A Framework for Threat-Driven Cyber Security Verification of IoT Systems

Kulik, Tomas; Tran-Jørgensen, Peter W. V.; Boudjadar, Jalil; Schultz, Carl

doi:10.1109/icstw.2018.00033

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…However, the aforementioned general composition methods do not take into consideration the special intrinsic of security. Theoretical works for secure system composition are presented in [36][37][38][39]. In [36], the authors document a set of composition theorems which can prove protocols' security under standard stand-alone and compositional definitions of security.…”

Section: Security Validationmentioning

confidence: 99%

“…In [38], methods of concurrent general composition and universal composability are examined on public-key models and scenarios with no trust infrastructure. Newer studies [39] update the security composition theory with properties like mitigation of eavesdropping and identity faking. Therefore, a modeling framework is proposed [39] for security verification and is applied in distributed and industrial IoT infrastructures.…”

Section: Security Validationmentioning

confidence: 99%

“…Newer studies [39] update the security composition theory with properties like mitigation of eavesdropping and identity faking. Therefore, a modeling framework is proposed [39] for security verification and is applied in distributed and industrial IoT infrastructures. The framework performs model-checking techniques and the overall analysis is supported by the Alloy Analyzer [40].…”

Section: Security Validationmentioning

confidence: 99%

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Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem

et al. 2020

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Key challenges in Internet-of-Things (IoT) system design and management include the secure system composition and the calculation of the security and dependability level of the final system. This paper presents an event-based model-checking framework for IoT systems’ design and management, called CompoSecReasoner. It invokes two main functionalities: (i) system composition verification, and (ii) derivation and validation of security, privacy, and dependability (SPD) metrics. To measure the SPD values of a system, we disassemble two well-known types of security metrics—the attack surface methodologies and the medieval castle approach. The first method determines the attackable points of the system, while the second one defines the protection level that is provided by the currently composed system-of-systems. We extend these techniques and apply the Event Calculus method for modelling the dynamic behavior of a system with progress in time. At first, the protection level of the currently composed system is calculated. When composition events occur, the current system status is derived. Thereafter, we can deploy reactive strategies and administrate the system automatically at runtime, implementing a novel setting for Moving Target Defenses. We demonstrate the overall solution on a real ambient intelligence application for managing the embedded devices of two emulated smart buildings.

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Section: Security Validationmentioning

confidence: 99%

Section: Security Validationmentioning

confidence: 99%

Section: Security Validationmentioning

confidence: 99%

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Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem

et al. 2020

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“…In this section, we present security analysis about our system in Alloy [44], [45]. As a formal specification language, Alloy can be used to express a system based on first order logic.…”

Section: Security Analysismentioning

confidence: 99%

Dynamic Distributed Honeypot Based on Blockchain

Shi

Liu

et al. 2019

IEEE Access

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Honeypot technology can be applied to efficiently attract attackers and exhaust their resources. However, the traditional static honeypot is easy to be recognized by anti-honeypot technology. By contrast, most of the dynamic honeypots can simulate the real system in time, thus interacting with an intruder in disguise. In this paper, we employ the dynamic property of honeypot in four kinds of services of our system. However, this dynamic property shows up in a location and identification, indicating that genuine or fake services (honeypots) are changeable in different hosts. Thus, the dynamic property of our system differs from the dynamic honeypot aforementioned. Besides, we adopt the blockchain platform (Ethereum) to decentralize our system and store the port access data by delivering a private chain. To illustrate the effectiveness of our scheme in theory and practice, security analysis, eavesdropping attack, scanning attack, and DoS attack experiments are conducted. The results show that our scheme is valid in safeguarding against network attack. INDEX TERMS Honeypot, blockchain, security analysis, network security.

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“…Our earlier work has primarily focused on verifying security aspects of the control network itself, which only forms a small part of the system as a whole [22]. As part of that work we were able to verify security properties of a control network consisting of up to 100 nodes using the Alloy Analyzer [19].…”

Section: Introductionmentioning

confidence: 99%

Security Verification of Industrial Control Systems using Partial Model Checking

Kulik

Boudjadar

Tran-Jørgensen

2020

Proceedings of the 8th International Conference on Formal Methods in Software Engineering

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Industrial control systems are moving from isolated to distributed and cloud-connected architectures. While the operational benefits of this migration form the driving force for this trend, the necessary security assurance is often difficult to achieve. Formal methods, including model checking, provide capable technologies to deal with this challenge. However, when formal verification must account for the complexity of modern control systems the state space being explored grows drastically as more details are included in the analysis. This may eventually cause a state space explosion, which makes formal verification infeasible. To address this, we propose a method for decomposing cloud-connected control systems into modules representing the different parts of the system (clients, the cloud, the control network, etc.). Based on the decomposed version of the system, we use UPPAAL to model several well-known cyber attacks and formally verify the system's behavior under these attacks. To determine viability of our approach, we first use statistical model checking SMC to assess the probabilities of success for selected attacks. Based on SMC outcomes, we use symbolic model checking to individually analyse the subsystem affected by each attack. The results obtained from this analysis are then used to demonstrate the feasibility of our approach. We demonstrate our method using an actual control system architecture provided by our industrial partner. CCS CONCEPTS • Security and privacy → Logic and verification; Denial-ofservice attacks; Security requirements.

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A Framework for Threat-Driven Cyber Security Verification of IoT Systems

Cited by 9 publications

References 11 publications

Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem

Artificial Intelligence-Driven Composition and Security Validation of an Internet of Things Ecosystem

Dynamic Distributed Honeypot Based on Blockchain

Security Verification of Industrial Control Systems using Partial Model Checking

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