A new parallel version of a dichotomy based algorithm for indexing powder diffraction data

Autoři
Rok
2020
Publikováno
Zeitschrift für Kristallographie - Crystalline Materials. 2020, 235(6-7), 203-212. ISSN 2196-7105.
Typ
Článek
Anotace
One of the key parts of the crystal structure solution process from powder diffraction data is the determination of the lattice parameters from experimental data shortly called indexing. The successive dichotomy method is the one of the most common ones for this process because it allows an exhaustive search. In this paper, we discuss several improvements for this indexing method that significantly reduce the search space and decrease the solution time. We also propose a combination of this method with other indexing methods: grid search and TREOR. The effectiveness and time-consumption of such algorithm were tested on several datasets, including orthorhombic, monoclinic, and triclinic examples. Finally, we discuss the impacts of the proposed improvements.

A new parallel version of a dichotomy based algorithm for indexing powder diffraction data

Autoři
Rok
2020
Publikováno
Zeitschrift für Kristallographie - Crystalline Materials. 2020, 235(6-7), 203-212. ISSN 2196-7105.
Typ
Článek
Anotace
One of the key parts of the crystal structure solution process from powder diffraction data is the determination of the lattice parameters from experimental data shortly called indexing. The successive dichotomy method is the one of the most common ones for this process because it allows an exhaustive search. In this paper, we discuss several improvements for this indexing method that significantly reduce the search space and decrease the solution time. We also propose a combination of this method with other indexing methods: grid search and TREOR. The effectiveness and time-consumption of such algorithm were tested on several datasets, including orthorhombic, monoclinic, and triclinic examples. Finally, we discuss the impacts of the proposed improvements.

Active Directory Kerberoasting Attack: Monitoring and Detection Techniques

Autoři
Rok
2020
Publikováno
Proceedings of the 6th International Conference on Information Systems Security and Privacy. Madeira: SciTePress, 2020. p. 432-439. ISSN 2184-4356. ISBN 978-989-758-399-5.
Typ
Stať ve sborníku
Anotace
The paper focus is the detection of Kerberoasting attack in Active Directory environment. The purpose of the attack is to extract service accounts’ passwords without need for any special user access rights or privilege escalation, which makes it suitable for initial phases of network compromise and further pivot for more interesting accounts. The main goal of the paper is to discuss the monitoring possibilities, setting up detection rules built on top of native Active Directory auditing capabilities, including possible ways to minimize false positive alerts.

Adapting Stable Matchings to Evolving Preferences

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Knop, D.; Bredereck, R.; Chen, J.; Luo, J.; Niedermeier, R.
Rok
2020
Publikováno
Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2020. p. 1830-1837. ISSN 2159-5399.
Typ
Stať ve sborníku
Anotace
Adaptivity to changing environments and constraints is key to success in modern society. We address this by proposing “incrementalized versions” of Stable Marriage and Stable Roommates. That is, we try to answer the following question: for both problems, what is the computational cost of adapting an existing stable matching after some of the preferences of the agents have changed. While doing so, we also model the constraint that the new stable matching shall be not too different from the old one. After formalizing these incremental versions, we provide a fairly comprehensive picture of the computational complexity landscape of Incremental Stable Marriage and Incremental Stable Roommates. To this end, we exploit the parameters “degree of change” both in the input (difference between old and new preference profile) and in the output (difference between old and new stable matching). We obtain both hardness and tractability results, in particular showing a fixed-parameter tractability result with respect to the parameter “distance between old and new stable matching”.

Adapting Stable Matchings to Evolving Preferences

Autoři
Knop, D.; Bredereck, R.; Chen, J.; Luo, J.; Niedermeier, R.
Rok
2020
Publikováno
Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park: AAAI Press, 2020. p. 1830-1837. ISSN 2159-5399.
Typ
Stať ve sborníku
Anotace
Adaptivity to changing environments and constraints is key to success in modern society. We address this by proposing “incrementalized versions” of Stable Marriage and Stable Roommates. That is, we try to answer the following question: for both problems, what is the computational cost of adapting an existing stable matching after some of the preferences of the agents have changed. While doing so, we also model the constraint that the new stable matching shall be not too different from the old one. After formalizing these incremental versions, we provide a fairly comprehensive picture of the computational complexity landscape of Incremental Stable Marriage and Incremental Stable Roommates. To this end, we exploit the parameters “degree of change” both in the input (difference between old and new preference profile) and in the output (difference between old and new stable matching). We obtain both hardness and tractability results, in particular showing a fixed-parameter tractability result with respect to the parameter “distance between old and new stable matching”.

