SQL injection attack detection in network flow data

被引:15
|
作者
Crespo-Martinez, Ignacio Samuel [1 ]
Campazas-Vega, Adrian [2 ]
Guerrero-Higueras, Angel Manuel [2 ]
Riego-DelCastillo, Virginia [2 ]
Alvarez-Aparicio, Claudia [2 ]
Fernandez-Llamas, Camino [2 ]
机构
[1] Supercomp Castilla & Leon SCAYLE, Campus Vegazana S-N, Leon 24071, Spain
[2] Univ Leon, Robot Grp, Campus Vegazana S-N, Leon 24071, Spain
关键词
Ensamble learning; Machine learning; Netflow; Network security; SQLIA detection; AGREEMENT;
D O I
10.1016/j.cose.2023.103093
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
SQL injections rank in the OWASP Top 3. The literature shows that analyzing network datagrams allows for detecting or preventing such attacks. Unfortunately, such detection usually implies studying all pack-ets flowing in a computer network. Therefore, routers in charge of routing significant traffic loads usually cannot apply the solutions proposed in the literature. This work demonstrates that detecting SQL in-jection attacks on flow data from lightweight protocols is possible. For this purpose, we gathered two datasets collecting flow data from several SQL injection attacks on the most popular database engines. After evaluating several machine learning-based algorithms, we get a detection rate of over 97% with a false alarm rate of less than 0.07% with a Logistic Regression-based model.(c) 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
引用
收藏
页数:11
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