R2D2 Project: applying AI for APT detection

In the dynamic and ever-changing landscape of cybersecurity, Advanced Persistent Threats (APTs) stand out as one of the most significant challenges. These threats, characterized by their sophistication and ability to evade traditional defences, can infiltrate corporate and government networks, remaining undetected for extended periods. Effective detection of APTs is therefore a critical priority to protect the integrity and confidentiality of information.

APTs are characterized by their high sophistication and persistence in target systems. Attackers, often backed by significant resources, employ complex tactics to gain unauthorized access to systems and extract sensitive data or cause prolonged damage. These tactics include the use of custom malware, exploitation of unknown vulnerabilities (zero-day), and advanced social engineering techniques. On the defensive side, the MITRE ATT&CK framework provides a comprehensive structure detailing the various methods and techniques attackers use at each stage of the cyberattack lifecycle, allowing for a better understanding and defence against these advanced threats.

In this context, artificial intelligence (AI) emerges as a promising tool to enhance APT detection capabilities. AI, with its ability to analyse vast amounts of data and detect anomalous patterns, offers an advanced solution against traditional cybersecurity techniques. Unlike conventional methods, which often rely on predefined signatures and static rules, AI can adapt and learn from new threats in real-time.

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