Tiago Dias

Tiago Dias, Eva Maia, Isabel Praça

Software's pervasive impact and increasing reliance in the era of digital transformation raise concerns about vulnerabilities, emphasizing the need for software security. Fuzzy testing is a dynamic analysis software testing technique that consists of feeding faulty input data to a System Under Test (SUT) and observing its behavior. Specifically regarding black-box RESTful API testing, recent literature has attempted to automate this technique using heuristics to perform the input search and using the HTTP response status codes for classification. However, most approaches do not keep track of code coverage, which is important to validate the solution. This work introduces a black-box RESTful API fuzzy testing tool that employs Reinforcement Learning (RL) for vulnerability detection. The fuzzer operates via the OpenAPI Specification (OAS) file and a scenarios file, which includes information to communicate with the SUT and the sequences of functionalities to test, respectively. To evaluate its effectiveness, the tool was tested on the Petstore API. The tool found a total of six unique vulnerabilities and achieved 55\% code coverage.

Tiago Dias, Nuno Oliveira, Norberto Sousa et al.

Cybersecurity has been a concern for quite a while now. In the latest years, cyberattacks have been increasing in size and complexity, fueled by significant advances in technology. Nowadays, there is an unavoidable necessity of protecting systems and data crucial for business continuity. Hence, many intrusion detection systems have been created in an attempt to mitigate these threats and contribute to a timelier detection. This work proposes an interpretable and explainable hybrid intrusion detection system, which makes use of artificial intelligence methods to achieve better and more long-lasting security. The system combines experts' written rules and dynamic knowledge continuously generated by a decision tree algorithm as new shreds of evidence emerge from network activity.