The advent of cloud computing and Internet of Things (IoT) has deeply changed the design and operation of IT systems, affecting mature concepts like trust, security, and privacy. The benefits in terms of new services and applications come at a price of new fundamental risks, and the need of adapting risk management frameworks to properly understand and address them. While research on risk management is an established practice that dates back to the 90s, many of the existing frameworks do not even come close to address the intrinsic complexity and heterogeneity of modern systems. They rather target static environments and monolithic systems thus undermining their usefulness in real-world use cases. In this paper, we present an assurance-based risk management framework that addresses the requirements of risk management in modern distributed systems. The proposed framework implements a risk management process integrated with assurance techniques. Assurance techniques monitor the correct behavior of the target system, that is, the correct working of the mechanisms implemented by the organization to mitigate the risk. Flow networks compute risk mitigation and retrieve the residual risk for the organization. The performance and quality of the framework are evaluated in a simulated industry 4.0 scenario.

Security assurance provides a wealth of techniques to demonstrate that a target system holds some nonfunctional properties and behaves as expected. These techniques have been recently applied to the cloud ecosystem, while encountering some critical issues that reduced their benefit when hybrid systems, mixing public and private infrastructures, are considered. In this paper, we present a new assurance framework that evaluates the trustworthiness of hybrid systems, from traditional private networks to public clouds. It implements an assurance process that relies on a Virtual Private Network (VPN)-based solution to smoothly integrate with the target systems. The assurance process provides a transparent and non-invasive solution that does not interfere with the working of the target system. The performance of the framework have been experimentally evaluated in a simulated scenario.

Current distributed systems increasingly rely on hybrid architectures built on top of IoT, edge, and cloud, backed by dynamically configurable networking technologies like 5G. In this complex environment, traditional security governance solutions cannot provide the holistic view needed to manage these systems in an effective and efficient way. In this paper, we propose a security assurance framework for edge and IoT systems based on an advanced architecture capable of dealing with 5G-native applications.

Machine learning is becoming ubiquitous. From financial to medicine, machine learning models are boosting decisionmaking processes and even outperforming humans in some tasks. This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding predictions, where perturbations of fractions of the training set (poisoning) can seriously undermine the model accuracy. Research on poisoning attacks and defenses even predates the introduction of deep neural networks, leading to several promising solutions. Among them, ensemble-based defenses, where different models are trained on portions of the training set and their predictions are then aggregated, are getting significant attention, due to their relative simplicity and theoretical and practical guarantees. The work in this paper designs and implements a hash-based ensemble approach for ML robustness and evaluates its applicability and performance on random forests, a machine learning model proved to be more resistant to poisoning attempts on tabular datasets. An extensive experimental evaluation is carried out to evaluate the robustness of our approach against a variety of attacks, and compare it with a traditional monolithic model based on random forests.

Machine Learning (ML) is increasingly used to drive the operation of complex distributed systems deployed on the cloud-edge continuum enabled by 5G. Correspondingly, distributed systems' behavior is becoming more non-deterministic in nature. This evolution of distributed systems requires the definition of new assurance approaches for the verification of non-functional properties. Certification, the most popular assurance technique for system and software verification, is not immediately applicable to systems whose behavior is determined by Machine Learning-based inference. However, there is an increasing push from policy makers, regulators, and industrial stakeholders towards the definition of techniques for the certification of non-functional properties (e.g., fairness, robustness, privacy) of ML. This article analyzes the challenges and deficiencies of current certification schemes, discusses open research issues and proposes a first certification scheme for ML-based distributed systems.