Towards Hierarchical Probabilistic CTL Model Checking: Theoretical Foundations

With the rapid development of IoT in recent years, Smart Home, one of the IoT application markets, has also been gaining popularity. The emergence of Smart Homes has brought convenience to people’s lives, especially for people who live alone with physical illness. Smart Home users normally have higher expectations for reliability and safety of sensor systems, particularly in light of how complicated and uncertain the living environment is. The present work attempts to propose a data-knowledge integrated solution to analyze, model and evaluate the reliability of sensor systems in a smart home by combining quantitative reliability analysis and probabilistic model checking. Probabilistic model checking techniques use logical reasoning to check quantitative properties (as system requirements) and provide mathematical guarantee for them. More specifically, Smart Home Sensor Systems (SHSS) is described as a Markov Chain, commonly used probabilistic model, which models the system behaviour (e.g., probabilistic choice of state transition), and SHSS reliability properties are defined by Probabilistic Computation Tree Logic (PCTL). These choices of model and specification formula allow us to use one of the most recently developed open source probabilistic model checkers, PRISM, to perform the model checking of reliability verification task in SHSS. A real world smart home dataset (Van Kasteren dataset) is employed along with PRISM to illustrate the modeling approach and demonstrate the feasibility and applicability of the proposed approach.

A locative inconsistency-tolerant hierarchical probabilistic computation tree logic (LIHpCTL) is introduced in this paper to establish the logical foundation of a new model checking paradigm. This logic is an extension of several previously proposed extensions of the standard temporal logic known as CTL, which is widely used for model checking. The extended model checking paradigm proposed is intended to appropriately verify locative (spatial), inconsistent, hierarchical, probabilistic (randomized), and time-dependent concurrent systems. Additionally, a survey of various studies on probabilistic, inconsistency-tolerant, and hierarchical temporal logics and their applications in model checking is conducted.

The role of human behaviour in enterprise security is one of the little studied aspects. The author proposes a reinforcement model of collaborative security employing basic concepts from game theory, socio-psychology, and probabilistic model-checking. The proposed model aims towards solving the problem of inducing positive network effect to enable user centric monitoring of security violations, in particular, against violations related to ”semantic manipulation” of context dependent logical resources. Preventing such violations using existing security enforcement mechanisms is neither feasible nor cost effective. The author defines a payoff mechanism to formalize the model by stipulating appropriate payoffs as reward, punishment, and community price according to reporting of genuine or false violations, non-reporting of the detected violations, and proactive reporting of vulnerabilities and threats by the users. Correctness properties of the model are defined in terms of probabilistic robustness property and constraints for economic feasibility of the payoffs. For estimating the payoff parameters, system and user behaviours are further modelled in terms of probabilistic finite state machines (PFSM) and likelihood of the success of the model is specified using probabilistic computation tree logic (PCTL). PRISM model checker based automated quantitative analysis elicits the process of the estimation of various parameters in the model using PFSMs and PCTL formulas.

Port supply chains play a very important role in the process of economic globalization. Lack of trust of the mechanism is the main factor in restricting the development of port supply chains. Blockchains have great potential to solve the trust problem among all participants using port supply chains, which can reduce costs and improve efficiency. As the bridge between blockchains and port supply chains, smart contracts reconstruct the business process of blockchain-empowered port supply chains. In this article, we present an architecture of a consortium blockchain-empowered port supply chain system, and propose a system verification framework for the smart contracts of port supply chains with probabilistic behaviors. The smart contracts are modeled as DTMCs (Discrete-Time Markov Chains), which are automatically transformed through the BPMN (Business Process Model and Notation) description of the smart contracts. The requirements are specified by PCTL (Probabilistic Computation Tree Logic). Moreover, we implement the customs clearance process of the Shanghai Yangshan Port based on blockchain Hyperledger Fabric, and reconstruct the clearance process with smart contracts. We use it to demonstrate the effectiveness of this framework, and identify the smart contracts that do not meet the expected needs of users.

According to our survey about the express delivery from hundreds of campuses, we find that the location of commodity storage places impacts the delivery success rate and service quality. Thus, how to select the proper delivery location is vital to logistics enterprises, which contributes to improve the work efficiency and reduce delivery costs. In this paper, probabilistic model checking is used to verify the delivery fetch system, which evaluates the solution of location distributions in a quantitative way. First, it formalizes the fetch process of express delivery system between business and customer in the form of Discrete-Time Markov Chain (DTMC) when considering the stochastic behavior. Second, Probabilistic Computation Tree Logic (PCTL) is introduced as the verification property to the temporal behavior checking. Third, formal verifications are conducted by the supporting tool PRISM concerning on the transition probabilities computing. Furthermore, verification results are proven to follow the law of Bernouli Large Numbers, which aims to illustrate that the simulation result is close to the actual express delivery situation. Fourth, the express delivery location model is extended as new structure Co-DTMC in order to calculate time consumption and cost consumption, where the punishment factor is designed for the purpose of optimization. Finally, experiments are carried out to show that our approach can effectively select an optimal place for express delivery, which provides an alternative solution to integrate time and cost into the location selection.

