Search

Blockchain technology plays a pivotal role in the undergoing fourth industrial revolution or Industry 4.0. It is considered a tremendous boost to company digitalization, thus, considerable investments in blockchain are being made. However, there is no single blockchain technology, but various solutions exist, and they cannot interoperate with one each other. The ecosystem envisioned by the Industry 4.0 does not have centralized management or leading organization, so a single blockchain solution cannot be imposed. The various organizations hold their own blockchains, which must interoperate seamlessly. Despite some solutions for blockchain interoperability being proposed, the problem is still open. This paper aims to devise a secure solution for blockchain interoperability. The proposed approach consists of a relay scheme based on Trusted Execution Environment to provide higher security guarantees than the current literature. In particular, the proposed solution adopts an off-chain secure computation element invoked by a smart contract on a blockchain to securely communicate with its peered counterpart. A prototype has been implemented and used for the performance assessment, e.g., to measure the latency increase due to cross-blockchain interactions. The achieved and reported experimental results show that the proposed security solution introduces an additional latency that is entirely tolerable for transactions. At the same time, the usage of the Trusted Execution Environment imposes a negligible overhead.

This paper presents SPHERE, a project aimed at the realization of an integrated framework to abstract the hardware complexity of interconnected, modern system-on-chips (SoC) and simplify the management of their heterogeneous computational resources. The SPHERE framework leverages hypervisor technology to virtualize computational resources and isolate the behavior of different subsystems running on the same platform, while providing safety, security, and real-time communication mechanisms. The main challenges addressed by SPHERE are discussed in the paper along with a set of new technologies developed in the context of the project. They include isolation mechanisms for mixed-criticality applications, predictable I/O virtualization, the management of time-sensitive networks with heterogeneous traffic flows, and the management of field-programmable gate arrays (FPGA) to provide efficient implementations for cryptography modules, as well as hardware acceleration for deep neural networks. The SPHERE architecture is validated through an autonomous driving use-case.

Cloud computing, edge computing, and the Internet of Things are significantly changing from the original architectural models with pure provisioning of virtual resources (and services) to a transparent and adaptive hosting environment, where cloud providers, as well as “on-premise” resources and end nodes, fully realize the “everything-as-a-service” provisioning concept. The optimal design of these architectures, including the selection of optimal services to acquire, is not trivial in the cloud–edge context due to the involvement of a variable number and the type of available resources offerings and to the impact on cost, performance, and other relevant features such as security, almost never considered. This article presents a novel formalization of the cloud–edge allocation problem for the industrial IoT context. The proposed optimization process takes explicitly into account two critical aspects that are often overlooked in similar approaches, namely, the new cloud–edge on-demand service offerings model for the allocation of resources and the impact on the deployed application, in terms of cost, performance, and security policies actually implemented. An efficient yet suboptimal deterministic solver is also presented and compared with a linear programming one. Results are the same in 86% of the cases on the considered data set while our solver is orders of magnitude faster than the linear one.

So far, multiple classifier systems have been increasingly designed to take advantage of hardware features, such as high parallelism and computational power. Indeed, compared to software implementations, hardware accelerators guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design of a hardware accelerator unfeasible, hindering the adoption of commercial configurable devices. For this reason, in this paper, we exploit approximate computing design paradigm to trade hardware area overhead off for classification accuracy. In particular, starting from trained DT models and employing precision-scaling technique, we explore approximate decision tree variants by means of multiple objective optimization problem, demonstrating a significant performance improvement targeting field-programmable gate array devices.

The growing convergence among information and operation technology worlds in modern Industrial Internet of Things (IIoT) systems is posing new security challenges, requiring the adoption of novel security mechanisms involving light architectures and protocols to cope with IIoT devices resource constraints. In this article, we investigate the adoption of physically unclonable functions (PUFs) in the IIoT context, and propose the design of a PUF-based architecture (Pseudo-PUF), obtained by suitably combining a weak PUF and an encryption module, that can be successfully adopted to implement advanced security primitives while meeting the existing requirements of IIoT devices in terms of cost and resource demand. To demonstrate the feasibility of our proposal, we analyzed the overall quality of different Pseudo-PUF instances with respect to well-known PUF quality metrics, and found that it is possible to obtain good results with a negligible impact on the devices, thus making our approach suited to IIoT deployments.

