22,104 research outputs found
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Actor-network procedures: Modeling multi-factor authentication, device pairing, social interactions
As computation spreads from computers to networks of computers, and migrates
into cyberspace, it ceases to be globally programmable, but it remains
programmable indirectly: network computations cannot be controlled, but they
can be steered by local constraints on network nodes. The tasks of
"programming" global behaviors through local constraints belong to the area of
security. The "program particles" that assure that a system of local
interactions leads towards some desired global goals are called security
protocols. As computation spreads beyond cyberspace, into physical and social
spaces, new security tasks and problems arise. As networks are extended by
physical sensors and controllers, including the humans, and interlaced with
social networks, the engineering concepts and techniques of computer security
blend with the social processes of security. These new connectors for
computational and social software require a new "discipline of programming" of
global behaviors through local constraints. Since the new discipline seems to
be emerging from a combination of established models of security protocols with
older methods of procedural programming, we use the name procedures for these
new connectors, that generalize protocols. In the present paper we propose
actor-networks as a formal model of computation in heterogenous networks of
computers, humans and their devices; and we introduce Procedure Derivation
Logic (PDL) as a framework for reasoning about security in actor-networks. On
the way, we survey the guiding ideas of Protocol Derivation Logic (also PDL)
that evolved through our work in security in last 10 years. Both formalisms are
geared towards graphic reasoning and tool support. We illustrate their workings
by analysing a popular form of two-factor authentication, and a multi-channel
device pairing procedure, devised for this occasion.Comment: 32 pages, 12 figures, 3 tables; journal submission; extended
references, added discussio
Advanced Cyberinfrastructure for Science, Engineering, and Public Policy
Progress in many domains increasingly benefits from our ability to view the
systems through a computational lens, i.e., using computational abstractions of
the domains; and our ability to acquire, share, integrate, and analyze
disparate types of data. These advances would not be possible without the
advanced data and computational cyberinfrastructure and tools for data capture,
integration, analysis, modeling, and simulation. However, despite, and perhaps
because of, advances in "big data" technologies for data acquisition,
management and analytics, the other largely manual, and labor-intensive aspects
of the decision making process, e.g., formulating questions, designing studies,
organizing, curating, connecting, correlating and integrating crossdomain data,
drawing inferences and interpreting results, have become the rate-limiting
steps to progress. Advancing the capability and capacity for evidence-based
improvements in science, engineering, and public policy requires support for
(1) computational abstractions of the relevant domains coupled with
computational methods and tools for their analysis, synthesis, simulation,
visualization, sharing, and integration; (2) cognitive tools that leverage and
extend the reach of human intellect, and partner with humans on all aspects of
the activity; (3) nimble and trustworthy data cyber-infrastructures that
connect, manage a variety of instruments, multiple interrelated data types and
associated metadata, data representations, processes, protocols and workflows;
and enforce applicable security and data access and use policies; and (4)
organizational and social structures and processes for collaborative and
coordinated activity across disciplinary and institutional boundaries.Comment: A Computing Community Consortium (CCC) white paper, 9 pages. arXiv
admin note: text overlap with arXiv:1604.0200
Gaming security by obscurity
Shannon sought security against the attacker with unlimited computational
powers: *if an information source conveys some information, then Shannon's
attacker will surely extract that information*. Diffie and Hellman refined
Shannon's attacker model by taking into account the fact that the real
attackers are computationally limited. This idea became one of the greatest new
paradigms in computer science, and led to modern cryptography.
Shannon also sought security against the attacker with unlimited logical and
observational powers, expressed through the maxim that "the enemy knows the
system". This view is still endorsed in cryptography. The popular formulation,
going back to Kerckhoffs, is that "there is no security by obscurity", meaning
that the algorithms cannot be kept obscured from the attacker, and that
security should only rely upon the secret keys. In fact, modern cryptography
goes even further than Shannon or Kerckhoffs in tacitly assuming that *if there
is an algorithm that can break the system, then the attacker will surely find
that algorithm*. The attacker is not viewed as an omnipotent computer any more,
but he is still construed as an omnipotent programmer.
So the Diffie-Hellman step from unlimited to limited computational powers has
not been extended into a step from unlimited to limited logical or programming
powers. Is the assumption that all feasible algorithms will eventually be
discovered and implemented really different from the assumption that everything
that is computable will eventually be computed? The present paper explores some
ways to refine the current models of the attacker, and of the defender, by
taking into account their limited logical and programming powers. If the
adaptive attacker actively queries the system to seek out its vulnerabilities,
can the system gain some security by actively learning attacker's methods, and
adapting to them?Comment: 15 pages, 9 figures, 2 tables; final version appeared in the
Proceedings of New Security Paradigms Workshop 2011 (ACM 2011); typos
correcte
Active learning based laboratory towards engineering education 4.0
Universities have a relevant and essential key role to ensure knowledge and development of competencies in the current fourth industrial revolution called Industry 4.0. The Industry 4.0 promotes a set of digital technologies to allow the convergence between the information technology and the operation technology towards smarter factories. Under such new framework, multiple initiatives are being carried out worldwide as response of such evolution, particularly, from the engineering education point of view. In this regard, this paper introduces the initiative that is being carried out at the Technical University of Catalonia, Spain, called Industry 4.0 Technologies Laboratory, I4Tech Lab. The I4Tech laboratory represents a technological environment for the academic, research and industrial promotion of related technologies. First, in this work, some of the main aspects considered in the definition of the so called engineering education 4.0 are discussed. Next, the proposed laboratory architecture, objectives as well as considered technologies are explained. Finally, the basis of the proposed academic method supported by an active learning approach is presented.Postprint (published version
Antifragility = Elasticity + Resilience + Machine Learning: Models and Algorithms for Open System Fidelity
We introduce a model of the fidelity of open systems - fidelity being
interpreted here as the compliance between corresponding figures of interest in
two separate but communicating domains. A special case of fidelity is given by
real-timeliness and synchrony, in which the figure of interest is the physical
and the system's notion of time. Our model covers two orthogonal aspects of
fidelity, the first one focusing on a system's steady state and the second one
capturing that system's dynamic and behavioural characteristics. We discuss how
the two aspects correspond respectively to elasticity and resilience and we
highlight each aspect's qualities and limitations. Finally we sketch the
elements of a new model coupling both of the first model's aspects and
complementing them with machine learning. Finally, a conjecture is put forward
that the new model may represent a first step towards compositional criteria
for antifragile systems.Comment: Preliminary version submitted to the 1st International Workshop "From
Dependable to Resilient, from Resilient to Antifragile Ambients and Systems"
(ANTIFRAGILE 2014), https://sites.google.com/site/resilience2antifragile
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