Quantitative Verification for Monitoring Event-Streaming Systems

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.

This study proposes a hierarchical probabilistic computation tree logic, HpCTL, which is an extension of the standard probabilistic computation tree logic pCTL, as a theoretical basis for hierarchical probabilistic CTL model checking. Hierarchical probabilistic model checking is a new paradigm that can appropriately verify hierarchical randomized (or stochastic) systems. Furthermore, a probability-measure-independent translation from HpCTL into pCTL is defined, and a theorem for embedding HpCTL into pCTL is proved using this translation. Finally, the relative decidability of HpCTL with respect to pCTL is proved using this embedding theorem. These embedding and relative decidability results allow us to reuse the standard pCTL-based probabilistic model checking algorithms to verify hierarchical randomized systems that can be described using HpCTL.

Problem statement: Membrane computing formalism has provided better modeling capabilities for biological systems in comparison to conventional mathematical models. Model checking could be used to reason about the biological system in detail and with precision by verifying formally whether membrane computing model meets the properties of the system. Approach: This study was carried to investigate the preservation of properties of two biological systems that had been modeled and simulated in membrane computing by a method of model checking using PRISM. The two biological systems were prey-predator population and signal processing in the legend-receptor networks of protein TGF-β. Results: The model checking of membrane computing model of the biological systems with five different properties showed that the properties of the biological systems could be preserved in the membrane computing model. Conclusion: Membrane computing model not only provides a better approach in representing and simulating a biological system but also able to sustain the basic properties of the system.

<b>Problem statement:</b> Most of the biological systems have been hierarchical in structure with processes interacting between different compartments. Membrane computing formalism has provided modeling capabilities in representing the structure of biological systems. <b>Approach:</b> This study was carried to investigate the modeling of a multi-compartment biological system by using membrane computing formalism. The hormone-induced calcium oscillations in liver cells which was modeled with ordinary differential equation was used as a case study. The membrane computing model of this case study was verified and validated by using simulation strategy of Gillespie algorithm and the method of model checking using probabilistic symbolic model checker. The results of membrane computing model were compared to the ordinary differential equation model. <b>Results:</b> The simulation and model checking of membrane computing model of the biological case study showed that the properties of the multi-compartments biological system could be preserved with the membrane computing model. Membrane computing model could also accommodate the structure and processes of the multi-compartments biological system which were absent in the ordinary differential equation model. <b>Conclusion:</b> Membrane computing model provides a better approach in representing a multi-compartment system and able to sustain the basic properties of the system. However appropriate value of parameters to represent the rules of the processes of the membrane computing model to manage the stochastic behavior should be formulated to meet the performance of the biological system.

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.

Model checking epistemic–probabilistic logic using probabilistic interpreted systems

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.

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 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.

Probabilistic model checking is an automated technique to verify whether a probabilistic system, e.g., a distributed network protocol which can exhibit failures, satisfies a temporal logic property, for example, “the minimum probability of the network recovering from a fault in a given time period is above 0.98”. Dually, we can also synthesise, from a model and a property specification, a strategy for controlling the system in order to satisfy or optimise the property, but this aspect has received less attention to date. In this paper, we give an overview of methods for automated verification and strategy synthesis for probabilistic systems. Primarily, we focus on the model of Markov decision processes and use property specifications based on probabilistic LTL and expected reward objectives. We also describe how to apply multi-objective model checking to investigate trade-offs between several properties, and extensions to stochastic multi-player games. The paper concludes with a summary of future challenges in this area.

Variations in timing can occur due to multiple sources on a chip. Many circuit level statistical techniques are used to analyze timing in the presence of these sources of variation. It is desirable to have “variation awareness” at the Register Transfer Level (RTL), and estimate block level delay distributions early in the design cycle, to evaluate design choices quickly and minimize post-synthesis simulation costs. We introduce SHARPE, a rigorous, systematic methodology to verify design correctness in RTL in the presence of variations. In this paper, we describe SHARPE in the context of computing statistical delay invariants in the presence of input variations. We treat the RTL source code as a program and use static program analysis techniques to compute probabilities. We model the probabilistic RTL modules as Discrete Time Markov Chains (DTMCs) that are then checked formally for probabilistic invariants using PRISM, a probabilistic model checker. Our technique is illustrated on the RTL description of the data path of OR1200, an open source embedded processor. We demonstrate the enhanced scalability of SHARPE by applying compositional reasoning for probabilistic model checking.

The NRL Pump protocol defines a multilevel secure component whose goal is to minimize leaks of information from high level systems to lower level systems, without degrading average time performances. We define a probabilistic model for the NRL Pump and show how a probabilistic model checker (FHP-murϕ) can be used to estimate the capacity of a probabilistic covert channel in the NRL Pump. We are able to compute the probability of a security violation as a function of time for various configurations of the system parameters (e.g. buffer sizes, moving average size, etc). Because of the model complexity, our results cannot be obtained using an analytical approach and, because of the low probabilities involved, it can be hard to obtain them using a simulator.

Malicious insider threats are difficult to detect and to mitigate. Many approaches for explaining behaviour exist, but there is little work to relate them to formal approaches to insider threat detection. In this work we present a general formal framework to perform analysis for malicious insider threats, based on probabilistic modelling, verification, and synthesis techniques. The framework first identifies insiders’ intention to perform an inside attack, using Bayesian networks, and in a second phase computes the probability of success for an inside attack by this actor, using probabilistic model checking.

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.