An Auto-tuning Sanitizing System for Mitigating Injection Flaws

Jan-Min Chen

《International Journal of Network Security》 17卷2期 (2015/03) Pp. 189-198

https://doi.org/10.6633/IJNS.201503.17(2).11

並列摘要

Injection attacks are dangerous and ubiquitous, contributing enormously to some of the most elaborate Web hacks. Enforcing proper input validation is an effective countermeasure to improve injection flaws. Unless a web application has a strong, centralized mechanism for validating all input from HTTP requests, injection flaws are very likely to exist. However, improper constraining rules may induce some detection error. False negatives may render security risks and false positives will cause improper limits of input characters. In this paper, we design an auto-tuning system to help validating input for each vulnerable injection point. A proper validation rule can be automatically generated through an auto-tuning mechanism. The experimental results show that the system can effectively protect against injection attacks and lower false positives while compared with traditional methods.

並列關鍵字

Constraining rule ； content filtering ； detection accuracy ； injection flaws ； input validation