{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T22:30:09Z","timestamp":1793485809504,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":16,"publisher":"ACM","license":[{"start":{"date-parts":[[2018,3,12]],"date-time":"2018-03-12T00:00:00Z","timestamp":1520812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2018,3,12]]},"DOI":"10.1145\/3194452.3194469","type":"proceedings-article","created":{"date-parts":[[2018,5,29]],"date-time":"2018-05-29T12:24:55Z","timestamp":1527596695000},"page":"47-51","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":81,"title":["DeepXSS"],"prefix":"10.1145","author":[{"given":"Yong","family":"Fang","sequence":"first","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan, P.R.China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu, Sichuan, P.R.China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan, P.R.China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Cybersecurity, Sichuan University, Chengdu, Sichuan, P.R.China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2018,3,12]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"OWASP OWASP \n      Top\n     10 - 2017.\n   Available at https:\/\/www.owasp.org\/images\/7\/72\/OWASP_Top_10-2017_(en).pdf.pdf. Accessed \n  November\n   3 2017\n  .  OWASP OWASP Top 10 - 2017. Available at https:\/\/www.owasp.org\/images\/7\/72\/OWASP_Top_10-2017_(en).pdf.pdf. Accessed November 3 2017."},{"key":"e_1_3_2_1_2_1","volume-title":"Reliability and Security (QRS), 2017 IEEE International Conference on. IEEE","author":"Mohammadi M","unstructured":"Mohammadi M , Chu B , Lipford H R . Detecting Cross-Site Scripting Vulnerabilities through Automated Unit Testing{C}\/\/Software Quality , Reliability and Security (QRS), 2017 IEEE International Conference on. IEEE , 2017: 364--373. Mohammadi M, Chu B, Lipford H R. Detecting Cross-Site Scripting Vulnerabilities through Automated Unit Testing{C}\/\/Software Quality, Reliability and Security (QRS), 2017 IEEE International Conference on. IEEE, 2017: 364--373."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.14257\/ijsia.2015.9.3.14"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13198-015-0376-0"},{"key":"e_1_3_2_1_5_1","volume-title":"Analysis","author":"Hadpawat T","year":"2017","unstructured":"Hadpawat T , Vaya D. Analysis of Prevention of XSS Attacks at Client Side{J} . Analysis , 2017 , 173(10). Hadpawat T, Vaya D. Analysis of Prevention of XSS Attacks at Client Side{J}. Analysis, 2017, 173(10)."},{"key":"e_1_3_2_1_6_1","first-page":"1","article-title":"Text-mining based predictive model to detect XSS vulnerable files in Web applications{C}\/\/India Conference (INDICON), 2015 Annual IEEE","volume":"2015","author":"Gupta M K","unstructured":"Gupta M K , Govil M C , Singh G . Text-mining based predictive model to detect XSS vulnerable files in Web applications{C}\/\/India Conference (INDICON), 2015 Annual IEEE . IEEE , 2015 : 1 -- 6 . Gupta M K, Govil M C, Singh G. Text-mining based predictive model to detect XSS vulnerable files in Web applications{C}\/\/India Conference (INDICON), 2015 Annual IEEE. IEEE, 2015: 1--6.","journal-title":"IEEE"},{"issue":"5","key":"e_1_3_2_1_7_1","first-page":"761","article-title":"An Unsupervised Method for Detection of XSS Attack{J}","volume":"19","author":"Goswami S","year":"2017","unstructured":"Goswami S , Hoque N , Bhattacharyya D K , An Unsupervised Method for Detection of XSS Attack{J} . IJ Network Security , 2017 , 19 ( 5 ): 761 -- 775 . Goswami S, Hoque N, Bhattacharyya D K, et al. An Unsupervised Method for Detection of XSS Attack{J}. IJ Network Security, 2017, 19(5): 761--775.","journal-title":"IJ Network Security"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2660859.2660969"},{"issue":"4","key":"e_1_3_2_1_9_1","article-title":"An Efficient XSS Attack Detection Approach Based on Machine Learning Classifier on SNSs{J}","volume":"13","author":"Rathore S","year":"2017","unstructured":"Rathore