vTRUST: A Formal Modeling and Verification Framework for Virtualization Systems

Hao, Jianan; Liu, Yang; Cai, Wentong; Bai, Guangdong; Sun, Jun

doi:10.1007/978-3-642-41202-8_22

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…Moreover, the loss function of Adaboost will be modified to consider the classification costs. Moreover, we will also investigate the application of the proposed algorithm to information security [41,43,53,42,41,52,18], bioinformatics [40,24,57], medial imaging [56,55,54], computer vision [38,23,22,36,37,19,17,46,44,45,15], reinforcement learning [27,28], cloud computing [50,51] and microprocessor reliability modeling [8,6,7,5,59,61,60].…”

Section: Discussionmentioning

confidence: 99%

Online classifier adaptation for cost-sensitive learning

Zhang

García

2015

Neural Comput & Applic

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In this paper, we propose the problem of online cost-sensitive classifier adaptation and the first algorithm to solve it. We assume we have a base classifier for a cost-sensitive classification problem, but it is trained with respect to a cost setting different to the desired one. Moreover, we also have some training data samples streaming to the algorithm one by one. The problem is to adapt the given base classifier to the desired cost setting using the steaming training samples online. To solve this problem, we propose to learn a new classifier by adding an adaptation function to the base classifier, and update the adaptation function parameter according to the streaming data samples. Given a input data sample and the cost of misclassifying it, we update the adaptation function parameter by minimizing cost weighted hinge loss and respecting previous learned parameter simultaneously. The proposed algorithm is compared to both online and off-line cost-sensitive algorithms on two cost-sensitive classification problems, and the experiments show that it not only outperforms them one classification performances, but also requires significantly less running time.

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Section: Discussionmentioning

confidence: 99%

Online classifier adaptation for cost-sensitive learning

Zhang

García

2015

Neural Comput & Applic

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“…Several works also consider virtualization within the cloud system. For example the authors of [120] have proposed a formal framework for analysis of security and trust in virtualized system, combining hardware and software models expressed in CSP# [242] dispatched to the PAT model checker discovering a subtle bug in a real world cloud system. Similarly, the authors of [44] have proposed a security subsystem for change analysis within virtualized infrastructures in relation to security policies, by utilization of graphs and graph transformations dispatched to GROOVE model checker [105].…”

Section: Enterprisementioning

confidence: 99%

“…Specification languages. AADL (Architecture Analysis & Design Language) [18,73], ASF (Anonymous Secure Framework) [156], ASLan++ (AVANTSSAR Specification Language) [21], BAN logic [54,232], Boogie [34], Boxed Ambients [138], CASM (ASM-based SL for compilers) [257], CCS (Calculus of Communicating Systems) [155], COVERT (compositional analysis of Android apps) [29], CSP (Communicating Sequential Systems) [123], CSP# (shared variables CSP) [241], CTL (Computation tree temporal logic) [233], Cloud Calculus [138], Cryptol [92], Dynamic State Machine [189], ERC20 token contracts [203], Event-B [85], HLPSL (High Level Protocol Specification Language) [46], Hoare logic [111], LS2 (Logic of Secure Systems) [30], LTL (linear-time temporal logic) [271], Markov Decision Process [184], Petri nets [15], π -calculus [43], PlusCal [9], Promela [175], RTL (real-time logic) [113], SPDL (Security Protocol Description Language) [171], SysML-Sec [18], TLA+ (Temporal Logic of Actions) [75], Trusted CSP# [30], überSpark [257], VDM [101], Verilog [167], VHDL [114], VML [230], vTRUST [120], XMHF (eXtensible and Modular Hypervisor Framework)…”