Many traditional applications can be refined thanks to the development of blockchain technology. One of these services is non-repudiation, in which participants in a communication process cannot deny their involvement. Due to the vulnerabilities of the non-repudiation protocols, one of the parties involved in the communication can often avoid non-repudiation rules and obtain the expected information to the detriment of the interests of the other party, resulting in adverse effects. This paper studies the fairness guarantee quantitatively through probabilistic model checking. E-fairness is measured by modeling the protocol in probabilistic timed automata and verifying the appropriate property specified in the probabilistic computation tree logic. Furthermore, our analysis proposes insight for choosing suitable values for different parameters associated with the protocol so that a certain degree of fairness can be obtained. Therefore, the reverse question—for a certain degree of fairness ε, how can the protocol parameters be specified to ensure fairness—is answered.

SysML activity diagrams are OMG/INCOSE standard models for specifying and analyzing systems' behaviors. In this paper, we propose an abstraction approach for this type of diagrams that helps to mitigate the state-explosion problem in probabilistic model checking. To this end, we present two algorithms to reduce the size of a given SysML activity diagram. The first eliminates the irrelevant behaviors regarding the property under check, while the second merges control nodes into equivalent ones. The resulting abstracted model can answer safely the Probabilistic Computation Tree Logic (PCTL) property. Moreover, we present a novel calculus for activity diagrams (NuAC) that captures their underlying semantics. In addition, we prove the soundness of our approach by defining a probabilistic weak simulation relation between the semantics of the abstract and the concrete models. This relation is shown to preserve the satisfaction of the PCTL properties. Finally, we demonstrate the effectiveness of our approach on an online shopping system case study.

Probabilistic model checking has been developed for verifying systems that have stochastic and nondeterministic behavior. Given a probabilistic system, a probabilistic model checker takes a property and checks whether or not the property holds in that system. For this reason, probabilistic model checking provide rigorous guarantees. So far, however, probabilistic model checking has focused on propositional models where a state is represented by a symbol. On the other hand, it is commonly required to make relational abstractions in planning and reinforcement learning. Various frameworks handle relational domains, for instance, STRIPS planning and relational Markov Decision Processes. Using propositional model checking in relational settings requires one to ground the model, which leads to the well known state explosion problem and intractability. We present pCTL-REBEL, a lifted model checking approach for verifying pCTL properties of relational MDPs. It extends REBEL, a relational model-based reinforcement learning technique, toward relational pCTL model checking. PCTL-REBEL is lifted, which means that rather than grounding, the model exploits symmetries to reason about a group of objects as a whole at the relational level. Theoretically, we show that pCTL model checking is decidable for relational MDPs that have a possibly infinite domain, provided that the states have a bounded size. Practically, we contribute algorithms and an implementation of lifted relational model checking, and we show that the lifted approach improves the scalability of the model checking approach.

In formal verification, reliable results are of utmost importance. In model checking of digital systems, mainly incorrect implementations of the model checking algorithms due to logical errors are the source of wrong results. In probabilistic model checking, however, numerical instabilities are an additional source for inconsistent results. We motivate our investigations with an example, for which several state-of-the-art probabilistic model checking tools give completely wrong results due to inexact computations. We then analyze, at which points inaccuracies are introduced during the model checking process. We discuss first ideas how, in spite of these inaccuracies, reliable results can be obtained or at least the user be warned about potential correctness problems: (1) usage of exact (rational) arithmetic, (2) usage of interval arithmetic to obtain safe approximations of the actual probabilities, (3) provision of certificates which testify that the result is correct, and (4) integration of a "degree of belief" for each sub-formula into existing model checking tools.

High-performance data streaming technologies are increasingly adopted in IT companies to support the integration of heterogeneous and possibly distributed applications. Compared with the traditional message queuing middleware, a streaming platform enables the implementation of event-streaming systems (ESS) which include not only complex queues but also pipelines that transform and react to the streams of data. By analysing the centralised data streams, one can evaluate the Quality-of-Service for other systems and components that produce or consume those streams. We consider the exploitation of <i>probabilistic model checking</i> as a performance monitoring technique for ESS systems. Probabilistic model checking is a mature, powerful verification technique with successful application in performance analysis. However, an ESS system may contain quantitative parameters that are determined by event streams observed in a certain period of time. In this paper, we present a novel theoretical framework called QV4M (meaning &#x201C;quantitative verification for monitoring&#x201D;) for monitoring ESS systems, which is based on two recent methods of probabilistic model checking. QV4M assumes the parameters in a probabilistic system model as random variables and infers the statistical significance for the probabilistic model checking output. We also present an empirical evaluation of computational time and data cost for QV4M.

