There is much talk about UFOs sometimes. Is there anyway? If so what are they? Here are some questions we want to answer them briefly in this paper. An unidentified flying object or UFO is defined as any apparent object in the sky that can't be identified and classified as an object or phenomenon already known. But the name is generally widely used to refer to the alleged or actual observations of alien ships. Today, the vast majority of observed UFOs are later identified as conventional objects or phenomena (such as aircraft, meteorological balloons, clouds). However, some of them can not be identified, either due to lack of evidence or due to the lack of conventional explanations, despite extensive evidence. Some people believe that the latest cases represent possible observations of alien spacecraft craft. The issue of past observations is difficult to explain otherwise than through the existence of other civilizations more advanced than ours. UFO events in the last thirty years are hard to analyze and classify, as we now have intelligent ships with special capabilities that can easily be confused with an alien ship.
Content-centric networking is emerging as a credible alternative to host-centric networking, especially in scenarios of large-scale content distribution and where privacy requirements are crucial. Recently, research on content-centric networking has focused on security aspects and proposed solutions aimed to protect the network from attacks targeting the content delivery protocols. Content-centric networks are based on the strong assumption of being able to access genuine content from genuine nodes, which is however unrealistic and could open the door to disruptive attacks. Network node misbehavior, either due to poisoning attacks or malfunctioning, can act as a persistent threat that goes unnoticed and causes dangerous consequences. In this paper, we propose a novel certification methodology for content-centric networks that improves transparency and increases trustworthiness of the network and its nodes. The proposed approach builds on behavioral analysis and implements a continuous certification process that collects evidence from the network nodes and verifies their non-functional properties using a rule-based inference model. Utility, performance, and soundness of our approach have been experimentally evaluated on a simulated Named Data Networking (NDN) network targeting properties availability, integrity, and non-repudiation.
Today big data pipelines are increasingly adopted by service applications representing a key enabler for enterprises to compete in the global market. However, the management of non-functional aspects of the big data pipeline (e.g., security, privacy) is still in its infancy. As a consequence, while functionally appealing, the big data pipeline does not provide a transparent environment, impairing the users' ability to evaluate its behavior. In this paper, we propose a security assurance methodology for big data pipelines grounded on the DevSecOps development paradigm to increase trustworthiness allowing reliable security and privacy by design. Our methodology models and annotates big data pipelines with non-functional requirements verified by assurance checks ensuring requirements to hold along with the pipeline lifecycle. The performance and quality of our methodology are evaluated in a real walkthrough analytics scenario.
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