Section: Specification Languages and Associated Toolsmentioning

confidence: 99%

A Survey of Practical Formal Methods for Security

et al. 2022

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In today’s world, critical infrastructure is often controlled by computing systems. This introduces new risks for cyber attacks, which can compromise the security and disrupt the functionality of these systems. It is therefore necessary to build such systems with strong guarantees of resiliency against cyber attacks. One way to achieve this level of assurance is using formal verification, which provides proofs of system compliance with desired cyber security properties. The use of Formal Methods (FM) in aspects of cyber security and safety-critical systems are reviewed in this article. We split FM into the three main classes: theorem proving, model checking and lightweight FM. To allow the different uses of FM to be compared, we define a common set of terms. We further develop categories based on the type of computing system FM are applied in. Solutions in each class and category are presented, discussed, compared and summarised. We describe historical highlights and developments and present a state-of-the-art review in the area of FM in cyber security. This review is presented from the point of view of FM practitioners and researchers, commenting on the trends in each of the classes and categories. This is achieved by considering all types of FM, several types of security and safety-critical systems and by structuring the taxonomy accordingly. The article hence provides a comprehensive overview of FM and techniques available to system designers of security-critical systems, simplifying the process of choosing the right tool for the task. The article concludes by summarising the discussion of the review, focusing on best practices, challenges, general future trends and directions of research within this field.

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“…In a recent work in [38], a formal framework called vTRUST, is proposed to formally describe virtualization systems with abstraction. The system can be used to verify various properties in virtualization systems such as confidentiality, verifiability, isolation and Platform Configuration Registers (PCR) consistency with respect to certain adversary models.…”

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confidence: 99%

Challenges in Formal Methods for Testing and Verification of Cloud Computing Systems

Gawanmeh¹,

Alomari²

2015

SCPE

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Abstract. Formal methods are necessary to capture the semantics and behavior of processes of various systems. They characterize and provide insight into the behavior of real systems and thus identify their deterministic and non-deterministic features. The design and deployment of cloud computing systems utilize the current technology development in order to provide the appropriate service and accommodate the increasing demand while maintaining high quality and error free service. In this paper, we discuss the state of the art on using formal methods for the verification of cloud computing systems. Even though formal methods have been used successfully in the design and verification of several aspects of these systems, there are still many design issues in cloud computing that can be enhanced using formal methods. For instance, several scheduling algorithms are being used for cloud frameworks, such as Hadoop for instance, that are found to suffer from scheduling failures. This could have been avoided if the schedular has been properly verified. On the other hand, several new paradigms have evolved with cloud computing such as big data, these require fundamental changed on methods and algorithms that are being used for classical distributed systems, which in turn, increase the chance of having faulty systems that are difficult to highlight using only simulation methods. 1. Introduction and Backgrounds. Cloud computing [27] has emerged recently as a new paradigm that moved enterprise computing from the classical host-based architecture pattern into the elastic computing pattern. This new service-oriented vision delivers resources and applications on-demand based on the payper-use concept. Cloud computing platforms include two main aspects: hardware infrastructure and software architecture. The first includes communication network equipment, links, data and storage centers, servers, and computational resources. On the other hand, the second includes operating systems, databases, software development platforms, software applications, and middleware. The National Institute of Standards and Technology [58] defined five basic characteristics of cloud computing: on-demand service, fast application elasticity, network access, resource pooling, and measured service. In addition, applications in the cloud are composed of multiple software components running on complex distributed virtual machines, therefore, several management tasks and dedicated protocols are required for configuring and monitoring these distributed applications in order to preserve their consistency.The cloud system should provide the application users with robustness, fault tolerance, execution automation, and powerful computing facilities. This implies various cloud service requirements to be maintained. Therefore, several verification challenges arise throughout the design development and deployment of these systems. In addition, unlike conventional software and hardware systems, a wide range of different properties and design requirements arise in the...

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vTRUST: A Formal Modeling and Verification Framework for Virtualization Systems

Cited by 7 publications

References 10 publications

Online classifier adaptation for cost-sensitive learning

Online classifier adaptation for cost-sensitive learning

A Survey of Practical Formal Methods for Security

Challenges in Formal Methods for Testing and Verification of Cloud Computing Systems

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