Biblio

Artificial neural networks in general and deep learning networks in particular established themselves as popular and powerful machine learning algorithms. While the often tremendous sizes of these networks are beneficial when solving complex tasks, the tremendous number of parameters also causes such networks to be vulnerable to malicious behavior such as adversarial perturbations. These perturbations can change a model's classification decision. Moreover, while single-step adversaries can easily be transferred from network to network, the transfer of more powerful multi-step adversaries has - usually - been rather difficult.In this work, we introduce a method for generating strong adversaries that can easily (and frequently) be transferred between different models. This method is then used to generate a large set of adversaries, based on which the effects of selected defense methods are experimentally assessed. At last, we introduce a novel, simple, yet effective approach to enhance the resilience of neural networks against adversaries and benchmark it against established defense methods. In contrast to the already existing methods, our proposed defense approach is much more efficient as it only requires a single additional forward-pass to achieve comparable performance results.

Modern computing networks that enable distributed computing are comprised of a wide range of heterogeneous devices with different levels of resources, which are interconnected by different networking technologies and communication protocols. This integration, together with the state of the art technologies, has brought into play new uncertainties, associated with physical world and the cyber space. In heterogeneous networked control systems environments, awareness and resilience are two important properties that these systems should bear and comply with. In this work the problem of resilience enhancement in heterogeneous networked control systems is addressed based on a distributed middleware, which is propped up on a hierarchical multi-agent framework, where each of the constituent agents is devoted to a specific task. The proposed architecture takes into account physical and cyber vulnerabilities and ensures state and context awareness, and a minimum level of acceptable operational performance, in response to physical and cyber disturbances. Experiments on a IPv6-based test-bed proved the relevance and benefits offered by the proposed architecture.

Advances on the integration of wireless sensor and actuator networks, as a whole, have contribute to the greater reconfigurability of systems and lower installation costs with application to supervision of networked control systems. This integration, however, increases some vulnerabilities associated with the physical world and also with the cyber and security world. This trend makes the wireless nodes one of the most vulnerable component of these kind of systems, which can have a major impact on the overall performance of the networked control system. This paper presents an architecture relying on a hierarchical multi-agent system for resilience enhancement, with focus on wireless sensor and actuator networks. The proposed framework was evaluated on an IPv6 test-bed comprising several distributed devices, where performance and communication links health are analyzed. The relevance of the proposed approach is demonstrated by results collected from the test-bed.

Cyber-physical systems are an important component of most industrial infrastructures that allow the integration of control systems with state of the art information technologies. These systems aggregate distinct communication platforms and networked devices with different capabilities. This integration, has brought into play new uncertainties, not only from the tangible physical world, but also from a cyber space perspective. In light of this situation, awareness and resilience are invaluable properties of these kind of systems. The present work proposes an architecture based on a distributed middleware that relying on a hierarchical multi-agent framework for resilience enhancement. The proposed architecture takes into account physical and cyber vulnerabilities and guarantee state and context awareness, and a minimum level of acceptable operation, in response to physical disturbances and malicious attacks. This framework was evaluated on an IPv6 test-bed comprising several distributed devices, where performance and communication links health are analysed. Results from tests prove the relevance and benefits of the proposed approach.