Privacy is a topic of increasing interest not only for scientific communities, but also for public opinion and for regulatory bodies from all Countries. The problem is that privacy management and enforcement are very dynamic activities and monitoring known fallacies of systems is really difficult, since their number grows up day by day. Moreover, monitoring systems for yet unknown vulnerabilities is, of course, even more difficult. Here we want to present a model and an architecture, based on semantics and reasoning, which is able to detect privacy problems by abstract reasoning on monitored information. The methodology we present here fetches data and evidences from logs and other files, applying model transformation techniques in order to populate semantics repository for enacting reasoning actions. We provide here some experimental results in order to prove the strength of the proposed approach.

Product and application life-cycle management (PLM/ALM) are the processes that govern a product and a software system, respectively, encompassing the creation, deployment and operation of a system from the beginning to the end of its life. As both PLM and ALM require cross-discipline collaboration and cooperation, tools integration and inter-operation are necessary to enable the efficient and effective usage of tool suites supporting the management of the entire system life-cycle and overcome the limitations of all-in-one solutions from one tool vendor. In this context, the Open Services for Life-cycle Collaboration (OSLC) initiative proposes a set of specifications to allow a seamless integration based on linked data. This paper describes the work performed within the ARTEMIS JU project CRYSTAL to develop an environment for the functional system-level testing of railway controllers, relying on OSLC to enable inter-operation with existing PLM/ALM tools. A concrete realization of the proposed architecture is described also discussing some design and implementation choices. A real industrial case study is used to exemplify the features and the usage of the environment in testing one of the functionalities of the Radio Block Centre, the vital core of the European Rail Traffic Management System/European Train Control System (ERTMS/ETCS) Control System.

Fog-IoT systems enable to distribute computing, control, storage, and networking functions closer to edge devices, in order to improve efficiency and reduce latency. In order to cope with the multitude of security issues raised by the lack of centralized control and by the exposure of user sensitive data, suitable security solutions must be devised to protect data and thwart malicious attempts to compromise and take control over communication. In this paper, we propose a mutual authentication scheme relying upon the adoption of Physically Unclonable Functions (PUFs), which enables fog nodes and resource-constrained IoT devices to mutually prove their respective identities during communication, while meeting the existing low resource consumption requirements. The scheme is partially offered in an as-a-service fashion, thanks to the adoption of a cloud automation framework that facilitates its set-up on fog nodes.

Modern physical protection systems integrate a number of security systems (including procedures, equipments, and personnel) into a single interface to ensure an adequate level of protection of people and critical assets against malevolent human actions. Due to the critical functions of a protection system, the quantitative evaluation of its effectiveness is an important issue that still raises several challenges. In this paper we propose a model-driven approach to support the design and the evaluation of physical protection systems based on (a) UML models representing threats, protection facilities, assets, and relationships among them, and (b) the automatic construction of a Bayesian Network model to estimate the vulnerability of different system configurations. Hence, the proposed approach is useful both in the context of vulnerability assessment and in designing new security systems as it enables what-if and cost–benefit analyses. A real-world case study is further illustrated in order to validate and demonstrate the potentiality of the approach. Specifically, two attack scenarios are considered against the depot of a mass transit transportation system in Milan, Italy. © 2016, Springer-Verlag Berlin Heidelberg.

This paper aims at defining a model-driven approach for the diagnosability analysis of discrete event systems (DES). The proposed approach can be adopted during the design of modern control systems, in which many sensors and actuators are employed and the diagnosability of faults within a certain delay could be an issue. The proposal represents a first step towards an automatic model-driven process which derive formal models from a complete high-level specification of DESs. The specification activity of our approach relies on the Dynamic STate Machine (DSTM) formalism, a new language that extends state machines with dynamic instantiation, interrupts and asynchronous communication. The paper will describe how we can automatically derive Petri net and Promela models from the high-level DSTM specification. The former model can be used to apply diagnosability analysis approaches proposed in the DES community, while the latter can be used to apply model checking techniques. An application of the proposed model-driven approach is described by deriving both a PN and a Promela model for the well-known railway level crossing benchmark.

