Explanation by automated reasoning using the Isabelle Infrastructure framework

In this paper, we propose the use of interactive the- orem proving for explainable machine learning. After presenting our proposition, we illustrate it on the dedicated application of explaining security attacks using the Isabelle Infrastructure framework and its process of dependability engineering. This formal framework and process provides the logics for specifi- cation and modeling. Attacks on security of the system are ex- plained by specification and proofs in the Isabelle Infrastructure framework. Existing case studies of dependability engineering in Isabelle are used as feasibility studies to illustrate how different aspects of explanations are covered by the Isabelle Infrastructure framework

Inter-blockchain protocols with the Isabelle Infrastructure framework

The main incentives of blockchain technology are distribution and distributed change, consistency, and consensus. Beyond just being a distributed ledger for digital currency, smart contracts add transaction protocols to blockchains to execute terms of a contract in a blockchain network. Inter-blockchain (IBC) protocols define and control exchanges between different blockchains. The Isabelle Infrastructure framework has been designed to serve security and privacy for IoT architectures by formal specification and stepwise attack analysis and refinement. A major case study of this framework is a distributed health care scenario for data consistency for GDPR compliance. This application led to the development of an abstract system specification of blockchains for IoT infrastructures. In this paper, we first give a summary of the concept of IBC. We then introduce an instantiation of the Isabelle Infrastructure framework to model blockchains. Based on this we extend this model to instantiate different blockchains and formalize IBC protocols. We prove the concept by defining the generic property of global consistency and prove it in Isabelle

Inter-Blockchain Protocols with the Isabelle Infrastructure Framework

The main incentives of blockchain technology are distribution and distributed change, consistency, and consensus. Beyond just being a distributed ledger for digital currency, smart contracts add transaction protocols to blockchains to execute terms of a contract in a blockchain network. Inter-blockchain (IBC) protocols define and control exchanges between different blockchains. The Isabelle Infrastructure framework {has been designed to} serve security and privacy for IoT architectures by formal specification and stepwise attack analysis and refinement. A major case study of this framework is a distributed health care scenario for data consistency for GDPR compliance. This application led to the development of an abstract system specification of blockchains for IoT infrastructures. In this paper, we first give a summary of the concept of IBC. We then introduce an instantiation of the Isabelle Infrastructure framework to model blockchains. Based on this we extend this model to instantiate different blockchains and formalize IBC protocols. We prove the concept by defining the generic property of global consistency and prove it in Isabelle

Exploring rationality of self awareness in social networking for logical modeling of unintentional insiders

Unawareness of privacy risks together with approval seeking motivations make humans enter too much detail into the likes of Facebook, Twitter, and Instagram. To test whether the rationality principle applies, we construct a tool that shows to a user what is known publicly on social networking sites about her. In our experiment, we check whether this revelation changes human behaviour. To extrapolate and generalize, we use the insights gained by practical experimentation. Unaware users can become targeted by attackers. They then become unintentional insid- ers. We demonstrate this by extending the Isabelle Insider framework to accommodate a formal model of unintentional insiders, an open problem with long standing

Exploring rationality of self awareness in social networking for logical modeling of unintentional insiders

Unawareness of privacy risks together with approval seeking motivations make humans enter too much detail into the likes of Facebook, Twitter, and Instagram. To test whether the rationality principle applies, we construct a tool that shows to a user what is known publicly on social networking sites about her. In our experiment, we check whether this revelation changes human behaviour. To extrapolate and generalize, we use the insights gained by practical experimentation. Unaware users can become targeted by attackers. They then become unintentional insid- ers. We demonstrate this by extending the Isabelle Insider framework to accommodate a formal model of unintentional insiders, an open problem with long standing

Dependability engineering in Isabelle

In this paper, we introduce a process of formal system development supported by interactive theorem proving in a dedicated Isabelle framework. This Isabelle Infrastructure framework implements specification and verification in a cyclic process supported by attack tree analysis closely inter-connected with formal refinement of the specification. The process is cyclic: in a repeated iteration the refinement adds more detail to the system specification. It is a known hard problem how to find the next refinement step: this problem is addressed by the attack based analysis using Kripke structures and CTL logic. We call this cyclic process the Refinement-Risk cycle (RR-cycle). It has been developed for security and privacy of IoT healthcare systems initially but is more generally applicable for safety as well, that is, dependability in general. In this paper, we present the extensions to the Isabelle Infrastructure framework implementing a formal notion of property preserving refinement interleaved with attack tree analysis for the RR-cycle. The process is illustrated on the specification development and privacy analysis of the mobile Corona-virus warning app