Laboratory for vulnerability testing of desktop and mobile computing devices and algorithms and software (LAVA)

The project aims to address vulnerabilities to prevent cybercriminals from engaging in illegal activities or organizing acts of cyberterrorism, particularly those involving the use mobile devices. The focus is on protecting users in the PEGI 12, PEGI 16, and PEGI 18 age groups, who are particularly vulnerable to these threats.

The main goal of the project is to build a vulnerability analysis laboratory equipped with:

• experimental laboratory station for vulnerability testing of devices and software,
• vulnerability scanning technologies for IT systems making use of innovative methods developed during the project,
• tools for static application analysis and behavioral analysis.

The project will develop innovative methods for testing devices and applications using artificial intelligence and machine learning techniques. Its important aspect is automating of software testing and creating appropriate procedures and methodologies. One of the outcomes of the LaVa project is the development of consistent methodology for assessing the security level of IoT devices. As part of the project, we are also conducting a detailed review of existing tools and verifying their suitability in the context of performing tests on IoT devices. . Based on this analysis, we are building a coherent toolset—comprising of software solutions—designed specifically for conducting security tests on IoT devices.

In the realm of artificial intelligence security, we are developing a universal methodology for assessing the vulnerability of predictive models, enabling consistent comparisons across them. Our efforts are focused on cyber-physical systems, including robotics, industrial automation, as well as on cybersecurity.

To facilitate testing mobile devices and applications, the project is creating an environment for dynamic long-term analysis of mobile applications under near-real conditions – an isolated, secure testing environment and a repository of behavioral models describing how mobile applications perform under varying conditions.

Vulnerability testing of mobile applications will take into account various application usage profiles, considering factors like device movement, location changes, resource sharing with other applications, network connections, different versions of operating system and software, specific to major mobile device manufacturers.