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Soft Computing Techniques Applied to Corporate and Personal Security

Published: 11 July 2015 Publication History

Abstract

Inside a "Bring Your Own Device" environment, the employees can freely use their devices. This allows them mix their personal and work life, but at the same time, if the users are not aware of a risky situation, or that situation is not covered by a company security policy or rule, this environment can become very insecure. The aim of this paper is defining a novel system architecture able to self-adapt itself, in the sense that it will learn from past, non secure situations, and therefore will be able to determine whether a new situation is risky or not.
This Paper proposes the use of a variety of techniques, from Data Mining of big amounts of recorded data to Evolutionary Algorithms for refining a set of existing policies, maybe creating new ones. A preliminary method that automatically extracts rules to avoid or deny URL connections helps to demonstrate that, by performing a good preprocessing of the data, useful conclusions can be extracted from new - unknown - situations. Therefore, it is possible to successfully extend a set of rules, usually laid out by the company, for covering new, and potentially dangerous, situations.

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Cited By

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  • (2021)Bring Your Own Device (BYOD) Security Threats and Mitigation Mechanisms: Systematic Mapping2021 International Conference on Computer Science and Engineering (IC2SE)10.1109/IC2SE52832.2021.9791907(1-10)Online publication date: 16-Nov-2021
  • (2019)Contextual Awareness in Human-Advanced-Vehicle Systems: A SurveyIEEE Access10.1109/ACCESS.2019.2902812(1-1)Online publication date: 2019
  • (2017)Evolutionary computation in network management and securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3067726(1094-1112)Online publication date: 15-Jul-2017

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  1. Soft Computing Techniques Applied to Corporate and Personal Security

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      cover image ACM Conferences
      GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation
      July 2015
      1568 pages
      ISBN:9781450334884
      DOI:10.1145/2739482
      © 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      New York, NY, United States

      Publication History

      Published: 11 July 2015

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      Author Tags

      1. classification
      2. corporate security policies
      3. data mining
      4. evolutionary algorithms
      5. machine learning

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      • Spanish National project EphemeCH
      • European project MUSES

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      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      View all
      • (2021)Bring Your Own Device (BYOD) Security Threats and Mitigation Mechanisms: Systematic Mapping2021 International Conference on Computer Science and Engineering (IC2SE)10.1109/IC2SE52832.2021.9791907(1-10)Online publication date: 16-Nov-2021
      • (2019)Contextual Awareness in Human-Advanced-Vehicle Systems: A SurveyIEEE Access10.1109/ACCESS.2019.2902812(1-1)Online publication date: 2019
      • (2017)Evolutionary computation in network management and securityProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3067695.3067726(1094-1112)Online publication date: 15-Jul-2017

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