Advanced Persistent Threats (APTs) detection in the realm of cybersecurity

R2D2 Partners S2Group has made an interesting publication to demostrate how they are appliying AI for APT detection within the frame of the R2D2 (Reliability, Resilience, and Defense technology for the grid) project, examining both current challenges and implicit benefits. S2Group analyzes the distinctive characteristics of APTs, the obstacles in their detection, and how AI- based results can overcome these challenges.

To achieve their goals, APTs use highly sophisticated tactics such as custom malware, exploitation of unknown vulnerabilities, and advanced social engineering techniques. Understanding and defending against these threats is essential, and the MITRE ATT&CK framework is crucial as it details the different phases of the cyberattack lifecycle, from reconnaissance to evasion.

In response to this challenge, the use of artificial intelligence (AI) is proposed as an effective solution to improve the detection of APTs. AI excels in its ability to analyze large volumes of data and detect anomalous patterns in real time, thus overcoming the limitations of traditional methods based on signatures and static rules. Unlike these conventional approaches, AI can adapt and learn from new threats, offering more dynamic and effective detection.

AI for APTs detection

In R2D2 project, S2Group is committed to integrating AI into its Carmen tool for APT detection. This process is developed in two main phases.

First, Natural Language Processing (NLP) techniques are used to project threat tactics and techniques into an N-dimensional space. This allows the alignment of historical threat intelligence collected by the Threat Intelligence team with the real-time results produced by Carmen’s analyzers. By representing this data in a common space, it is possible to measure the distances and relationships between them, identifying potential correlations that alert to the presence of an APT.

In the second phase, all previously generated alerts are correlated using an algorithm that calculates the risk probability of an APT. This approach allows for a more accurate and rapid assessment of the situation, increasing detection precision and enabling early identification of threats before significant damage occurs to the organization.

In summary, the combination of AI, NLP, and a threat intelligence database in the R2D2 project represents a significant advancement towards more proactive and robust cybersecurity. This innovative approach enhances the ability of digital infrastructures to withstand and mitigate the most advanced threats, ensuring resilience against APTs.

Further information:

This project has received funding from the  European Union’s Horizon Europe research and innovation programme under grant agreement No 101075714.