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Early Detection of SQL Injection Attacks

Authors: Sarah M. North; Wei-Chuen Chen; Hossain Shahriar;

Early Detection of SQL Injection Attacks

Abstract

SQL Injection (SQLI) is a common vulnerability found in web applications. The starting point of SQLI attack is the client-side (browser). If attack inputs can be detected early at the browse side, then it could be thwarted early by not forwarding the malicious inputs to the server-side for further processing. This paper presents a client-side approach to detect SQLI attacks1 . The client-side accepts shadow SQL queries from the server-side and checks any deviation between shadow queries with dynamic queries generated with user supplied inputs. We measure the deviation of shadow query and dynamic query based on conditional entropy metrics and propose four metrics in this direction. We evaluate the approach with three PHP applications containing SQLI vulnerabilities. The evaluation results indicate that our approach can detect well-known SQLI attacks early at the client-side and impose negligible overhead.

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Keywords

SQL Injection, Information theory, Web security, Conditional entropy

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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