S , Sharma P K , Park J H . XSS Classifier : An Efficient XSS Attack Detection Approach Based on Machine Learning Classifier on SNSs{J} . Journal of Information Processing Systems , 2017 , 13 ( 4 ). Rathore S, Sharma P K, Park J H. XSSClassifier: An Efficient XSS Attack Detection Approach Based on Machine Learning Classifier on SNSs{J}. Journal of Information Processing Systems, 2017, 13(4).","journal-title":"Journal of Information Processing Systems"},{"key":"e_1_3_2_1_10_1","volume-title":"Accessed","author":"Filter Evasion Cheat Sheet","year":"2017","unstructured":"OWASP, XSS Filter Evasion Cheat Sheet . Available at https:\/\/www.owasp.org\/index.php\/XSS_Filter_Evasion_Cheat_Sheet. Accessed December 12, 2017 . OWASP, XSS Filter Evasion Cheat Sheet. Available at https:\/\/www.owasp.org\/index.php\/XSS_Filter_Evasion_Cheat_Sheet. Accessed December 12, 2017."},{"key":"e_1_3_2_1_11_1","volume-title":"Efficient estimation of word representations in vector space{J}. arXiv preprint arXiv:1301.3781","author":"Mikolov T","year":"2013","unstructured":"Mikolov T , Chen K , Corrado G , Efficient estimation of word representations in vector space{J}. arXiv preprint arXiv:1301.3781 , 2013 . Mikolov T, Chen K, Corrado G, et al. Efficient estimation of word representations in vector space{J}. arXiv preprint arXiv:1301.3781, 2013."},{"key":"e_1_3_2_1_12_1","unstructured":"Joulin A Mikolov T. Inferring algorithmic patterns with stack-augmented recurrent nets{C}\/\/Advances in neural information processing systems. 2015: 190--198.   Joulin A Mikolov T. Inferring algorithmic patterns with stack-augmented recurrent nets{C}\/\/Advances in neural information processing systems. 2015: 190--198."},{"key":"e_1_3_2_1_13_1","first-page":"1259","article-title":"LSTM-Based Memory Profiling for Predicting Data Attacks in Distributed Big Data Systems{C}\/\/Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017 IEEE International","volume":"2017","author":"Aditham S","unstructured":"Aditham S , Ranganathan N , Katkoori S . LSTM-Based Memory Profiling for Predicting Data Attacks in Distributed Big Data Systems{C}\/\/Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017 IEEE International . IEEE , 2017 : 1259 -- 1267 . Aditham S, Ranganathan N, Katkoori S. LSTM-Based Memory Profiling for Predicting Data Attacks in Distributed Big Data Systems{C}\/\/Parallel and Distributed Processing Symposium Workshops (IPDPSW), 2017 IEEE International. IEEE, 2017: 1259--1267.","journal-title":"IEEE"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_15_1","volume-title":"The Python Deep Learning library.","author":"Keras","year":"2018","unstructured":"Keras , Keras : The Python Deep Learning library. Available at https:\/\/keras.io\/. Accessed January 9, 2018 . Keras, Keras: The Python Deep Learning library. Available at https:\/\/keras.io\/. Accessed January 9, 2018."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/HPCC.2014.137"}],"event":{"name":"ICCAI 2018: 2018 International Conference on Computing and Artificial Intelligence","location":"Chengdu China","acronym":"ICCAI 2018","sponsor":["Sichuan University"]},"container-title":["Proceedings of the 2018 International Conference on Computing and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3194452.3194469","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3194452.3194469","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:26:46Z","timestamp":1750213606000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3194452.3194469"}},"subtitle":["Cross Site Scripting Detection Based on Deep Learning"],"short-title":[],"issued":{"date-parts":[[2018,3,12]]},"references-count":16,"alternative-id":["10.1145\/3194452.3194469","10.1145\/3194452"],"URL":"https:\/\/doi.org\/10.1145\/3194452.3194469","relation":{},"subject":[],"published":{"date-parts":[[2018,3,12]]},"assertion":[{"value":"2018-03-12","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}