Industrial control systems (ICSs), such as Supervisory Control and Data Acquisition (SCADA) systems, are increasingly popular for manufacturing applications, leading to significant improvements in efficiency and productivity. However, the vulnerability of these systems to ransomware attacks has become a major concern. This vulnerability is mainly due to the centralized nature of ICSs, which prioritize efficiency over security. To address this issue, this paper proposes a decentralized Blockchain-Based ICS (BBICS) architecture. Such architecture uses a peer-to-peer network of nodes to replicate critical data and distribute transactions using a consensus mechanism, which synchronizes nodes and resolves single points of failure. Additionally, BBICS encrypts critical data in a tamper-resistant manner to prevent attackers from decrypting or manipulating data. Moreover, zero-trust authorization and authentication further enhance security by preventing the broadcasting of ransomware attacks in internal networks of devices. The evaluation of the proposed system with respect to performance and reliability under normal and ransomware attack situations suggest BBICS’ feasibility and practicality.

Legal contracts specify requirements for business transactions. As any other requirements specification, contracts may contain errors and violate properties expected by contracting parties. Symboleo was recently proposed as a formal specification language for legal contracts. This paper presents SymboleoPC, a tool for analyzing Symboleo contracts using model checking. It highlights the architecture, implementation and testing of the tool, as well as a scalability evaluation with respect to the size of contracts and properties to be checked through a series of experiments. The results suggest that SymboleoPC can be usefully applied to the analysis of formal specifications of contracts with real-life sizes and structures.

Smart contracts (SCs) are software systems that monitor and control the execution of legal contracts to ensure compliance with the contracts' terms and conditions. They often exploit Internet-of-Things technologies to support their monitoring functions, and blockchain technology to ensure the integrity of their data. Ethereum and business blockchain platforms, such as Hyperledger Fabric, are popular choices for SC development. However, there is a gap in the knowledge of SCs between developers and legal experts. Symboleo is a formal specification language for legal contracts that was introduced to address this issue. Symboleo specifications directly encode legal concepts such as parties, obligations, and powers. In this paper, we propose a tool-supported method for translating Symboleo specifications into smart contracts. We have extended the current Symboleo IDE, implemented the ontology and semantics of Symboleo into a reusable library, and developed the Symboleo2SC tool to generate Hyperledger Fabric code exploiting this library. Symboleo2SC was evaluated with three sample contracts. The results shows that legal contract specifications in Symboleo can be fully converted to SCs for monitoring purposes. Moreover, Symboleo2SC helps simplify the SC development process, saves development effort, and helps reduce risks of coding errors.

A contract is a legally binding agreement that expresses high-level requirements of parties in terms of obligations, powers and constraints. Parties' actions influence the status of a contract and shall comply with its clauses. Manual contract monitoring is very laborious in real markets, such as transactive energy, where plenty of complex contracts are running concurrently. Furthermore, liability, right and performance transition through run-time operations such as subcontracting, assignment and substitution complicate contract interpretation. Automation is needed to ensure that contracts respect desirable properties and to support monitoring of compliance and handling of violations. In this thesis research, I propose an innovative ontology that defines fundamental contractual notions (such as the ones mentioned above) and their relationships, on which is built a specification language, called Symboleo, that provides syntax and axiomatic semantics of contracts via first-order logic. Symboleo enables the development of advanced automation tools such as a compliance checker that monitors contracts at runtime, and a model checking verification method that analyzes liveness and safety properties of contracts. This paper reports on the problem domain, research method, current status, expected contributions, and main foreseen challenges.

Legal contracts specify the terms and conditions (in essence, requirements) that apply to business transactions. Smart contracts are software systems that monitor and control the execution of contracts to ensure compliance. This paper proposes a formal specification language for contracts, called Symboleo, where contracts consist of collections of obligations and powers that define the legal contract's compliant executions. The formal semantics of Symboleo is based on an extension of an ontology for Law and is described in terms of logical axioms on statecharts that describe the lifetimes of contracts, obligations and powers. Our proposal includes a preliminary evaluation through the specification of a real life-inspired Sale-of-Goods contract, with a prototype execution engine. We envision this language to enable formally verifying contracts to detect requirements-level issues and to generate executable smart contracts (e.g., on blockchain technology).

We propose a self-adaptive process (SAP) that maintains the software architecture quality using the MAPE-K standard model. The proposed process can be plugged into various software development processes and service-oriented methodologies due to its explicitly defined inputs and outputs. To this aim, the proposed SAP is integrated with the service-oriented modeling and application (SOMA) methodology in a two-layered structure to create a novel methodology, named self-adaptive service-oriented architecture methodology (SASOAM), which provides a semi-automatic self-aware method by the composition of architectural tactics. Moreover, the maintenance activity of SOMA is improved using architectural and adaptive patterns, which results in controlling the software architecture quality. The improvement in the maintainability of SOMA is demonstrated by an analytic hierarchy process (AHP) based evaluation method. Furthermore, the proposed method is applied to a case study to represent the feasibility and practicality of SASOAM.