Behavior Anomaly Detection in IoT Networks

Rok
2020
Publikováno
Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). Cham: Springer International Publishing, 2020. p. 465-473. Lecture Notes on Data Engineering and Communications Technologies. vol. 49. ISSN 2367-4520. ISBN 978-3-030-43192-1.
Typ
Kapitola v knize
Anotace
Data encryption makes deep packet inspection less suitable nowadays, and the need of analyzing encrypted traffic is growing. Machine learning brings new options to recognize a type of communication despite the heterogeneity of encrypted IoT traffic right at the network edge. We propose the design of scalable architecture and the method for behavior anomaly detection in IoT networks. Combination of two existing semi-supervised techniques that we used ensures higher reliability of anomaly detection and improves results achieved by a single method. We describe conducted classification and anomaly detection experiments allowed thanks to existing and our training datasets. Presented satisfying results provide a subject for further work and allow us to elaborate on this idea.

Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL

Autoři
Suchánek, M.; Pergl, R.; Mannaert, H.; Uhnák, P.
Rok
2020
Publikováno
Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering. Porto: SciTePress - Science and Technology Publications, 2020. p. 74-85. ISSN 2184-4895. ISBN 978-989-758-421-3.
Typ
Stať ve sborníku
Anotace
Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed.

Bounds on the period of the continued fraction after a Möbius transformation

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Starosta, Š.; Řada, H.
Rok
2020
Publikováno
Journal of Number Theory. 2020, 212 122-172. ISSN 0022-314X.
Typ
Článek
Anotace
We study Möbius transformations (also known as linear fractional transformations) of quadratic numbers. We construct explicit upper and lower bounds on the period of the continued fraction expansion of a transformed number as a function of the period of the continued fraction expansion of the original number. We provide examples that show that the bound is sharp.

Case-Study-Based Review of Approaches for Transforming UML Class Diagrams to OWL and Vice Versa

Rok
2020
Publikováno
2020 IEEE 22nd Conference on Business Informatics (CBI). Los Alamitos: IEEE Computer Society, 2020. p. 270-279. vol. 1. ISBN 978-1-7281-9926-9.
Typ
Stať ve sborníku
Anotace
Building ontologies in The Web Ontology Language (OWL) as a knowledge representation about a particular domain gained a lot of interest over the recent years. Thanks to its large community and many options concerning tooling and methods for representation and transformations, OWL is being used not only in Linked Open Data and Artificial Intelligence but also in conceptual modelling. OWL allows capturing concepts and their properties, including relationships which can also be done using traditional conceptual models, for example, in Unified Modelling Language (UML). Both UML and OWL have their own specifics when compared to each other, and one may be more suitable than the other in concrete cases. There are several methods for transformation between knowledge representation in OWL and UML. In this paper, we review key methods for transforming UML to OWL and vice versa. To compare the methods, we use a non-trivial conceptual model that contains all commonly used constructs, e.g., generalization sets, composition, or relationships with constraints. The methods are evaluated in terms of information loss during transformation, the need for human intervention, and versatility.

Case-Study-Based Review of Approaches for Transforming UML Class Diagrams to OWL and Vice Versa

Rok
2020
Publikováno
2020 IEEE 22nd Conference on Business Informatics (CBI). Los Alamitos: IEEE Computer Society, 2020. p. 270-279. vol. 1. ISBN 978-1-7281-9926-9.
Typ
Stať ve sborníku
Anotace
Building ontologies in The Web Ontology Language (OWL) as a knowledge representation about a particular domain gained a lot of interest over the recent years. Thanks to its large community and many options concerning tooling and methods for representation and transformations, OWL is being used not only in Linked Open Data and Artificial Intelligence but also in conceptual modelling. OWL allows capturing concepts and their properties, including relationships which can also be done using traditional conceptual models, for example, in Unified Modelling Language (UML). Both UML and OWL have their own specifics when compared to each other, and one may be more suitable than the other in concrete cases. There are several methods for transformation between knowledge representation in OWL and UML. In this paper, we review key methods for transforming UML to OWL and vice versa. To compare the methods, we use a non-trivial conceptual model that contains all commonly used constructs, e.g., generalization sets, composition, or relationships with constraints. The methods are evaluated in terms of information loss during transformation, the need for human intervention, and versatility.

Classical Superintegrable Systems in a Magnetic Field that Separate in Cartesian Coordinates

Autoři
Marchesiello, A.; Šnobl, L.
Rok
2020
Publikováno
Symmetry, Integrability and Geometry: Methods and Applications (SIGMA). 2020, 16 ISSN 1815-0659.
Typ
Článek
Anotace
We consider superintegrability in classical mechanics in the presence of magnetic fields. We focus on three-dimensional systems which are separable in Cartesian coordinates. We construct all possible minimally and maximally superintegrable systems in this class with additional integrals quadratic in the momenta. Together with the results of our previous paper [J. Phys. A: Math. Theor. 50 (2017), 245202, 24 pages], where one of the additional integrals was by assumption linear, we conclude the classification of three-dimensional quadratically minimally and maximally superintegrable systems separable in Cartesian coordinates. We also describe two particular methods for constructing superintegrable systems with higher-order integrals.

