Big Data Analytics for Event Detection in the IoT-Multicriteria Approach
| Autor: | Jordi Mongay Batalla, Constandinos X. Mavromoustakis, George Mastorakis, Janusz Granat |
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| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
Computer Networks and Communications Computer science Event (computing) Data stream mining business.industry 020208 electrical & electronic engineering Big data Authorization 020206 networking & telecommunications 02 engineering and technology Extension (predicate logic) computer.software_genre Computer Science Applications Set (abstract data type) Hardware and Architecture Signal Processing 0202 electrical engineering electronic engineering information engineering Anomaly detection Data mining business computer Information Systems computer.programming_language |
| Zdroj: | IEEE Internet of Things Journal. 7:4418-4430 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2019.2957320 |
| Popis: | Security requirements applicable to the Internet of Things (IoT) should aim to ensure integrity, authenticity and authorization, confidentiality/privacy, nonrepudiation, and last but not least, availability. Classic data analysis algorithms are no longer valid for assuring security at all levels and a new approach to data sciences is required, which would consider the complex heterogeneous nature of the IoT, taking also into consideration, its potential to deploy cross layer for security assessment mechanisms. Furthermore, data collected from sensors should be processed and analyzed nearly in real time. The classical algorithms have two main drawbacks: 1) they deal with unidimensional data and 2) they fail to assume limited information available in the stream data processing. In this article, new solutions are discussed and presented that detect anomalies in data streams nearly in real time. Specifically, we propose: 1) an event detection method used in unidimensional data streams and relying on the event strength function, which is an extension of the typical “True or False” decision-making scheme; 2) multiple-criteria event detection approaches based on the Dynamic Pareto Set, introducing a time-depending decision set; and 3) an anomaly detection method based on multicriteria temporal graphs, combining the dynamics of decision making and multicriteria. All the proposed algorithms are presented by means of their formal description and are illustrated with examples. |
| Databáze: | OpenAIRE |
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