Where Do Your IoT Ingredients Come From?

Bodei, Chiara; Degano, Pierpaolo; Ferrari, Gian Luigi; Galletta, Letterio

doi:10.1007/978-3-319-39519-7_3

Cited by 17 publications

(35 citation statements)

References 18 publications

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“…We briefly present a version of IoT-LySa [4,3,9], a specification language recently proposed for designing IoT systems. It is, in turn, an adaption of LySa [2], a process calculus introduced to specify and analyse cryptographic protocols and checking their security properties (see e.g.…”

Section: Overview Of Iot-lysamentioning

confidence: 99%

“…Here we present a variant of the CFA of [7], following the same schema of the ones in [4,8]. It approximates the abstract behaviour of a system of nodes and tracks the trajectories of data.…”

Section: Control Flow Analysismentioning

confidence: 99%

“…With respect to the previous analyses of IoT-LySa, such as the ones in [4,7], the component α is new, and also the combined use of the above five components is new and allows us to potentially integrate the present CFA with the previous ones. For simplicity, we do not have here the component that tracks which output tuples that may be accepted in input as in [7].…”

Section: Control Flow Analysismentioning

confidence: 99%

“…It is also possible to prove the existence of a (minimal) estimate, as in [4], since estimates form a Moore family with a minimal element.…”

Section: Control Flow Analysismentioning

confidence: 99%

“…We intend here to exploit the Security by Design development methodology, introduced in [4,3,9] by taking inspiration from the work by Barrère, Hankin, et al [1,20], where the authors propose some security metrics for identifying critical components in ICS (Industrial Control System) networks.…”

Section: Introductionmentioning

confidence: 99%

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Security Metrics at Work on the Things in IoT Systems

Bodei

Degano

Ferrari

et al. 2020

From Lambda Calculus to Cybersecurity Through Program Analysis

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The Internet of Things (IoT) is deeply changing our society. Daily we use smart devices that automatically collect, aggregate and exchange data about our lives. These data are often pivotal when they are used e.g. to train learning algorithms, to control cyber-physical systems, and to guide administrators to take crucial decisions. As a consequence, security attacks on devices can cause severe damages on IoT systems that take care of essential services, such as delivering power, water, transport, and so on. The difficulty of preventing intrusions or attacks is magnified by the big amount of devices and components IoT systems are composed of. Therefore, it is crucial to identify the most critical components in a network of devices and to understand their level of vulnerability, so as to detect where it is better to intervene for improving security. In this paper, we start from the modelling language IoT-LySa and from the results of Control Flow Analysis that statically predict the manipulation of data and their possible trajectories. On this basis, we aim at deriving possible graphs of how data move and which are their dependencies. These graphs can be analysed, by exploiting some security metrics -among which those introduced by Barrere, Hankin et al. -offering system administrators different estimates of the security level of their systems. Partially supported by Università di Pisa PRA_2018_66 DECLWARE: Metodologie dichiarative per la progettazione e il deployment di applicazioni and by MIUR project PRIN 2017FTXR7S IT MATTERS (Methods and Tools for Trustworthy Smart Systems). := (x c ch I check I ≥ cr I thI ). alarmI c al I , 1CR, ring .h ACR = µh.(1CR, {Ring}).τ.h

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Section: Overview Of Iot-lysamentioning

confidence: 99%

Section: Control Flow Analysismentioning

confidence: 99%

Section: Control Flow Analysismentioning

confidence: 99%

“…It is also possible to prove the existence of a (minimal) estimate, as in [4], since estimates form a Moore family with a minimal element.…”

Section: Control Flow Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Security Metrics at Work on the Things in IoT Systems

Bodei

Degano

Ferrari

et al. 2020

From Lambda Calculus to Cybersecurity Through Program Analysis

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A process calculus SMrCaIT for IoT

Chen

Zhu

2023

J Software Evolu Process

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With the rapid popularization of smart devices, the applications and technologies of the Internet of Things (IoT) are in high demand worldwide, especially in improving development efficiency and ensuring system quality, reliability, and security. Formal methods have been successfully used to specify, verify, and analyze software and hardware systems, effectively alleviating the above problems in these systems. However, most of the existing works mainly focus on the practical applications of IoT, and there is a lack of research on modeling and analyzing IoT systems from the perspective of formal methods. In this paper, we first propose a secure mobile real‐time process calculus for specifying and reasoning about IoT systems, called SMrCaIT, which supports not only value‐passing communication but also name‐passing communication. In addition, this calculus can strictly separate process actions and mobility modeling by providing parametric mobility models. Subsequently, we present the operational semantics of this calculus, in particular, the rules on how to secure channel communication by closing the scope of a channel and handling scope extrusion. Taking vehicle ad hoc network (VANET) as a case, the application details of SMrCaIT and its operational semantics are fully demonstrated. By using the rewrite engine Real‐Time Maude, we further implement SMrCaIT and its operational semantics and verify some properties of the VANET case to indicate the effectiveness of our calculus in real‐world scenes.

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Revealing the Trajectories of KLAIM Tuples, Statically

Bodei

Degano

Ferrari

et al. 2019

Models, Languages, and Tools for Concurrent and Distributed Programming

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KLAIM (Kernel Language for Agents Interaction and Mobility) has been devised to design distributed applications composed by many components deployed over the nodes of a distributed infrastructure and to offer programmers primitive constructs for communicating, distributing and retrieving data. Data could be sensitive and some nodes could not be secure. As a consequence it is important to track data in their traversal of the network. To this aim, we propose a Control Flow Analysis that over-approximates the behaviour of KLAIM processes and tracks how tuple data can move in the network.

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Where Do Your IoT Ingredients Come From?

Cited by 17 publications

References 18 publications

Security Metrics at Work on the Things in IoT Systems

Security Metrics at Work on the Things in IoT Systems

A process calculus SMrCaIT for IoT

Revealing the Trajectories of KLAIM Tuples, Statically

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