Automatic Covert Channel Analysis of a Multilevel Secure Component

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.

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.

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.

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.

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.

In this paper we describe PRISM, a tool being developed at the University of Birmingham for the analysis of probabilistic systems. PRISM supports three probabilistic models: discrete-time Markov chains, Markov decision processes and continuous-time Markov chains. Analysis is performed through model checking such systems against specifications written in the probabilistic temporal logics PCTL and CSL. The tool features three model checking engines: one symbolic, using BDDs (binary decision diagrams) and MTBDDs (multi-terminal BDDs); one based on sparse matrices; and one which combines both symbolic and sparse matrix methods. PRISM has been successfully used to analyse probabilistic termination, performance, and quality of service properties for a range of systems, including randomized distributed algorithms, manufacturing systems and workstation clusters.KeywordsModel CheckMarkov Decision ProcessBinary Decision DiagramSymbolic Model CheckerModel Check ToolThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper extends and applies the strategies recently developed by the authors for handling constraints under uncertainty to robust design optimization. For the scope of this paper, robust optimization is a methodology aimed at problems for which some parameters are uncertain and are only known to belong to some uncertainty set. This set can be described by either a deterministic or a probabilistic model. In the methodology developed herein, optimization-based strategies are used to bound the constraint violation region using hyper-spheres and hyper-rectangles. By comparing the resulting bounding sets with any given uncertainty model, it can be determined whether the constraints are satisfied for all members of the uncertainty model (i.e., constraints are feasible) or not (i.e., constraints are infeasible). If constraints are infeasible and a probabilistic uncertainty model is available, upper bounds to the probability of constraint violation can be efficiently calculated. The tools developed enable approximating not only the set of designs that make the constraints feasible but also, when required, the set of designs for which the probability of constraint violation is below a prescribed admissible value. When constraint feasibility is possible, several design criteria can be used to shape the uncertainty model of performance metrics of interest. Worst-case, least-second-moment, and reliability-based design criteria are considered herein. Since the problem formulation is generic and the tools derived only require standard optimization algorithms for their implementation, these strategies are easily applicable to a broad range of engineering problems.

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.

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.

The development of safety-critical systems is a rather challenging task, especially due to the cost and complexity associated with this endeavor. For this reason, early fault assessment is a key element towards minimizing vulnerability at the design stage of development. Existing early analysis techniques are often unable to conduct a comprehensive and exhaustive analysis on complex redundant architectures, which may lead to less than optimal risk evaluation. This paper seeks to address some of these issues by proposing a high-level analysis methodology based on probabilistic model checking. This analysis is done by introducing new probabilistic models for repairable fault trees described in the Continuous-Time Markov Decision Process formalism. The models include repairable components and redundancy partitioning to evaluate fault vulnerability across different implementations of the system. The presented approach is very scalable and results demonstrate that the proposed analysis is as reliable as physical FPGA testing, in some scenarios.

This paper concerns the formal modelling of medium access control in nonbeacon-enabled IEEE 802.15.4 wireless personal area networks with probabilistic timed automata supported by the PRISM probabilistic model checker. In these networks, the devices contend for the medium by executing an unslotted carrier sense multiple access with collision avoidance algorithm. In the literature, a model of a network which consists of two stations sending data to two different destination stations is introduced. We have improved this model and, based on it, we propose two ways of modelling a network with an arbitrary number of sending stations, each having its own destination. We show that the same models are valid representations of a star-shaped network with an arbitrary number of stations which send data to the same destination station. We also propose how to model such a network if some of the sending stations are not within radio range of the others, i.e. if they are hidden. We present some results obtained for these models by probabilistic model checking using PRISM.

This paper proposes an online human-aware behavior planning method to enable Unmanned Aerial Vehicles (UAVs) to dynamically satisfy high-level linear temporal logic (LTL) task descriptions from human collaborators. The proposed method has the potential to be applied to the industrial human–UAV collaboration. The specific process is as follows. Firstly, the global high-level task of the UAV is described using the formal language of LTL, which can usually be issued by human collaborators in natural language. Secondly, the task description is transformed into a deterministic Rabin automaton, and then the state values of the automaton are given by the distance from the accepted states. Thirdly, the Markov decision process is used to construct the probabilistic behavior model of the UAV offline. Based on this model, a finite state automaton in the finite horizon is dynamically constructed online, and the Cartesian product system within the horizon is constructed, in which the expected arrival states within the horizon are set by the minimum state values. Finally, aiming at maximizing the reachability probability of the expected states, the value iterative algorithm is introduced to solve the optimal behavior plan online within the finite horizon. After it, an infinite horizon planning and execution algorithm is formed. Experiments and analysis show that the results of the online behavior planning are consistent with the task logic at the semantic level, indicating the correctness of the proposed method. Moreover, the proposed method can effectively alleviate the state explosion caused by the global probabilistic model checking and improve the efficiency of plan generation.