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Register a new userFormal Modelling and Verification of Software Defined Network
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Added by
Jnanamurthy H K
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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In cloud computing, software-defined network (SDN) gaining more attention due to its advantages in network configuration to improve network performance and network monitoring. SDN addresses an issue of static architecture in traditional networks by allowing centralised control of a network system. SDN contains centralised network intelligence module which separates a process of forwarding packets (data plane) from packet routing process (control plane). It is essential to ensure the correctness of SDN due to secure data transmitting in it. In this paper. Model-checking is chosen to verify an SDN network. The Computation Tree Logic (CTL) and Linear Temporal Logic (LTL) used as a specification to express properties of an SDN. Then complete SDN structure is defined formally along with its Kripke structure. Finally, temporal properties are analysed against the SDN Kripke model to assure the properties of SDN is correct.
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Computer networks have become a critical infrastructure. In fact, networks should not only meet strict requirements in terms of correctness, availability, and performance, but they should also be very flexible and support fast updates, e.g., due to policy changes, increasing traffic, or failures. This paper presents a structured survey of mechanism and protocols to update computer networks in a fast and consistent manner. In particular, we identify and discuss the different desirable consistency properties that should be provided throughout a network update, the algorithmic techniques which are needed to meet these consistency properties, and the implications on the speed and costs at which updates can be performed. We also explain the relationship between consistent network update problems and classic algorithmic optimization ones. While our survey is mainly motivated by the advent of Software-Defined Networks (SDNs) and their primary need for correct and efficient update techniques, the fundamental underlying problems are not new, and we provide a historical perspective of the subject as well.
Traditionally, fault- or event-tree analyses or FMEAs have been used to estimate the probability of a safety-critical device creating a dangerous condition. However, these analysis techniques are less effective for systems primarily reliant on software, and are perhaps least effective in Safety of the Intended Functionality (SOTIF) environments, where the failure or dangerous situation occurs even though all components behaved as designed. This paper describes an approach we are considering at BlackBerry QNX: using Bayesian Belief Networks to predict defects in embedded software, and reports on early results from our research.
In Software Product Line Engineering (SPLE), a portfolio of similar systems is developed from a shared set of software assets. Claimed benefits of SPLE include reductions in the portfolio size, cost of software development and time to production, as well as improvements in the quality of the delivered systems. Yet, despite these benefits, SPLE is still in the early adoption stage. We believe that automated approaches, tools and techniques that provide better support for SPLE activities can further facilitate its adoption in practice and increase its benefits. To promote work in this area, the FMSPLE16 workshop focuses on automated analysis and formal methods, which can (1) lead to a further increase in development productivity and reduction in maintenance costs associated with management of the SPLE artifacts, and (2) provide proven guarantees for the correctness and quality of the delivered systems.
Software defined networking (SDN) has been adopted to enforce the security of large-scale and complex networks because of its programmable, abstract, centralized intelligent control and global and real-time traffic view. However, the current SDN-based security enforcement mechanisms require network managers to fully understand the underlying configurations of network. Facing the increasingly complex and huge SDN networks, we urgently need a novel security policy management mechanism which can be completely transparent to any underlying information. That is it can permit network managers to define upper-level security policies without containing any underlying information of network, and by means of model transformation system, these upper-level security policies can be transformed into their corresponding lower-level policies containing underlying information automatically. Moreover, it should ensure system model updated by the generated lower-level policies can hold all of security properties defined in upper-level policies. Based on these insights, we propose a security policy model transformation and verification approach for SDN in this paper. We first present the formal definition of a security policy model (SPM) which can be used to specify the security policies used in SDN. Then, we propose a model transformation system based on SDN system model and mapping rules, which can enable network managers to convert SPM model into corresponding underlying network configuration policies automatically, i.e., flow table model (FTM). In order to verify SDN system model updated by the generated FTM models can hold the security properties defined in SPM models, we design a security policy verification system based on model checking. Finally, we utilize a comprehensive case to illustrate the feasibility of the proposed approach.
Formal Verification (FV) and Machine Learning (ML) can seem incompatible due to their opposite mathematical foundations and their use in real-life problems: FV mostly relies on discrete mathematics and aims at ensuring correctness; ML often relies on probabilistic models and consists of learning patterns from training data. In this paper, we postulate that they are complementary in practice, and explore how ML helps FV in its classical approaches: static analysis, model-checking, theorem-proving, and SAT solving. We draw a landscape of the current practice and catalog some of the most prominent uses of ML inside FV tools, thus offering a new perspective on FV techniques that can help researchers and practitioners to better locate the possible synergies. We discuss lessons learned from our work, point to possible improvements and offer visions for the future of the domain in the light of the science of software and systems modeling.
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