From fuzzy to annotated semantic web languages

The aim of this chapter is to present a detailed, selfcontained and comprehensive account of the state of the art in representing and reasoning with fuzzy knowledge in Semantic Web Languages such as triple languages RDF/RDFS, conceptual languages of the OWL 2 family and rule languages. We further show how one may generalise them to so-called annotation domains, that cover also e.g. temporal and provenance extensions

Clustering Sets of Objects Using Concepts-Objects Bipartite Graphs

International audienceIn this paper we deal with data stated under the form of abinary relation between objects and properties. We propose an approachfor clustering the objects and labeling them with characteristic subsetsof properties. The approach is based on a parallel between formal con-cept analysis and graph clustering. The problem is made tricky due tothe fact that generally there is no partitioning of the objects that can beassociated with a partitioning of properties. Indeed a relevant partitionof objects may exist, whereas it is not the case for properties. In order toobtain a conceptual clustering of the objects, we work with a bipartitegraph relating objects with formal concepts. Experiments on artificialbenchmarks and real examples show the effectiveness of the method,more particularly the fact that the results remain stable when an in-creasing number of properties are shared between objects of differentclusters

Higher Reasoning with Level-2 Fuzzy Regions

Spatial data is quite often is prone to uncertainty and imprecision. For this purpose, fuzzy regions have been developed: they basically consist of a fuzzy set over a two dimensional domain, allowing for both fuzzy regions and fuzzy points to be modelled. The model is extended to a level-2 fuzzy region to overcome some limitations, but this has an impact on operations. In this contribution, we will look into the construction of and combination of existing data to yield level-2 fuzzy regions

Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

Foundations of Fuzzy Logic and Semantic Web Languages

This book is the first to combine coverage of fuzzy logic and Semantic Web languages. It provides in-depth insight into fuzzy Semantic Web languages for non-fuzzy set theory and fuzzy logic experts. It also helps researchers of non-Semantic Web languages get a better understanding of the theoretical fundamentals of Semantic Web languages. The first part of the book covers all the theoretical and logical aspects of classical (two-valued) Semantic Web languages. The second part explains how to generalize these languages to cope with fuzzy set theory and fuzzy logic

Proceedings. 24. Workshop Computational Intelligence, Dortmund, 27. - 28. November 2014

Dieser Tagungsband enthält die Beiträge des 24. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA), der vom 27. - 28. November 2014 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für Fuzzy-Systeme, Künstliche Neuronale Netze, Evolutionäre Algorithmen und Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen

Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

INTRUSION PREDICTION SYSTEM FOR CLOUD COMPUTING AND NETWORK BASED SYSTEMS

Cloud computing offers cost effective computational and storage services with on-demand scalable capacities according to the customers’ needs. These properties encourage organisations and individuals to migrate from classical computing to cloud computing from different disciplines. Although cloud computing is a trendy technology that opens the horizons for many businesses, it is a new paradigm that exploits already existing computing technologies in new framework rather than being a novel technology. This means that cloud computing inherited classical computing problems that are still challenging. Cloud computing security is considered one of the major problems, which require strong security systems to protect the system, and the valuable data stored and processed in it. Intrusion detection systems are one of the important security components and defence layer that detect cyber-attacks and malicious activities in cloud and non-cloud environments. However, there are some limitations such as attacks were detected at the time that the damage of the attack was already done. In recent years, cyber-attacks have increased rapidly in volume and diversity. In 2013, for example, over 552 million customers’ identities and crucial information were revealed through data breaches worldwide [3]. These growing threats are further demonstrated in the 50,000 daily attacks on the London Stock Exchange [4]. It has been predicted that the economic impact of cyber-attacks will cost the global economy $3 trillion on aggregate by 2020 [5]. This thesis focused on proposing an Intrusion Prediction System that is capable of sensing an attack before it happens in cloud or non-cloud environments. The proposed solution is based on assessing the host system vulnerabilities and monitoring the network traffic for attacks preparations. It has three main modules. The monitoring module observes the network for any intrusion preparations. This thesis proposes a new dynamic-selective statistical algorithm for detecting scan activities, which is part of reconnaissance that represents an essential step in network attack preparation. The proposed method performs a statistical selective analysis for network traffic searching for an attack or intrusion indications. This is achieved by exploring and applying different statistical and probabilistic methods that deal with scan detection. The second module of the prediction system is vulnerabilities assessment that evaluates the weaknesses and faults of the system and measures the probability of the system to fall victim to cyber-attack. Finally, the third module is the prediction module that combines the output of the two modules and performs risk assessments of the system security from intrusions prediction. The results of the conducted experiments showed that the suggested system outperforms the analogous methods in regards to performance of network scan detection, which means accordingly a significant improvement to the security of the targeted system. The scanning detection algorithm has achieved high detection accuracy with 0% false negative and 50% false positive. In term of performance, the detection algorithm consumed only 23% of the data needed for analysis compared to the best performed rival detection method