Description du projet
Protéger les secteurs essentiels contre les cyberattaques
La numérisation a créé des opportunités pour les secteurs essentiels comme les transports, l’énergie, la santé et la finance qui dépendent des technologies numériques. Mais chaque opportunité s’accompagne d’un risque. Dans ce cas, les cyberattaques et la cybercriminalité posent problème. C’est pourquoi l’UE travaille sur différents fronts pour promouvoir la cyber-résilience et lutter contre la cybercriminalité. Dans ce contexte, le projet OPTIMA, financé par l’UE, concevra des techniques et des outils pour l’extraction de renseignements sur les menaces (données collectées, traitées et analysées pour comprendre les motivations et les cibles d’un acteur menaçant) en utilisant des algorithmes d’apprentissage automatique. Le projet examinera des manières de préparer les renseignements exploitables sur les menaces et de les partager sans divulguer d’informations confidentielles.
Objectif
The OPTIMA project (Organization sPecific Threat Intelligence Mining and sharing) aims to design techniques and tools for the extraction of Threat Intelligence targeted to organizations using ML algorithms, and effectively share attack records using privacy-preserving methods. The project will use technologies to protect societies from cyber-attacks and sophisticated threats prioritized in the European Council’s New Strategic Agenda. The key beneficiaries of the project are (a) security operation center-to support real time monitoring (b) incident response, threat hunting, fraud detection team-to prioritize risk (c), operational leaders- to prioritize activities of IT staff and (d) Strategic leaders such as Chief Information Security Officers - to make well-informed business decisions. This project will be executed at the University of Padua, under the supervision of Prof. Mauro Conti. The project will investigate solutions for the core questions: RQ1: How effectively can ML algorithms extract organization-specific threat artefacts to be utilized for preparing actionable Threat Intelligence? RQ2: How can organizations share threat intelligence without disclosing their private information to others?
The objectives (SO) of the project are as follows:
1. SO1-To develop techniques for automatic extraction of threat intelligence using OSINT data for diverse IT industries (health care, finance, IoT, education, etc.) using deep learning approaches.
2. SO2-To create a novel automated system to derive Indicator of Compromise (IOC) based on word embedding and syntactic dependencies of words to identify unseen IOCs. Utilizing the extracted IOCs a threat index will be estimated to define the impact of threat and attack trends across individual organizations;
3. SO3-To build a system by integrating cryptographic tools and Federated learning which will enable an organization to anonymously share threat logs with different parties in a privacy-preserving manner
Champ scientifique
- natural sciencescomputer and information sciencesinternetinternet of things
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systems
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
Mots‑clés
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinateur
35122 Padova
Italie