153--160. https:\/\/doi.org\/10.1109\/DSC.2018.00030","DOI":"10.1109\/DSC.2018.00030"},{"key":"key-10.1145\/3368926.3369733-7","doi-asserted-by":"crossref","unstructured":"Z. Cui, F. Xue, X. Cai, Y. Cao, G. Wang, and J. Chen. 2018. Detection of Malicious Code Variants Based on Deep Learning. IEEE Transactions on Industrial Informatics 14, 7 (July 2018), 3187--3196. https:\/\/doi.org\/10.1109\/TII.2018.2822680","DOI":"10.1109\/TII.2018.2822680"},{"key":"key-10.1145\/3368926.3369733-8","doi-asserted-by":"crossref","unstructured":"Z. Cui, F. Xue, X. Cai, Y. Cao, G. Wang, and J. Chen. 2018. Detection of Malicious Code Variants Based on Deep Learning. IEEE Transactions on Industrial Informatics 14, 7 (July 2018), 3187--3196. https:\/\/doi.org\/10.1109\/TII.2018.2822680","DOI":"10.1109\/TII.2018.2822680"},{"key":"key-10.1145\/3368926.3369733-9","doi-asserted-by":"crossref","unstructured":"Y. Liu and Y. Wang. 2019. A Robust Malware Detection System Using Deep Learning on API Calls. In 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1456--1460. https:\/\/doi.org\/10.1109\/ITNEC.2019.8728992","DOI":"10.1109\/ITNEC.2019.8728992"},{"key":"key-10.1145\/3368926.3369733-10","doi-asserted-by":"crossref","unstructured":"A. Masood and J. Java. 2015. Static analysis for web service security - Tools amp; techniques for a secure development life cycle. In 2015 IEEE International Symposium on Technologies for Homeland Security (HST). 1--6. https:\/\/doi.org\/10.1109\/THS.2015.7225337","DOI":"10.1109\/THS.2015.7225337"},{"key":"key-10.1145\/3368926.3369733-11","doi-asserted-by":"crossref","unstructured":"M. Mazumder and T. Braje. 2016. Safe Client\/Server Web Development with Haskell. In 2016 IEEE Cybersecurity Development (SecDev). 150--150. https:\/\/doi.org\/10.1109\/SecDev.2016.040","DOI":"10.1109\/SecDev.2016.040"},{"key":"key-10.1145\/3368926.3369733-12","doi-asserted-by":"crossref","unstructured":"M. A. E. Mohd Efendi, Z. Ibrahim, M. N. Ahmad Zawawi, F. Abdul Rahim, N. A. Muhamad Pahri, and A. Ismail. 2019. A Survey on Deception Techniques for Securing Web Application. In 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS). 328--331. https:\/\/doi.org\/10.1109\/BigDataSecurity-HPSC-IDS.2019.00066","DOI":"10.1109\/BigDataSecurity-HPSC-IDS.2019.00066"},{"key":"key-10.1145\/3368926.3369733-13","unstructured":"Internet Live Stats. 2019. Internet Usage and Social Media Statistics. https:\/\/www.internetlivestats.com\/."},{"key":"key-10.1145\/3368926.3369733-14","doi-asserted-by":"crossref","unstructured":"Yifan Tian, Jiabao Wang, Zhenji Zhou, and Shengli Zhou. 2017. CNN-Webshell: Malicious Web Shell Detection with Convolutional Neural Network. In Proceedings of the 2017 VI International Conference on Network, Communication and Computing (ICNCC 2017). ACM, New York, NY, USA, 75--79. https:\/\/doi.org\/10.1145\/3171592.3171593","DOI":"10.1145\/3171592.3171593"},{"key":"key-10.1145\/3368926.3369733-15","unstructured":"Le V-G, Nguyen H-T, Pham D-P, Phung V-O, and N-H Nguyen. 2019. GuruWS: A Hybrid Platform for Detecting Malicious Web Shells and Web Application Vulnerabilities. Transactions on Computational Collective Intelligence, Springer, Berlin, Heidelberg 11370, XXXII (01 2019), 184--208."},{"key":"key-10.1145\/3368926.3369733-16","unstructured":"Le V-G, Nguyen H-T, Lu L-D, and N-H Nguyen. 2016. A solution for automatically malicious Web shell and Web application vulnerability detection. In Computational Collective Intelligence, Volume 9875 of the series Lecture Notes in Computer Science. Springer-Verlag, Berlin, Heidelberg, 367--378."},{"key":"key-10.1145\/3368926.3369733-17","doi-asserted-by":"crossref","unstructured":"M. Yeo, Y. Koo, Y. Yoon, T. Hwang, J. Ryu, J. Song, and C. Park. 2018. Flow-based malware detection using convolutional neural network. In 2018 International Conference on Information Networking (ICOIN). 910--913. https:\/\/doi.org\/10.1109\/ICOIN.2018.8343255","DOI":"10.1109\/ICOIN.2018.8343255"},{"key":"key-10.1145\/3368926.3369733-18","doi-asserted-by":"crossref","unstructured":"K. ÃŰzkan, Åđ. IÅ§Äśk, and Y. Kartal. 2018. Evaluation of convolutional neural network features for malware detection. In 2018 6th International Symposium on Digital Forensic and Security (ISDFS). 1--5. https:\/\/doi.org\/10.1109\/ISDFS.2018.8355390","DOI":"10.1109\/ISDFS.2018.8355390"}],"event":{"name":"the Tenth International Symposium","location":"Hanoi, Ha Long Bay, Viet Nam","acronym":"SoICT 2019","number":"10","sponsor":["SOICT, School of Information and Communication Technology - HUST","NAFOSTED, The National Foundation for Science and Technology Development"],"start":{"date-parts":[[2019,12,4]]},"end":{"date-parts":[[2019,12,6]]}},"container-title":["Proceedings of the Tenth International Symposium on Information and Communication Technology  - SoICT 2019"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3368926.3369733","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3369733&ftid=2101264&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:53:05Z","timestamp":1750204385000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3368926.3369733"}},"subtitle":[],"proceedings-subject":"Information and Communication Technology","short-title":[],"issued":{"date-parts":[[2019]]},"references-count":18,"URL":"https:\/\/doi.org\/10.1145\/3368926.3369733","relation":{},"subject":[],"published":{"date-parts":[[2019]]}}}