Cloud-based architectures, services and resources are growing up in complexity day by day. Composition and Orchestration deal with the problem of creating complex services promoting reuse and optimal allocation of resources but many security issues arise complicating the work of designers. Even if any composing elements in a composition meets security requirements, the composite service may not. In order to reduce design and developing complexity, Big Vendors and scientific literature promote Composition by Patterns. In this work we present a Model Driven Engineering methodology that addresses validation and verification of security requirements by using formal methods and model transformation algorithms. We show how our model driven approach enables the specification of requirements at design phase and how it allows for verification of security requirements. © 2018 Elsevier Ltd

The use of smart-sensors to recognize automatically complex situations (anomalous behaviors, physical security threats, etc.) requires ‘intelligent’ methods to improve the trustworthiness of automatic decisions. Voting and consensus mechanisms can be employed whether supported by probabilistic formalisms to correlate event occurrence, to merge local events and to estimate the likelihood of overall decisions. This paper presents the results of a quantitative comparison of three different voting schemes based on Bayesian Networks. These models present a growing complexity and they are able to provide a trustworthiness estimation based on single nodes detection reliability in terms of false alarm probabilities.

Recently emerged as an effective approach, Approximate Computing introduces a new design paradigm for trade system overhead off for result quality. Indeed, by relaxing the need for a fully precise outcome, Approximate Computing techniques allow to gain performance parameters, such as computational time or area of integrated circuits, by executing inexact operations. In this work, we propose an approximate version of the K-means algorithm to be used for the image segmentation, with the aim to reduce the area needed to synthesize it on a hardware target. In particular, we detail the methodology to find approximate variants of the K-means and some experimental evidences as a proof-of-concept. © 2017, Springer International Publishing AG.

Due to the pervasiveness of digital technologies in all aspects of human lives, it is increasingly unlikely that a digital device is involved as goal, medium or simply 'witness' of a criminal event. Forensic investigations include recovery, analysis and presentation of information stored in digital devices and related to computer crimes. These activities often involve the adoption of a wide range of imaging and analysis tools and the application of different techniques on different devices, with the consequence that the reconstruction and presentation activities result complicated. This work presents a method, based on Semantic Web technologies, that helps digital investigators to correlate and present information acquired from forensic data, with the aim to get a more valuable reconstruction of events or actions in order to reach case conclusions. © 2017 IEEE.

Nowadays the security of computer devices is growing significantly. This is due to more and more devices are connected to the network. For this reason, optimize the performance of systems able to detect intrusions (IDS) is a goal of common interest. The following work consists of use the generalizing power of neural networks to classify the attacks. In particular, we will use multilayer perceptron (MLP) with the algorithm of back-propagation algorithm and the sigmoidal activation function. We use a subset of the DARPA dataset, known as KDD99. It is a public dataset labeled for an IDS and previously processed. We will make an analysis of the results obtained using different configurations, varying the number of hidden layers and the number of training epochs to obtain a low number of false results. We observe that it is required a large number of training epochs and how, using the entire data set consists of 41 features, the best classification is carried out for the type of DOS and Probe attacks. © 2017 IEEE.

The growing adoption of digital and computer-related technologies has led to the increase of computer crimes occurrences. Today we are exposed to a great number of threats that involve IT technologies, such as terrorism or cyber-scams through Internet, child pornography, espionage and tampering of computer equipment. Digital investigations play a leading role to guarantee a legal use of digital technologies, as well to search for evidences left by the author of a crime. Digital investigations often combine a wide span of skills and require the adoption of different tools and techniques in order to capture, store and analyze evidences from a Computer Crime Scene. The lack of integration and interoperability between these tools protract the analysis process and pauperize presentation results for non-technical parties (from legal, academic or business areas). This work presents a method, based on Semantic Web technologies, to integrate, correlate and query different sources of forensic data with the goal of more valuable reconstruction of a case. © Springer International Publishing AG 2017.

Scientific community is now spending more and more efforts in defining and developing effective methodologies and technologies in order to easy design and development of Cloud solutions. In order to exploit the features of existing Cloud services and Resources Orchestration becomes a hot research topic. In this scenario, Cloud Designers promote reuse but a clear and simple design and verification methodology still misses in literature. In this scenario, a simple (UML-based) modelling profile and a Model-Driven Engineering methodology for Cloud-based Value Added Services are very appealing. In this work we define a modelling profile able to describe Orchestrated Cloud Services and Resources by means of Cloud Design Patterns and we show how Cloud Designer can use it both to ease composition and verification purposes.

