Abstract
As web applications become increasingly complex and connected, it becomes imperative to reduce the vulnerabilities in applications. SQLIA is a part of OWASP vulnerabilities and it is extremely important to prevent them. The proposed system aims to predict the occurrence of SQLIA on a given server, with applications deployed on it, from a given source, at a particular time. This prediction can be done with the help of JMeter tool. Apache JMeter is used to simulate logs data. From this, one can pre-process, extract features, and classify, which is then fed to a model for prediction of SQLIA.
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References
Scholte, T., Robertson, W., Balzarotti, D., Kirda., E.: An empirical analysis of input validation mechanisms in web applications and languages. In: 27th Annual ACM Symposium on Applied Computing, pp. 1419–1426 (2012)
Alkhalaf, M., Aydin, A., Bultan, T.: Semantic differential repair for input validation and sanitization. In: ACM International Symposium on Software Testing and Analysis, pp. 225–236 (2014)
Frajták, K., Bureš, M., Jelínek, I.: Reducing user input validation code in web applications using Pex extension. In: ACM 15th International Conference on Computer Systems and Technologies, pp. 302–308 (2014)
Li, X., Xue, Y.: A survey on server-side approaches to securing web applications. ACM Comput. Surv. (CSUR) 46(4), 54:1–54:29 (2014)
Cho, S., Choi, J., Kim, G., Park, M., Cho, S., Han, S.: Runtime input validation for Java web applications using static bytecode instrumentation. In: ACM International Conference on Research in Adaptive and Convergent Systems, pp. 148–152 (2016)
Medeiros, I., Neves, N.F., Correia, M.: Automatic detection and correction of web application vulnerabilities using data mining to predict false positives. In: ACM 23rd International Conference on World Wide Web, pp. 63–73 (2014)
Shar, L.K., Tan, H.B.K.: Predicting common web application vulnerabilities from input validation and sanitization code patterns. In: 27th IEEE/ACM International Conference on Automated Software Engineering, pp. 310–313 (2012)
Shar, L.K., Tan, H.B.K.: Mining input sanitization patterns for predicting SQL injection and cross site scripting vulnerabilities. In: 34th International Conference on Software Engineering, pp. 1293–1296 (2012)
Solomon, O.U., William, J.B., Lu, F.: Applied machine learning predictive analytics to SQL injection attack detection and prevention. In: IFIP/IEEE IM 2017 Workshop: 3rd International Workshop on Security for Emerging Distributed Network Technologies, pp. 1087–1090 (2017)
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. 170–174 (2018)
Haldar, V., Chandra, D., Franz, M.: Dynamic taint propagation for Java. In: Annual Computer Security Applications Conference, pp. 09–15 (2005)
Halfond, W.G.J., Orso, A.: AMNESIA analysis and monitoring for neutralizing SQL-injection attacks. In: Proceedings of IEEE and ACM International Conference on Automatic Software Engineering, Long Beach, CA, USA, pp. 54–59 (2005)
Komiya, R., Paik, I., Hisada, M.: Classification of malicious web code by machine learning. In: 3rd International Conference on Awareness Science and Technology (iCAST), pp. 406–411 (2011)
Jingling, Z., Junxin, Q., Liang, Z., Baojiang, C.: Dynamic taint tracking of Web application based on static code analysis. In: 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 96–101 (2016)
Kumar, S., Mahajan, R., Kumar, N., Khatri, S.K.: A study on web application security and detecting security vulnerabilities. In: 6th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO), pp. 451–455 (2017)
Jane, P.Y., Chaudhari, M.S.: SQLIA: detection and prevention techniques: a survey. IOSR J. Comput. Eng. (IOSR-JCE) 2, 56–60 (2009). Second International Conference on Emerging Trends in Engineering (SICETE)
Peng, C.J., Lee, K.L., Ingersoll, G.M.: An introduction to logistic regression analysis and reporting. J. Educ. Res. 96, 3–14 (2002)
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Arumugam, C. et al. (2019). Prediction of SQL Injection Attacks in Web Applications. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_37
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DOI: https://doi.org/10.1007/978-3-030-24305-0_37
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