{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T03:36:01Z","timestamp":1752982561535,"version":"3.40.3"},"publisher-location":"Cham","reference-count":65,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031500503"},{"type":"electronic","value":"9783031500510"}],"license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-50051-0_8","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T05:02:24Z","timestamp":1702530144000},"page":"99-116","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Empirical Evaluations of\u00a0Machine Learning Effectiveness in\u00a0Detecting Web Application Attacks"],"prefix":"10.1007","author":[{"given":"Muhusina","family":"Ismail","sequence":"first","affiliation":[]},{"given":"Saed","family":"Alrabaee","sequence":"additional","affiliation":[]},{"given":"Saad","family":"Harous","sequence":"additional","affiliation":[]},{"given":"Kim-Kwang Raymond","family":"Choo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Mikheeva, O.I., Gatchin Yuri, A., Savkov, S.V., Khammatova, R.M., et al.: Search methods for abnormal activities of web applications. J. Sci. Tech. Inf. Technol. Mech. Optics 126(2), 233\u2013242 (2020)","DOI":"10.17586\/2226-1494-2020-20-2-233-242"},{"issue":"2","key":"8_CR2","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MSP.2006.46","volume":"4","author":"T Holz","year":"2006","unstructured":"Holz, T., Marechal, S., Raynal, F.: New threats and attacks on the world wide web. IEEE Secur. Priv. 4(2), 72\u201375 (2006)","journal-title":"IEEE Secur. Priv."},{"key":"8_CR3","unstructured":"Moshchuk, A., Bragin, T., Deville, D., Gribble, S.D., Levy, H.M.: SpyProxy: Execution-based detection of malicious web content. In: USENIX Security Symposium, pp. 1\u201316 (2007)"},{"key":"8_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2020.102096","volume":"100","author":"A Tekerek","year":"2021","unstructured":"Tekerek, A.: A novel architecture for web-based attack detection using convolutional neural network. Comput. Secur. 100, 102096 (2021)","journal-title":"Comput. Secur."},{"key":"8_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102218","volume":"104","author":"Y Huang","year":"2021","unstructured":"Huang, Y., Li, T., Zhang, L., Li, B., Liu, X.: JSContana: malicious javascript detection using adaptable context analysis and key feature extraction. Comput. Secur. 104, 102218 (2021)","journal-title":"Comput. Secur."},{"key":"8_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2021.100357","volume":"13","author":"NM Phung","year":"2021","unstructured":"Phung, N.M., Mimura, M.: Detection of malicious javascript on an imbalanced dataset. Internet of Things 13, 100357 (2021)","journal-title":"Internet of Things"},{"issue":"3","key":"8_CR7","first-page":"139","volume":"9","author":"V Nithya","year":"2015","unstructured":"Nithya, V., Pandian, S.L., Malarvizhi, C.: A survey on detection and prevention of cross-site scripting attack. Int. J. Secur. Its Appl. 9(3), 139\u2013152 (2015)","journal-title":"Int. J. Secur. Its Appl."},{"key":"8_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114386","volume":"168","author":"I Tariq","year":"2021","unstructured":"Tariq, I., Sindhu, M.A., Abbasi, R.A., Khattak, A.S., Maqbool, O., Siddiqui, G.F.: Resolving cross-site scripting attacks through genetic algorithm and reinforcement learning. Expert Syst. Appl. 168, 114386 (2021)","journal-title":"Expert Syst. Appl."},{"key":"8_CR9","unstructured":"Jeitner, P., Shulman, H.: Injection attacks reloaded: tunnelling malicious payloads over DNS. In: 30th $$\\{$$USENIX$$\\}$$ Security Symposium ($$\\{$$USENIX$$\\}$$ Security 21), pp. 3165\u20133182 (2021)"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Kc, G.S., Keromytis, A.D., Prevelakis, V.: Countering code-injection attacks with instruction-set randomization. In: Proceedings of the 10th ACM conference on Computer and communications security, pp. 272\u2013280 (2003)","DOI":"10.1145\/948109.948146"},{"key":"8_CR11","unstructured":"Hazel, P.: Perl compatible regular expressions, The University of Cambridge, p. 114 (2012)"},{"key":"8_CR12","unstructured":"Erlacher, F., Dressler, F.: On high-speed flow-based intrusion detection using snort-compatible signatures. IEEE Trans. Dependable Secur. Comput"},{"key":"8_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/978-3-030-68887-5_14","volume-title":"Risks and Security of Internet and Systems","author":"OB Fredj","year":"2021","unstructured":"Fredj, O.B., Cheikhrouhou, O., Krichen, M., Hamam, H., Derhab, A.: An OWASP top ten driven survey on web application protection methods. In: Garcia-Alfaro, J., Leneutre, J., Cuppens, N., Yaich, R. (eds.) CRiSIS 2020. LNCS, vol. 12528, pp. 235\u2013252. