Secure Intelligent Methods for Advanced RecoGnition of malware and stegomalware - SIMARGL (DIT.AD006.047)
Project areaCyberSecurity (DIT.AD006)
Structure responsible for the research project
With the prevailing risk of cybersecurity breaches, improving the cyber security posture and detection algorithms is of utmost importance. Malware is now recognized as the severe threat for commercial and critical IT systems (e.g., financial sector) , but also for citizens (e.g., mobile malware). Still, currently malware is well understood and can be tackled reasonably well. What is becoming more problematic, is the stegomalware and the use of the information hiding techniques by cyber criminals. And here comes SIMARGL: our goal is to focus on this emerging future threat and to significantly improve malware and stegomalware detection. Currently, cyber criminals use quite simple information hiding techniques, but they learn and improve quickly. Our consortium believes that we cannot stay many steps behind, but provide relevant techniques to be prepared for the future attacks and stegomalwre. SIMARGL consortium does not start from scratch (current solutions are described in the proposal) and it features relevant partners, expertise and links to fulfill the project goals.
The main objectives of SIMARGL, especially for IMATI - CNR are:
- develop innovative indicators to detect covert channels and hidden, malicious communications within network traffic;
- engineer efficient and scalable detection techniques leveraging machine learning;
- define a new framework to mitigate the impact of steganographic malware.
Start date of activity
information hiding, steganography, malware
Last update: 11/12/2023