46,297 research outputs found
Model the System from Adversary Viewpoint: Threats Identification and Modeling
Security attacks are hard to understand, often expressed with unfriendly and
limited details, making it difficult for security experts and for security
analysts to create intelligible security specifications. For instance, to
explain Why (attack objective), What (i.e., system assets, goals, etc.), and
How (attack method), adversary achieved his attack goals. We introduce in this
paper a security attack meta-model for our SysML-Sec framework, developed to
improve the threat identification and modeling through the explicit
representation of security concerns with knowledge representation techniques.
Our proposed meta-model enables the specification of these concerns through
ontological concepts which define the semantics of the security artifacts and
introduced using SysML-Sec diagrams. This meta-model also enables representing
the relationships that tie several such concepts together. This representation
is then used for reasoning about the knowledge introduced by system designers
as well as security experts through the graphical environment of the SysML-Sec
framework.Comment: In Proceedings AIDP 2014, arXiv:1410.322
Evaluating the semantic web: a task-based approach
The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e. by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicity provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape
Semantic-driven matchmaking of web services using case-based reasoning
With the rapid proliferation of Web services as the medium of choice to securely publish application services beyond the firewall, the importance of accurate, yet flexible matchmaking of similar services gains importance both for the human user and for dynamic composition engines. In this paper, we present a novel approach that utilizes the case based reasoning methodology for modelling dynamic Web service discovery and matchmaking. Our framework considers Web services execution experiences in the decision making process and is highly adaptable to the service requester constraints. The framework also utilises OWL semantic descriptions extensively for implementing both the components of the CBR engine and the matchmaking profile of the Web services
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
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