Sharif Digital Repository

Due to increasing web-based attacks against web applications and inefficiency of intrusion detection and prevention systems for detecting and preventing web attacks in the application layer, web application firewalls (WAF) developed to deal with this problem. There are most common attacks affecting today’s web applications like SQL Injection (SQLi), Cross-Site Scripting (XSS) and Logical attacks. The Logical attack focuses on the abuse or exploitation of a web application’s logic flow, and unlike SQLi and XSS, it depends on the web application functionality. Nowadays, there are a lot of methods for designing a WAF which are divided into two categories: Signature-based and Anomaly-based... 

With the growing deployment of host and network intrusion detection systems, analyzing generated alerts from these systems becomes critically important and challenging due to its complexity and high amount of data. A perfect intrusion detection system would be able to identify all the attacks without raising any false and non-relevant alarms. Unfortunately, false alarms are commonplace in intrusion detection systems. Non-relevant alerts, which are associated with attacks that were not successful, are also common. The process of identifying false and non-relevant alerts is called alert verification. Also nowadays, web applications are widely used in critical and important roles (e.g.,... 

One of the most important requirements in deploying a security system is to ensure the effectiveness and absence of bypass patterns. This is especially important for attack-based detection systems. One of the systems that has recently attracted the attention of network administrators is Web Application Firewall (WAF). The purpose of this thesis is to propose a deep learning approach to identify the pattern of SQL Injection (SQLi) attacks which could potentially bypass a WAF. We delve into the problem of detecting SQLi attacks among a very large dataset of existing SQL queries. To this end, we use one of the latest implementation of Recurrent Neural Network (RNN) called Long Short-Term Memory...