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RESCUE

Cybersecurity

Countermeasures development for automated Bot attacks on critical services.

The Cybersecurity component of RESCUE allows for the development of novel countermeasures for automated Bot attack scenarios on critical services such as airport departure control systems.

Through Machine Learning, a next generation of cyber threat intelligence analysis is created. With the added benefit of data mining, a better situational awareness of the behaviour of threat actors is obtained.

Data sources contained within the Open Observability Platform allow for the detection of cyberattacks and exploits of functional vulnerabilities in the aviation and travel industry.

Through automated detonation and behavioural analysis in hermetically sealed containers, anti-malware techniques can be validated. Through all of this, a complete picture of the global malware landscape (with a focus on ransomware) comes into view.

Components

In order to improve the detection of Botnets and develop new countermeasures, new ways must be found to identify residential IP proxies and to distinguish them from other types of proxies that break the TCP protocol.

The same applies to the detection of CAPTCHA farms. As soon as a reliable distinction between Bots and real users is possible, Bots should be redirected to customized honeypots and thus contained.

Developing structures and procedures for gaining cyber threat intelligence to gain situational awareness of threat actor behaviours and strategies and to stay one step ahead of new attackers’ tools, tactics & procedures (TTP).

Extending capabilities to detect functional abuse and business logic attacks, such as seat spinning (also known as denial-of-inventory) or SMS pumping and developing countermeasures.

Develop comprehensive situational awareness of the current global malware landscape and validate anti-malware capabilities by detonating malware in a controlled environment and analysing behaviour.

Work‑package resources

Automated Fraud Investigation and Reporting system

This project implements an advanced automated system for fraud investigation and reporting using Large Language Models (LLMs) and machine learning techniques. The system is designed to process incidents, analyze logs, detect anomalies, generate comprehensive reports, and continuously improve its performance.

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