The increasing complexity of Cloud Architecture and the introduction of new paradigms like Internet of Things introduced the problem of creating Value Added Services by composition, not only of Resources, but of Services too. In this work we describe an architectural solution for Orchestration at all Cloud Layers. Here we described a language for orchestrating both resources and services in Cloud. The language manages composition of services and resources in order to create composite service based on Cloud Design Patterns. It is based on a Workflow language for description of composition and it enables verification of composite services by means of Model Driven Engineering techniques, providing a precious and easy-to-use tool for Cloud Engineering.

Verification and Validation of railway controllers is the most critical and time-consuming phase in a system development life-cycle. It is regulated by international standards, which explicitly recommend the usage of state machines to model the specification of the system under test. Despite the great deal of works addressing the usage of state machines and their extensions, model-based verification and validation processes still lack concise and expressive-enough notations able to easily capture peculiar features of the specific domain of multi-process control systems, on which proper tool chains can be implemented in order to realize effective and automated environments. This paper introduces a novel class of hierarchical state machines, called Dynamic STate Machines (DSTMs), and proposes an approach for modelling and validating railway control systems, based on the new specification language. Key features of DSTM are recursive execution, parallelism, parameter passing, abortion transition, and communication through global variables and channels, but its main peculiarity resides in the semantics of fork and join operators which allows for dynamic instantiation of machines (processes). The formal semantics of DSTM allows for the definition of verification and validation methodologies supported by automated tools. The paper also describes how DSTM specifications may be mapped to Promela models in order to achieve automated generation of test cases by model checking and Spin. The work presented in this paper was carried out in the context of an European project and is strongly driven by the industrial necessity of tackling issues concerning the automation of functional system-level testing of modern railway signalling systems. Hence, the language and the proposed approach are illustrated and motivated by applying them to a specific functionality of the Radio Block Centre, the vital core of the ERTMS/ETCS Control System.

Model-based approaches are widespread both in functional and non-functional verification activities of critical computer-based systems. Reverse engineering can also be used to support checks for correctness of system implementation against its requirements. In this paper, we show how a model-based technique, using the Unified Modeling language (UM l), suits the reverse engineering of complex control logics. UMl is usually exploited to drive the development of software systems, using an object-oriented and bottom-up approach; however, it can be also used to model legacy non-object-oriented logic processes featuring a clear distinction between data structures and related operations. Our case-study consists in the most important component of the European Railway Traffic Management System/European Train control System: the Radio Block center (RBc). The model we obtained from the logic code of the RB

The construction of a usable, formal, and extensible modeller and simulator for Smart Energy Grids is of a paramount importance in the industrial settings. Final users are interested in deploying effective smart-home configurations able to satisfy energy requests in the most economical way. Hence, a tool able to forecast both energy consumption and related costs of a smarthome configuration is needed. In this paper, the µGRIMOIRE (micro GRId MOdelling envIRonmEnt) toolset is presented. This tool is based on the well-known model-driven paradigm and its successful applications in the generation of formal/quantitative models for complex systems. By using a Domain Specific Modelling Language, a final user can define a smart-home system configuration and energy saving logics. Then, the tool offers the possibility of evaluating the desired user metrics by translating the model into a Fluid Stochastic Petri Net model representing both discrete and continuous variables. © Gentile et al.

The optimal management of water resources is a key problem for the sustainable exploitation of ground and surface water sources. One of the key issues is the assurance of quality of the provided water with respect to the presence of pollutant substances or micro biotic. This paper defines an automatic approach to evaluate the vulnerability of a provisioning network with respect the detection of a threat. By means of the creation of a high level model of a network and the automatic generation of proper formal models, it is possible to detect: (1) the effect of such threat on all the network nodes and (2) the most probable location of the contamination source.

The growing academic and industrial interest in Smart Grids raises the need for methods and techniques supporting the choice of best-saving strategies in intelligent home devices. This paper presents a model-based approach for the integrated evaluation of the energy billing profiles by using the formalism of fluid stochastic Petri nets, able to capture both continuous and discrete system aspects. The prime feature of the modelling approach is the compositionality by which a modeller can easy customise the overall configuration as well as the inner behaviour of a system. This paper also proves the feasibility of the approach by analysing three different saving policies in a Domestic Smart Grid environment and comparing the simulation results. Copyright © 2016 Inderscience Enterprises Ltd.