Biblio

In last decades' web application security has become one of the most important case study of information security studies. Business processes are transferred to web platforms. So web application usage is increased very fast. Web-based attacks have also increased due to the increased use of web applications. In order to ensure the security of web applications, intrusion detection and prevention systems and web application firewalls are used against web based attacks. Blockchain technology, which has become popular in recent years, enables reliable and transparent sharing of data with all stakeholders. In this study, in order to detect web-based attacks, a blockchain based web attack detection model that uses the signature based detection method is proposed. The signature based detection refers to the detection of attacks by looking for specific patterns against known web based attack types, such as Structured Query Language (SQL) Injection, Cross Site Scripting (XSS), Command Injection. Three web servers were used for the experimental study. A blockchain node has been installed with the MultiChain application for each server. Attacks on web applications are detected using the signature list found in the web application as well as detected using the signature list updated on the blockchain. According to the experimental results, the attacks signature detected and defined by a web application are updated in the blockchain lists and used by all web applications.

Testing and fixing Web Application Firewalls (WAFs) are two relevant and complementary challenges for security analysts. Automated testing helps to cost-effectively detect vulnerabilities in a WAF by generating effective test cases, i.e., attacks. Once vulnerabilities have been identified, the WAF needs to be fixed by augmenting its rule set to filter attacks without blocking legitimate requests. However, existing research suggests that rule sets are very difficult to understand and too complex to be manually fixed. In this paper, we formalise the problem of fixing vulnerable WAFs as a combinatorial optimisation problem. To solve it, we propose an automated approach that combines machine learning with multi-objective genetic algorithms. Given a set of legitimate requests and bypassing SQL injection attacks, our approach automatically infers regular expressions that, when added to the WAF's rule set, prevent many attacks while letting legitimate requests go through. Our empirical evaluation based on both open-source and proprietary WAFs shows that the generated filter rules are effective at blocking previously identified and successful SQL injection attacks (recall between 54.6% and 98.3%), while triggering in most cases no or few false positives (false positive rate between 0% and 2%).

Finding differences between programs with similar functionality is an important security problem as such differences can be used for fingerprinting or creating evasion attacks against security software like Web Application Firewalls (WAFs) which are designed to detect malicious inputs to web applications. In this paper, we present SFADIFF, a black-box differential testing framework based on Symbolic Finite Automata (SFA) learning. SFADIFF can automatically find differences between a set of programs with comparable functionality. Unlike existing differential testing techniques, instead of searching for each difference individually, SFADIFF infers SFA models of the target programs using black-box queries and systematically enumerates the differences between the inferred SFA models. All differences between the inferred models are checked against the corresponding programs. Any difference between the models, that does not result in a difference between the corresponding programs, is used as a counterexample for further refinement of the inferred models. SFADIFF's model-based approach, unlike existing differential testing tools, also support fully automated root cause analysis in a domain-independent manner. We evaluate SFADIFF in three different settings for finding discrepancies between: (i) three TCP implementations, (ii) four WAFs, and (iii) HTML/JavaScript parsing implementations in WAFs and web browsers. Our results demonstrate that SFADIFF is able to identify and enumerate the differences systematically and efficiently in all these settings. We show that SFADIFF is able to find differences not only between different WAFs but also between different versions of the same WAF. SFADIFF is also able to discover three previously-unknown differences between the HTML/JavaScript parsers of two popular WAFs (PHPIDS 0.7 and Expose 2.4.0) and the corresponding parsers of Google Chrome, Firefox, Safari, and Internet Explorer. We confirm that all these differences can be used to evade the WAFs and launch successful cross-site scripting attacks.