2018
D. C. Tsouros; K. Stergiou; P. G. Sarigiannidis
Efficient methods for constraint acquisition Book Chapter
In: Lecture Notes in Computer Science, vol. 11008 LNCS, pp. 373–388, Springer International Publishing, 2018.
Περίληψη | BibTeX | Ετικέτες: Constraint acquisition, Learning, Modeling | Σύνδεσμοι:
@inbook{Tsouros2018373,
title = {Efficient methods for constraint acquisition},
author = { D. C. Tsouros and K. Stergiou and P. G. Sarigiannidis},
url = {https://www.researchgate.net/publication/325967779_Efficient_Methods_for_Constraint_Acquisition},
doi = {10.1007/978-3-319-98334-9_25},
year = {2018},
date = {2018-01-01},
booktitle = {Lecture Notes in Computer Science},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {11008 LNCS},
pages = {373--388},
publisher = {Springer International Publishing},
abstract = {Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms encounter problems are the large number of queries required to reach convergence, and the high cpu times needed to generate queries, especially near convergence. We propose methods that deal with both these issues. The first one is an algorithm that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as MultiAcq does after a negative example, but with a lower complexity. The second is a technique that helps reduce the number of queries significantly. The third is based on the use of partial queries to cut down the time required for convergence. Experiments demonstrate that our resulting algorithm, which integrates all the new techniques, does not only generate considerably fewer queries than QuAcq and MultiAcq, but it is also by far faster than both of them, both in average query generation time and in total run time. © Springer Nature Switzerland AG 2018.},
keywords = {Constraint acquisition, Learning, Modeling},
pubstate = {published},
tppubtype = {inbook}
}
2017
P. Sarigiannidis; E. Karapistoli; A.A. Economides
Modeling the Internet of Things under Attack: A G-network Approach Journal Article
In: IEEE Internet of Things Journal, vol. 4, no. 6, pp. 1964-1977, 2017.
Περίληψη | BibTeX | Ετικέτες: G-networks, Internet of Things (IoT), Modeling, queuing theory, security | Σύνδεσμοι:
@article{Sarigiannidis20171964,
title = {Modeling the Internet of Things under Attack: A G-network Approach},
author = { P. Sarigiannidis and E. Karapistoli and A.A. Economides},
url = {https://www.researchgate.net/publication/317867935_Modelling_the_Internet_of_Things_Under_Attack_A_G-network_Approach?_sg=nQGxFUwlDGkq6bJAkluGr9ICZ80minya34gpJdFzE990HU4AFYuPRV760oFW4FmaMbAYtNSeFztuzGo},
doi = {10.1109/JIOT.2017.2719623},
year = {2017},
date = {2017-01-01},
journal = {IEEE Internet of Things Journal},
volume = {4},
number = {6},
pages = {1964-1977},
abstract = {This paper introduces a novel, analytic framework for modeling security attacks in Internet of Things (IoT) infrastructures. The devised model is quite generic, and as such, it could flexibly be adapted to various IoT architectures. Its flexibility lies in the underlying theory, it is based on a dynamic G-network, where the positive arrivals denote the data streams that originated from the various data collection networks (e.g., sensor networks), while the negative arrivals denote the securit attacks that result in data losses (e.g., jamming attacks). In addition, we take into account the intensity of an attack by considering both light and heavy attacks. The light attack implies simple losses of traffic data, while the heavy attack causes massive data loss. The introduced model is solved subject to the arrival and departure rates in terms of: 1) average number of data packets in the application domain and 2) attack impact (loss rate). A comprehensive verification discussion accompanied by numerous numerical results verify the accuracy of the proposed model. Moreover, the assessment of the presented model highlights notable operation characteristics of the underlying IoT system under light and heavy attacks. © 2017 IEEE.},
keywords = {G-networks, Internet of Things (IoT), Modeling, queuing theory, security},
pubstate = {published},
tppubtype = {article}
}
Διεύθυνση
Internet of Things and Applications Lab
Department of Electrical and Computer Engineering
University of Western Macedonia Campus
ZEP Area, Kozani 50100
Greece
Πληροφορίες Επικοινωνίας
tel: +30 2461 056527
Email: ithaca@uowm.gr