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68887-5_14"},{"key":"8_CR14","unstructured":"Perl-compatible regular expressions (PCRE), http:\/\/www.pcre.org (2021)"},{"key":"8_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1007\/978-3-662-45237-0_61","volume-title":"Computer Information Systems and Industrial Management","author":"R Kozik","year":"2014","unstructured":"Kozik, R., Chora\u015b, M., Renk, R., Ho\u0142ubowicz, W.: A proposal of algorithm for web applications cyber attack detection. In: Saeed, K., Sn\u00e1\u0161el, V. (eds.) CISIM 2014. LNCS, vol. 8838, pp. 680\u2013687. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-45237-0_61"},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Sharma, S., Zavarsky, P., Butakov, S.: Machine learning based intrusion detection system for web-based attacks. In: 2020 IEEE 6th 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), IEEE, pp. 227\u2013230 (2020)","DOI":"10.1109\/BigDataSecurity-HPSC-IDS49724.2020.00048"},{"key":"8_CR17","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1007\/978-3-030-91738-8_44","volume-title":"Advances in Information, Communication and Cybersecurity","author":"C Oumaima","year":"2022","unstructured":"Oumaima, C., Abdeslam, R., Yassine, S., Abderrazek, F.: Experimental study on\u00a0the\u00a0effectiveness of\u00a0machine learning methods in\u00a0web intrusion detection. In: Maleh, Y., Alazab, M., Gherabi, N., Tawalbeh, L., Abd El-Latif, A.A. (eds.) ICI2C 2021. LNNS, vol. 357, pp. 486\u2013494. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-91738-8_44"},{"key":"8_CR18","doi-asserted-by":"crossref","unstructured":"J. Offutt, Y. Wu, X. Du, H. Huang, Bypass testing of web applications. In: 15th International Symposium on Software Reliability Engineering, IEEE, pp. 187\u2013197 (2004)","DOI":"10.1109\/ISSRE.2004.13"},{"key":"8_CR19","doi-asserted-by":"crossref","unstructured":"Sun, F., Zhang, P., White, J., Schmidt, D., Staples, J., Krause, L.: A feasibility study of autonomically detecting in-process cyber-attacks. In: 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), IEEE, pp. 1\u20138 (2017)","DOI":"10.1109\/CYBConf.2017.7985745"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Cova, M., Kruegel, C., Vigna, G.: Detection and analysis of drive-by-download attacks and malicious JavaScript code. In: Proceedings of the 19th international conference on World wide web, pp. 281\u2013290 (2010)","DOI":"10.1145\/1772690.1772720"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Pazos, J.C., L\u00e9gar\u00e9, J.-S., Beschastnikh, I.: XSnare: application-specific client-side cross-site scripting protection. In: 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), IEEE, pp. 154\u2013165 (2021)","DOI":"10.1109\/SANER50967.2021.00023"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Johns, M., Engelmann, B., Posegga, J., Xssds: Server-side detection of cross-site scripting attacks. In: Annual Computer Security Applications Conference (ACSAC). IEEE, vol. 2008, pp. 335\u2013344 (2008)","DOI":"10.1109\/ACSAC.2008.36"},{"key":"8_CR23","doi-asserted-by":"crossref","unstructured":"Fang, Y., Li, Y., Liu, L., Huang, C.: DeepXSS: cross site scripting detection based on deep learning. In: Proceedings of the 2018 International Conference on Computing and Artificial Intelligence, pp. 47\u201351 (2018)","DOI":"10.1145\/3194452.3194469"},{"key":"8_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2019.106960","volume":"166","author":"GE Rodr\u00edguez","year":"2020","unstructured":"Rodr\u00edguez, G.E., Torres, J.G., Flores, P., Benavides, D.E.: Cross-site scripting (XSS) attacks and mitigation: a survey. Comput. Netw. 166, 106960 (2020)","journal-title":"Comput. Netw."