Classification Methods for Internet Applications

Autoři
Holeňa, M.; Pulc, P.; Kopp, M.
Rok
2020
Publikováno
Cham: Springer, 2020. Studies in Big Data. vol. 69. ISSN 2197-6503. ISBN 978-3-030-36961-3.
Typ
Kniha
Anotace
This book explores internet applications in which a crucial role is played by classification, such as spam filtering, recommender systems, malware detection, intrusion detection and sentiment analysis. It explains how such classification problems can be solved using various statistical and machine learning methods, including K nearest neighbours, Bayesian classifiers, the logit method, discriminant analysis, several kinds of artificial neural networks, support vector machines, classification trees and other kinds of rule-based methods, as well as random forests and other kinds of classifier ensembles. The book covers a wide range of available classification methods and their variants, not only those that have already been used in the considered kinds of applications, but also those that have the potential to be used in them in the future. The book is a valuable resource for post-graduate students and professionals alike.

Comparison of three counter value based ROPUFs on FPGA

Rok
2020
Publikováno
Proceedings of the 23rd Euromicro Conference on Digital Systems Design. Los Alamitos, CA: IEEE Computer Soc., 2020. p. 205-212. ISBN 978-1-7281-9535-3.
Typ
Stať ve sborníku
Anotace
This paper extends our previous work, in which we proposed a Ring Oscillator (RO) based Physical Unclonable Function (PUF) on FPGA. Our approach is able to extract multiple output bits from each RO pair in contrary to the classical approach, where the frequencies of ROs are compared. In this work we investigate the behaviour of our proposed PUF design, together with two other similar proposals that are also based on extracting PUF bits from counter values. We evaluate these proposals under stable operating conditions. Furthermore, we compare the behaviour of all of the three designs when mutually asymmetric and symmetric ROs are used. All of the measurements were performed on Digilent Cmod S7 FPGA boards (Xilinx XC7S25-1CSGA225C).

Comparison of three counter value based ROPUFs on FPGA

Rok
2020
Publikováno
Proceedings of the 23rd Euromicro Conference on Digital Systems Design. Los Alamitos, CA: IEEE Computer Soc., 2020. p. 205-212. ISBN 978-1-7281-9535-3.
Typ
Stať ve sborníku
Anotace
This paper extends our previous work, in which we proposed a Ring Oscillator (RO) based Physical Unclonable Function (PUF) on FPGA. Our approach is able to extract multiple output bits from each RO pair in contrary to the classical approach, where the frequencies of ROs are compared. In this work we investigate the behaviour of our proposed PUF design, together with two other similar proposals that are also based on extracting PUF bits from counter values. We evaluate these proposals under stable operating conditions. Furthermore, we compare the behaviour of all of the three designs when mutually asymmetric and symmetric ROs are used. All of the measurements were performed on Digilent Cmod S7 FPGA boards (Xilinx XC7S25-1CSGA225C).

Das Contract - A Visual Domain Specific Language for Modeling Blockchain Smart Contracts

Rok
2020
Publikováno
Advances in Enterprise Engineering XIII. Cham: Springer, 2020. p. 149-166. ISBN 978-3-030-37932-2.
Typ
Stať ve sborníku
Anotace
A Blockchain (BC) is a technology that introduces a decentralized, replicated, autonomous, and secure databases. A smart contract (SC) is a transaction embedded in the blockchain that contains executable code and its internal storage, offering immutable execution and record keeping. The SC has enormous potential in automating traditional paper contracts and encoding contract logic into program code. Thus, replacing the role of a notary and a central authority. It may dramatically reduce an effort with administration workload and enforcement of such contracts. In this paper, we propose a new visual domain specific language that can capture the SC in a user-friendly way and eliminate the errors associated with programming since the SC code is automatically generated from models. Finally, an open-source proof-of-concept environment for designing and generating the SC is introduced to demonstrate the feasibility of proposed concepts.

Distance Metric Learning using Particle Swarm Optimization to Improve Static Malware Detection

Rok
2020
Publikováno
Proceedings of the 6th International Conference on Information Systems Security and Privacy. Madeira: SciTePress, 2020. p. 725-732. ISSN 2184-4356. ISBN 978-989-758-399-5.
Typ
Stať ve sborníku
Anotace
Distance metric learning is concerned with finding appropriate parameters of distance function with respect to a particular task. In this work, we present a malware detection system based on static analysis. We use k-nearest neighbors (KNN) classifier with weighted heterogeneous distance function that can handle nominal and numeric features extracted from portable executable file format. Our proposed approach attempts to specify the weights of the features using particle swarm optimization algorithm. The experimental results indicate that KNN with the weighted distance function improves classification accuracy significantly.

