Formal analysis techniques for gossiping protocols

We give a survey of formal verification techniques that can be used to corroborate existing experimental results for gossiping protocols in a rigorous manner. We present properties of interest for gossiping protocols and discuss how various formal evaluation techniques can be employed to predict them

On the design and development of emulation platforms for NFV-based infrastructures

Network Functions Virtualisation (NFV) presents several advantages over traditional network architectures, such as flexibility, security, and reduced CAPEX/OPEX. In traditional middleboxes, network functions are usually executed on specialised hardware (e.g., firewall, DPI). Virtual Network Functions (VNFs) on the other hand, are executed on commodity hardware, employing Software Defined Networking (SDN) technologies (e.g., OpenFlow, P4). Although platforms for prototyping NFV environments have emerged in recent years, they still present limitations that hinder the evaluation of NFV scenarios such as fog computing and heterogeneous networks. In this work, we present NIEP: a platform for designing and testing NFV-based infrastructures and VNFs. NIEP consists of a network emulator and a platform for Click-based VNFs development. NIEP provides a complete NFV emulation environment, allowing network operators to test their solutions in a controlled scenario prior to deployment in production networks

In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems

The remarkable success of the use of machine learning-based solutions for network security problems has been impeded by the developed ML models' inability to maintain efficacy when used in different network environments exhibiting different network behaviors. This issue is commonly referred to as the generalizability problem of ML models. The community has recognized the critical role that training datasets play in this context and has developed various techniques to improve dataset curation to overcome this problem. Unfortunately, these methods are generally ill-suited or even counterproductive in the network security domain, where they often result in unrealistic or poor-quality datasets. To address this issue, we propose an augmented ML pipeline that leverages explainable ML tools to guide the network data collection in an iterative fashion. To ensure the data's realism and quality, we require that the new datasets should be endogenously collected in this iterative process, thus advocating for a gradual removal of data-related problems to improve model generalizability. To realize this capability, we develop a data-collection platform, netUnicorn, that takes inspiration from the classic "hourglass" model and is implemented as its "thin waist" to simplify data collection for different learning problems from diverse network environments. The proposed system decouples data-collection intents from the deployment mechanisms and disaggregates these high-level intents into smaller reusable, self-contained tasks. We demonstrate how netUnicorn simplifies collecting data for different learning problems from multiple network environments and how the proposed iterative data collection improves a model's generalizability

An Experimental Framework for 5G Wireless System Integration into Industry 4.0 Applications

The fourth industrial revolution, or Industry 4.0 (I4.0), makes use of wireless technologies together with other industrial Internet-of-Things (IIoT) technologies, cyber–physical systems (CPS), and edge computing to enable the optimization and the faster re-configuration of industrial production processes. As I4.0 deployments are ramping up, the practical integration of 5G wireless systems with existing industrial applications is being explored in both Industry and Academia, in order to find optimized strategies and to develop guidelines oriented towards ensuring the success of the industrial wireless digitalization process. This paper explores the challenges arisen from such integration between industrial systems and 5G wireless, and presents a framework applicable to achieve a structured and successful integration. The paper aims at describing the different aspects of the framework such as the application operational flow and its associated tools, developed based on analytical and experimental applied research methodologies. The applicability of the framework is illustrated by addressing the integration of 5G technology into a specific industrial use case: the control of autonomous mobile robots. The results indicate that 5G technology can be used for reliable fleet management control of autonomous mobile robots in industrial scenarios, and that 5G can support the migration of the on-board path planning intelligence to the edge-cloud

Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection

In the autoencoder based anomaly detection paradigm, implementing the autoencoder in edge devices capable of learning in real-time is exceedingly challenging due to limited hardware, energy, and computational resources. We show that these limitations can be addressed by designing an autoencoder with low-resolution non-volatile memory-based synapses and employing an effective quantized neural network learning algorithm. We propose a ferromagnetic racetrack with engineered notches hosting a magnetic domain wall (DW) as the autoencoder synapses, where limited state (5-state) synaptic weights are manipulated by spin orbit torque (SOT) current pulses. The performance of anomaly detection of the proposed autoencoder model is evaluated on the NSL-KDD dataset. Limited resolution and DW device stochasticity aware training of the autoencoder is performed, which yields comparable anomaly detection performance to the autoencoder having floating-point precision weights. While the limited number of quantized states and the inherent stochastic nature of DW synaptic weights in nanoscale devices are known to negatively impact the performance, our hardware-aware training algorithm is shown to leverage these imperfect device characteristics to generate an improvement in anomaly detection accuracy (90.98%) compared to accuracy obtained with floating-point trained weights. Furthermore, our DW-based approach demonstrates a remarkable reduction of at least three orders of magnitude in weight updates during training compared to the floating-point approach, implying substantial energy savings for our method. This work could stimulate the development of extremely energy efficient non-volatile multi-state synapse-based processors that can perform real-time training and inference on the edge with unsupervised data

Abmash: Mashing Up Legacy Web Applications by Automated Imitation of Human Actions

Many business web-based applications do not offer applications programming interfaces (APIs) to enable other applications to access their data and functions in a programmatic manner. This makes their composition difficult (for instance to synchronize data between two applications). To address this challenge, this paper presents Abmash, an approach to facilitate the integration of such legacy web applications by automatically imitating human interactions with them. By automatically interacting with the graphical user interface (GUI) of web applications, the system supports all forms of integrations including bi-directional interactions and is able to interact with AJAX-based applications. Furthermore, the integration programs are easy to write since they deal with end-user, visual user-interface elements. The integration code is simple enough to be called a "mashup".Comment: Software: Practice and Experience (2013)

Virtual machines In Education

Abstract To provide education and particularly providing practical educational experiences to the students in the field of computing and information technology related courses including practical experience in the field of Networking, System Administration, and Operating Systems needs a lot of resources for the institution. Because this level of technical education can’t be provided only theoretically, students also need hands-on practical experience, and providing practical experience faces a lot of problems such as lack of funding and physical space, risks and threats to the network environment when we attempt to provide real, physical laboratory for experiments. This problem can be solved by developing a virtual environment for delivering students practical education. In this report we will look into different technologies used for virtualization today and do a comparative study. We will also explore some of the institutions, which are using virtual machines based environment to provide students practical experience in the field of computing and information Technology. And see how peoples are getting benefits from using virtual machines. We present how networks of virtual machines can be beneficiary for computing and information technology student and institutions by providing necessary environment in virtual network

An Evaluation Framework for Reputation Management Systems

Reputation management (RM) is employed in distributed and peer-to-peer networks to help users compute a measure of trust in other users based on initial belief, observed behavior, and run-time feedback. These trust values influence how, or with whom, a user will interact. Existing literature on RM focuses primarily on algorithm development, not comparative analysis. To remedy this, we propose an evaluation framework based on the trace-simulator paradigm. Trace file generation emulates a variety of network configurations, and particular attention is given to modeling malicious user behavior. Simulation is trace-based and incremental trust calculation techniques are developed to allow experimentation with networks of substantial size. The described framework is available as open source so that researchers can evaluate the effectiveness of other reputation management techniques and/or extend functionality. This chapter reports on our framework’s design decisions. Our goal being to build a general-purpose simulator, we have the opportunity to characterize the breadth of existing RM systems. Further, we demonstrate our tool using two reputation algorithms (EigenTrust and a modified TNA-SL) under varied network conditions. Our analysis permits us to make claims about the algorithms’ comparative merits. We conclude that such systems, assuming their distribution is secure, are highly effective at managing trust, even against adversarial collectives