As the AIAS project progresses, we are excited to share some of our core research, technical, and innovation objectives that will drive our efforts toward securing AI systems and enhancing organizational resilience against adversarial threats.
- Development of the AIAS Security Platform: A central objective of the AIAS project is the conceptualization and development of an advanced security platform designed to strengthen the cybersecurity posture of Medium and Small-Medium Enterprises (MEs/SMEs). This innovative platform will leverage cutting-edge technologies such as life-long reinforcement learning, digital twins, and virtual personas. The AIAS platform is set to play a dual role: employing AI techniques to safeguard organizations against cyberattacks while simultaneously developing novel methodologies to protect AI systems from adversarial AI threats.
Key Components of the AIAS Platform:
- Deception Layer: Incorporating high-interaction honeypots, digital twins, and virtual personas to simulate an organization’s ecosystem and mislead potential attackers.
- Adversarial AI Engine Module: Generating and simulating cyberattacks, including adversarial AI attacks and common cyber threats like DDoS and ransomware, to assess system robustness.
- AI-based Detection Module: Utilizing reinforcement learning techniques to enhance the detection of cyberattacks across an organization’s systems and processes.
- Mitigation Module: Integrating game-theory and AI-driven decision-making models to propose effective mitigation strategies. This module will adopt a human-in-the-loop approach, empowering security professionals with user-friendly interfaces and Explainable AI (XAI) solutions to support informed decision-making during cybersecurity incidents.
- Exploiting Deep Neural Networks and Attack Graphs for Adversarial AI Defense: The AIAS project will harness the power of Deep Neural Network (DNN) models in combination with Attack Graphs, constructed based on the network configuration, hardware, and software assets of MEs/SMEs. This approach aims to develop a pioneering adversarial AI engine capable of identifying vulnerabilities in AI systems.
Core Activities:
- Adversarial AI Engine: This engine will simulate attack scenarios specifically targeting AI systems to uncover security gaps and vulnerabilities. The collected data will inform the development and retraining of AI defense models tailored to the unique operational environments of organizations.
- Taxonomy of Adversarial AI Attacks: A comprehensive taxonomy will be conducted to classify adversarial AI attacks, analyze potential attack vectors, and formalize the exploitable weaknesses within AI systems. Each vulnerability will be assessed based on its risk, calculated as a function of attack probability and impact on an organization’s security and privacy.
- Adversarial AI Attack Scenarios: Combining the taxonomy outcomes with system-specific parameters, such as training data characteristics, algorithm types (e.g., tree-based, deep neural networks, Bayesian), and hyperparameters, AIAS will design realistic attack scenarios. These scenarios will be instrumental in identifying security vulnerabilities and supporting the proactive defense strategies that underpin the AIAS platform.
Through these objectives, AIAS aims to push the boundaries of cybersecurity innovation by establishing robust defenses for AI systems while enhancing the overall security posture of organizations. Stay informed as we continue to advance towards creating a resilient digital environment.
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