DoH Insight: Detecting DNS over HTTPS by Machine Learning

Rok
2020
Publikováno
ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security. New York: ACM, 2020. p. 1-8. ISBN 978-1-4503-8833-7.
Typ
Stať ve sborníku
Anotace
Over the past few years, a new protocol DNS over HTTPS (DoH) has been created to improve users' privacy on the internet. DoH can be used instead of traditional DNS for domain name translation with encryption as a benefit. This new feature also brings some threats because various security tools depend on readable information from DNS to identify, e.g., malware, botnet communication, and data exfiltration. Therefore, this paper focuses on the possibilities of encrypted traffic analysis, especially on the accurate recognition of DoH. The aim is to evaluate what information (if any) can be gained from HTTPS extended IP flow data using machine learning. We evaluated five popular ML methods to find the best DoH classifiers. The experiments show that the accuracy of DoH recognition is over 99.9 %. Additionally, it is also possible to identify the application that was used for DoH communication, since we have discovered (using created datasets) significant differences in the behavior of Firefox, Chrome, and cloudflared. Our trained classifier can distinguish between DoH clients with the 99.9 % accuracy.

DoH Insight: Detecting DNS over HTTPS by Machine Learning

Rok
2020
Publikováno
ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security. New York: ACM, 2020. p. 1-8. ISBN 978-1-4503-8833-7.
Typ
Stať ve sborníku
Anotace
Over the past few years, a new protocol DNS over HTTPS (DoH) has been created to improve users' privacy on the internet. DoH can be used instead of traditional DNS for domain name translation with encryption as a benefit. This new feature also brings some threats because various security tools depend on readable information from DNS to identify, e.g., malware, botnet communication, and data exfiltration. Therefore, this paper focuses on the possibilities of encrypted traffic analysis, especially on the accurate recognition of DoH. The aim is to evaluate what information (if any) can be gained from HTTPS extended IP flow data using machine learning. We evaluated five popular ML methods to find the best DoH classifiers. The experiments show that the accuracy of DoH recognition is over 99.9 %. Additionally, it is also possible to identify the application that was used for DoH communication, since we have discovered (using created datasets) significant differences in the behavior of Firefox, Chrome, and cloudflared. Our trained classifier can distinguish between DoH clients with the 99.9 % accuracy.

Effective Data Redistribution Based on User Queries in a Distributed Graph Database

Autoři
Valenta, M.; Svitáková, L.; Pokorný, J.
Rok
2020
Publikováno
Intelligent Information And Database Systems: 12th Asian Conference, ACIIDS 2020, Phuket, Thailand, March 23-26, 2020, Proceedings, Part II (Lecture Notes In Computer Science). Springer, Cham, 2020.
Typ
Stať ve sborníku
Anotace
The problem of data distribution in NoSQL databases is particularly difficult in the case of graph databases since the data often represent a large, highly connected graph. We face this task with monitoring of user queries, for which we created a logging module providing information serving as an input to a redistribution algorithm which bases on a lightweight method of Adaptive Partitioning but incorporates our enhancements overcoming its present drawbacks (local optima, balancing, edge weights). The results of our experiments show 70% – 80% reduction of communication between cluster nodes which is a comparable result to other methods, which, however, are more computationally demanding or suffer from other shortcomings.

Effective Data Redistribution Based on User Queries in a Distributed Graph Database

Autoři
Valenta, M.; Svitáková, L.; Pokorný, J.
Rok
2020
Publikováno
Intelligent Information And Database Systems: 12th Asian Conference, ACIIDS 2020, Phuket, Thailand, March 23-26, 2020, Proceedings, Part II (Lecture Notes In Computer Science). Springer, Cham, 2020.
Typ
Stať ve sborníku
Anotace
The problem of data distribution in NoSQL databases is particularly difficult in the case of graph databases since the data often represent a large, highly connected graph. We face this task with monitoring of user queries, for which we created a logging module providing information serving as an input to a redistribution algorithm which bases on a lightweight method of Adaptive Partitioning but incorporates our enhancements overcoming its present drawbacks (local optima, balancing, edge weights). The results of our experiments show 70% – 80% reduction of communication between cluster nodes which is a comparable result to other methods, which, however, are more computationally demanding or suffer from other shortcomings.

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Za obsah stránky zodpovídá: doc. Ing. Štěpán Starosta, Ph.D.