},{"key":"8_CR25","doi-asserted-by":"crossref","unstructured":"Kaur, G., Malik, Y., Samuel, H., Jaafar, F.: Detecting blind cross-site scripting attacks using machine learning. In: Proceedings of the 2018 International Conference on Signal Processing and Machine Learning, pp. 22\u201325 (2018)","DOI":"10.1145\/3297067.3297096"},{"key":"8_CR26","doi-asserted-by":"crossref","unstructured":"Kemalis, K., Tzouramanis, T.: SQL-IDS: a specification-based approach for SQL-injection detection. In: Proceedings of the 2008 ACM symposium on Applied computing, pp. 2153\u20132158 (2008)","DOI":"10.1145\/1363686.1364201"},{"issue":"4","key":"8_CR27","doi-asserted-by":"publisher","first-page":"1470","DOI":"10.1109\/TR.2019.2910285","volume":"68","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Zhang, D., Wang, C., Zhao, J., Zhang, Z.: ART4SQLI: the art of SQL injection vulnerability discovery. IEEE Trans. Reliab. 68(4), 1470\u20131489 (2019)","journal-title":"IEEE Trans. Reliab."},{"issue":"3","key":"8_CR28","doi-asserted-by":"publisher","first-page":"1168","DOI":"10.1109\/TR.2019.2900007","volume":"68","author":"I Medeiros","year":"2019","unstructured":"Medeiros, I., Beatriz, M., Neves, N., Correia, M.: SEPTIC: detecting injection attacks and vulnerabilities inside the DBMS. IEEE Trans. Reliab. 68(3), 1168\u20131188 (2019)","journal-title":"IEEE Trans. Reliab."},{"issue":"1","key":"8_CR29","first-page":"33","volume":"11","author":"OB Fredj","year":"2019","unstructured":"Fredj, O.B.: SPHERES: an efficient server-side web application protection system. Int. J. Inf. Comput. Secur. 11(1), 33\u201360 (2019)","journal-title":"Int. J. Inf. Comput. Secur."},{"issue":"2","key":"8_CR30","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1049\/sfw2.12018","volume":"15","author":"Z Zhuo","year":"2021","unstructured":"Zhuo, Z., Cai, T., Zhang, X., Lv, F.: Long short-term memory on abstract syntax tree for SQL injection detection. IET Softw. 15(2), 188\u2013197 (2021)","journal-title":"IET Softw."},{"key":"8_CR31","doi-asserted-by":"publisher","first-page":"145385","DOI":"10.1109\/ACCESS.2019.2944951","volume":"7","author":"Q Li","year":"2019","unstructured":"Li, Q., Li, W., Wang, J., Cheng, M.: A SQL injection detection method based on adaptive deep forest. IEEE Access 7, 145385\u2013145394 (2019)","journal-title":"IEEE Access"},{"issue":"1","key":"8_CR32","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1109\/TR.2019.2925415","volume":"69","author":"H Gu","year":"2019","unstructured":"Gu, H., et al.: DIAVA: a traffic-based framework for detection of SQL injection attacks and vulnerability analysis of leaked data. IEEE Trans. Reliab. 69(1), 188\u2013202 (2019)","journal-title":"IEEE Trans. Reliab."},{"key":"8_CR33","unstructured":"Batista, L.O.: Fuzzy neural networks to create an expert system for detecting attacks by SQL injection, arXiv preprint arXiv:1901.02868"},{"key":"8_CR34","doi-asserted-by":"crossref","unstructured":"Fang, Y., Peng, J., Liu, L., Huang, C.: WOVSQLI: detection of SQL injection behaviors using word vector and LSTM. In: Proceedings of the 2nd International Conference on Cryptography, Security and Privacy, pp. 170\u2013174 (2018)","DOI":"10.1145\/3199478.3199503"},{"key":"8_CR35","doi-asserted-by":"crossref","unstructured":"Liu, M., Li, K., Chen, T.: DeepSQLi: deep semantic learning for testing SQL injection. In: Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 286\u2013297 (2020)","DOI":"10.1145\/3395363.3397375"},{"key":"8_CR36","doi-asserted-by":"crossref","unstructured":"D. Chen, Q. Yan, C. Wu, J. Zhao, Sql injection attack detection and prevention techniques using deep learning. J. Phys. Conf. Series 1757, 012055 IOP Publishing (2021)","DOI":"10.1088\/1742-6596\/1757\/1\/012055"},{"key":"8_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-642-21323-6_4","volume-title":"Computational Intelligence in Security for Information Systems","author":"HT Nguyen","year":"2011","unstructured":"Nguyen, H.T., Torrano-Gimenez, C., Alvarez, G., Petrovi\u0107, S., Franke, K.: Application of the generic feature selection measure in detection of web attacks. In: Herrero, \u00c1., Corchado, E. (eds.) CISIS 2011. LNCS, vol. 6694, pp. 25\u201332. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21323-6_4"},{"key":"8_CR38","doi-asserted-by":"crossref","unstructured":"Yavanoglu, O., Aydos, M.: A review on cyber security datasets for machine learning algorithms. In: IEEE International Conference on Big Data (big data). IEEE, vol. 2017, pp. 2186\u20132193 (2017)","DOI":"10.1109\/BigData.2017.8258167"},{"key":"8_CR39","doi-asserted-by":"crossref","unstructured":"Kascheev, S., Olenchikova, T.: The detecting cross-site scripting (XSS) using machine learning methods. In: Global Smart Industry Conference (GloSIC). IEEE, vol. 2020, pp. 265\u2013270 (2020)","DOI":"10.1109\/GloSIC50886.2020.9267866"},{"key":"8_CR40","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1007\/978-3-319-74690-6_20","volume-title":"The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018)","author":"FA Mereani","year":"2018","unstructured":"Mereani, F.A., Howe, J.M.: Detecting cross-site scripting attacks using machine learning. In: Hassanien, A.E., Tolba, M.F., Elhoseny, M., Mostafa, M. (eds.) AMLTA 2018. AISC, vol. 723, pp. 200\u2013210. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-74690-6_20"},{"key":"8_CR41","unstructured":"Halfond, W.G., Viegas, J., Orso, A., et al.: A classification of SQL-injection attacks and countermeasures. In: Proceedings of the IEEE International Symposium on Secure Software Engineering, IEEE, vol. 1, pp. 13\u201315 (2006)"},{"issue":"2","key":"8_CR42","doi-asserted-by":"publisher","first-page":"88","DOI":"10.18201\/ijisae.2019252786","volume":"7","author":"MM Saritas","year":"2019","unstructured":"Saritas, M.M., Yasar, A.: Performance analysis of ANN and naive Bayes classification algorithm for data classification. Int. J. Intell. Syst. Appl. Eng. 7(2), 88\u201391 (2019)","journal-title":"Int. J. Intell. Syst. Appl. Eng."},{"key":"8_CR43","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/3-540-44795-4_16","volume-title":"Machine Learning: ECML 2001","author":"A Garg","year":"2001","unstructured":"Garg, A., Roth, D.: Understanding probabilistic classifiers. In: De Raedt, L., Flach, P. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 179\u2013191. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44795-4_16"},{"key":"8_CR44","doi-asserted-by":"crossref","unstructured":"Kulkarni, C.C., Kulkarni, S.: Human agent knowledge transfer applied to web security. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), IEEE, pp. 1\u20134 (2013)","DOI":"10.1109\/ICCCNT.2013.6726770"},{"key":"8_CR45","unstructured":"Zhang, H.: The optimality of naive Bayes. Aa 1(2), 3 (2004)"},{"issue":"6","key":"8_CR46","first-page":"275","volume":"18","author":"AJ Myles","year":"2004","unstructured":"Myles, A.J., Feudale, R.N., Liu, Y., Woody, N.A., Brown, S.D.: An introduction to decision tree modeling. A J. Chemom. Soc. 18(6), 275\u2013285 (2004)","journal-title":"A J. Chemom. Soc."},{"issue":"3","key":"8_CR47","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A., Wiener, M., et al.: Classification and regression by randomforest. R News 2(3), 18\u201322 (2002)","journal-title":"R News"},{"key":"8_CR48","unstructured":"Howe, J., Mereani, F.: Detecting cross-site scripting attacks using machine learning. In: Advances in Intelligent Systems and Computing 723"},{"key":"8_CR49","doi-asserted-by":"crossref","unstructured":"Zhang, Z.: Introduction to machine learning: k-nearest neighbors. Anna. Transl. Med. 4(11)","DOI":"10.21037\/atm.2016.03.37"},{"key":"8_CR50","doi-asserted-by":"crossref","unstructured":"Bhor, R., Khanuja, H.: Analysis of web application security mechanism and attack detection using vulnerability injection technique. In: 2016 International Conference on Computing Communication Control and automation (ICCUBEA), IEEE, pp. 1\u20136 (2016)","DOI":"10.1109\/ICCUBEA.2016.7860004"},{"key":"8_CR51","unstructured":"Jakkula, V.: Tutorial on support vector machine (SVM), School of EECS, Washington State University 37"},{"issue":"13","key":"8_CR52","first-page":"1","volume":"42","author":"R Rawat","year":"2012","unstructured":"Rawat, R., Shrivastav, S.K.: SQL injection attack detection using SVM. Int. J. Comput. Appl. 42(13), 1\u20134 (2012)","journal-title":"Int. J. Comput. Appl."