Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 7th International Conference on Security of Information and Networks |
| Editors | Ron Poet |
| Place of Publication | New York |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 318-323 |
| Number of pages | 6 |
| ISBN (Print) | 9781450330336 |
| DOIs | |
| Publication status | Published - 9 Sep 2014 |
| Event | 7th International Conference on Security of Information and Networks - University of Glasgow, Glasgow, United Kingdom Duration: 9 Sep 2014 → 11 Sep 2014 Conference number: 7th |
Conference
| Conference | 7th International Conference on Security of Information and Networks |
|---|---|
| Abbreviated title | SIN 2014 |
| Country | United Kingdom |
| City | Glasgow |
| Period | 9/09/14 → 11/09/14 |
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Employing Neural Networks for the detection of SQL injection attack. / Moradpoor Sheykhkanloo, Naghmeh.
Proceedings of the 7th International Conference on Security of Information and Networks. ed. / Ron Poet. New York : Association for Computing Machinery (ACM), 2014. p. 318-323.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Employing Neural Networks for the detection of SQL injection attack
AU - Moradpoor Sheykhkanloo, Naghmeh
PY - 2014/9/9
Y1 - 2014/9/9
N2 - Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into the SQL database by simply using web browsers. SQLI attack can cause severe damages on a given SQL database such as losing data, disclosing confidential information or even changing the values of data. It has also been rated as the number-one attack on the Open Web Application Security Project (OWASP) top ten. In this paper, we propose an effective model to deal with this problem based on Neural Networks (NNs). The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to classify the generated URLs to either benign or malicious URLs, and an NN model in order to detect either a given URL is a malicious URL or a benign URL. The model is first trained and then evaluated by employing both benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.
AB - Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into the SQL database by simply using web browsers. SQLI attack can cause severe damages on a given SQL database such as losing data, disclosing confidential information or even changing the values of data. It has also been rated as the number-one attack on the Open Web Application Security Project (OWASP) top ten. In this paper, we propose an effective model to deal with this problem based on Neural Networks (NNs). The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to classify the generated URLs to either benign or malicious URLs, and an NN model in order to detect either a given URL is a malicious URL or a benign URL. The model is first trained and then evaluated by employing both benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.
U2 - 10.1145/2659651.2659675
DO - 10.1145/2659651.2659675
M3 - Conference contribution
SN - 9781450330336
SP - 318
EP - 323
BT - Proceedings of the 7th International Conference on Security of Information and Networks
A2 - Poet, Ron
PB - Association for Computing Machinery (ACM)
CY - New York
ER -