Model checking is an established method for verifying behavioral properties of system models. But model checkers tend to support low-level modeling languages that require intricate models to represent even the simplest systems. Modeling complexity arises in part from the need to encode domain knowledge at relatively low levels of abstraction. In this paper, we demonstrate that formalized domain knowledge can be reused to raise the abstraction level of model and property specifications, and hence the effectiveness of model checking. We describe a novel method for domain-specific model checking called cascading verification that uses composite reasoning over high-level system specifications and formalized domain knowledge to synthesize both low-level system models and their behavioral properties for verification. In particular, model builders use a high-level domain-specific language (DSL) based on YAML to express system specifications that can be verified with probabilistic model checking. Domain knowledge is encoded in the Web Ontology Language (OWL), the Semantic Web Rule Language (SWRL) and Prolog, which are used in combination to overcome their individual limitations. A compiler then synthesizes models and properties for verification by the probabilistic model checker PRISM. We illustrate cascading verification for the domain of uninhabited aerial vehicles (UAVs), for which we have constructed a prototype implementation. An evaluation of this prototype reveals nontrivial reductions in the size and complexity of input specifications compared to the artifacts synthesized for PRISM.

Towards enhanced threat modelling and analysis using a Markov Decision Process

The interest in self-organization as a feasible metaphor for dealing with the growing complexity of today’s software systems is constantly rising. In particular, by adopting self-organization, systems can adapt to highly dynamic environments by local interactions among system’s components. As a consequence, the global behavior of the system can be regarded as an emergent property since it appears by a process emerging from local interactions among components. The corresponding system dynamics is usually non-linear and complex so that the adoption of simulation and verification techniques in the early design stage becomes essential to carry out an effective design. Accordingly, in this paper we discuss a hybrid approach relying on stochastic simulation and probabilistic model checking. We show also a possible application of the approach on a problem called collective sort, by adopting the PRISM probabilistic model checker as a concrete tool for analyzing emergent properties. A discussion of the corresponding results is provided.

In the automata theoretic approach to explicit state LTL model checking, the synchronized product of the model and an automaton that represents the negated formula is checked for emptiness. In practice, a (transition-based generalized) Büchi automaton (TGBA) is used for this procedure. This paper investigates whether using a more general form of acceptance, namely a transition-based generalized Rabin automaton (TGRA), improves the model checking procedure. TGRAs can have significantly fewer states than TGBAs; however, the corresponding emptiness checking procedure is more involved. With recent advances in probabilistic model checking and LTL to TGRA translators, it is only natural to ask whether checking a TGRA directly is more advantageous in practice. We designed a multi-core TGRA checking algorithm and performed experiments on a subset of the models and formulas from the 2015 Model Checking Contest and generated LTL formulas for models from the BEEM database. While we found little to no improvement by checking TGRAs directly, we show how various aspects of a TGRA’s structure influences the model checking performance. In this paper, we also introduce a Fin-less acceptance condition, which is a disjunction of TGBAs. We show how to convert TGRAs into automata with Fin-less acceptance and show how a TGBA emptiness procedure can be extended to check Fin-less automata.

Transaction‐level modeling with SystemC has been very successful in describing the behavior of embedded systems by providing high‐level executable models, in which many of them have inherent probabilistic behaviors, eg, random data and unreliable components. It is thus crucial to have both quantitative and qualitative analysis of the probabilities of system properties.Such analysis can be conducted by constructing a formal model of the system under verification and using Probabilistic Model Checking. However, this method is infeasible for large systems, due to the state space explosion. In this article, we demonstrate the successful use of statistical model checking to conduct such analysis directly from large SystemC models and allow designers to express a wide range of useful properties.The first contribution of this work is a framework to verify properties expressed in Bounded Linear Temporal Logic for SystemC models with both timed and probabilistic characteristics.Second, the framework allows users to expose a rich set of user code primitives as atomic propositions in Bounded Linear Temporal Logic. Moreover, users can define their own fine‐grained time resolution rather than the boundary of clock cycles in the SystemC simulation.The third contribution is an implementation of a statistical model checker. It contains an automatic monitor generation for producing execution traces of the model‐under‐verification, the mechanism for automatically instrumenting the model‐under‐verification, and the interaction with statistical model checking algorithms.