},{"key":"8_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0027019","volume-title":"Artificial Neural Networks","year":"1995","unstructured":"Braspenning, P.J., Thuijsman, F., Weijters, A.J.M.M. (eds.): Neural Network School 1999. LNCS, vol. 931. Springer, Heidelberg (1995). https:\/\/doi.org\/10.1007\/BFb0027019"},{"key":"8_CR54","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.eswa.2017.07.005","volume":"88","author":"I Manzoor","year":"2017","unstructured":"Manzoor, I., Kumar, N., et al.: A feature reduced intrusion detection system using ANN classifier. Expert Syst. Appl. 88, 249\u2013257 (2017)","journal-title":"Expert Syst. Appl."},{"key":"8_CR55","unstructured":"CSIC 2010 Dataset, https:\/\/petescully.co.uk\/research\/csic-2010-http-dataset-in-csv-format-for-weka-analysis\/ (2021)"},{"key":"8_CR56","doi-asserted-by":"crossref","unstructured":"Bhatnagar, M., Rozinaj, G., Yadav, P.K.: Web intrusion classification system using machine learning approaches. In: International Symposium ELMAR. IEEE, vol. 2022, pp. 57\u201360 (2022)","DOI":"10.1109\/ELMAR55880.2022.9899790"},{"key":"8_CR57","doi-asserted-by":"publisher","unstructured":"Ramos J\u00fanior, L.S., Mac\u00eado, D., Oliveira, A.L.I., Zanchettin, C.: Detecting Malicious HTTP Requests Without Log Parser Using RequestBERT-BiLSTM. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. LNCS(), vol 13654 . Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21689-3_24","DOI":"10.1007\/978-3-031-21689-3_24"},{"key":"8_CR58","doi-asserted-by":"crossref","unstructured":"Ghazal, S.F., Mjlae, S.A.: Cybersecurity in deep learning techniques: Detecting network attacks. Int. J. Adv. Comput. Sci. Appl. 13(11)","DOI":"10.14569\/IJACSA.2022.0131125"},{"key":"8_CR59","doi-asserted-by":"publisher","unstructured":"Li, W., Zhang, X.Y.: GBLNet: Detecting Intrusion Traffic with Multi-granularity BiLSTM. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science \u2013 ICCS 2022. ICCS 2022. LNCS, vol 13353. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-08760-8_32","DOI":"10.1007\/978-3-031-08760-8_32"},{"key":"8_CR60","doi-asserted-by":"crossref","unstructured":"Tan, S., Sun, R., Liang, Z.: Detection of malicious web requests using neural networks with multi granularity features. In: Proceedings of the 5th International Conference on Big Data Technologies, pp. 83\u201389 (2022)","DOI":"10.1145\/3565291.3565304"},{"key":"8_CR61","doi-asserted-by":"crossref","unstructured":"Shaheed, A., Kurdy, M.: Web application firewall using machine learning and features engineering, Secur. Commun. Netw. (2022)","DOI":"10.1155\/2022\/5280158"},{"issue":"2","key":"8_CR62","first-page":"219","volume":"6","author":"S Toprak","year":"2022","unstructured":"Toprak, S., Yavuz, A.G.: Web application firewall based on anomaly detection using deep learning. Acta Infologica 6(2), 219\u2013244 (2022)","journal-title":"Acta Infologica"},{"key":"8_CR63","doi-asserted-by":"crossref","unstructured":"J. J. Davis, A. J. Clark, Data preprocessing for anomaly based network intrusion detection: a review. Comput. Secur. 30(6\u20137), 353\u2013375 (2011)","DOI":"10.1016\/j.cose.2011.05.008"},{"issue":"2","key":"8_CR64","first-page":"111","volume":"1","author":"SB Kotsiantis","year":"2006","unstructured":"Kotsiantis, S.B., Kanellopoulos, D., Pintelas, P.E.: Data preprocessing for supervised leaning. Int. J. Comput. Sci. 1(2), 111\u2013117 (2006)","journal-title":"Int. J. Comput. Sci."},{"key":"8_CR65","unstructured":"Performance metrics, https:\/\/towardsdatascience.com\/20-popular-machine-learning-metrics-part-1-classification-regression-evaluation-metrics1ca3e282a2ce (2021)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Future Access Enablers for Ubiquitous and Intelligent Infrastructures"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50051-0_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T22:43:33Z","timestamp":1730846613000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50051-0_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,15]]},"ISBN":["9783031500503","9783031500510"],"references-count":65,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50051-0_8","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2023,12,15]]},"assertion":[{"value":"15 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FABULOUS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fabulous2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/fabulous-conf.eai-conferences.org\/2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Confy +","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}