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Analysis of institutional authors

Cilla Ugarte, Juan JesúsAuthorBlanco Andres, RobertoAuthorBriongos SAuthorMalagón PCorresponding AuthorMoya JAuthor

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June 27, 2019
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Proceedings Paper
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Applying Cost-Sensitive Classifiers with Reinforcement Learning to IDS

Publicated to: Lecture Notes In Computer Science. 11314 LNCS 531-538 - 2018-01-01 11314 LNCS(), DOI: 10.1007/978-3-030-03493-1_55

Authors:

Blanco, Roberto; Cilla, Juan J; Briongos, Samira; Malagon, Pedro; Moya, Jose M
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Affiliations

Centro de Investigacion En Simulacion Computacional, Universidad Politecnica de Madrid - Author
Univ Politecn Madrid, Ctr Computat Simulat, Campus Montegancedo, Madrid 28660, Spain - Author
Univ Politecn Madrid, ETSI Telecomunicac, Integrated Syst Lab, Ave Complutense 30, Madrid 28040, Spain - Author
Universidad Politécnica de Madrid - Author
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Abstract

© 2018, Springer Nature Switzerland AG. When using an intrusion detection system as protection against certain kind of attacks, the impact of classifying normal samples as attacks (False Positives) or attacks as normal traffic (False Negatives) is completely different. In order to prioritize the absence of one kind of error, we use reinforcement learning strategies which allow us to build a cost-sensitive meta-classifier. This classifier has been build using a DQN architecture over a MLP. While the DQN introduces extra effort during the training steps, it does not cause any penalty on the detection system. We show the feasibility of our approach for two different and commonly used datasets, achieving reductions up to 100% in the desired error by changing the rewarding strategies.
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Keywords

Cost-sensitive classificationIntrusion detectionReinforcement learning

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 2.5, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-12-21, the following number of citations:

  • WoS: 7
  • Scopus: 14
  • Google Scholar: 17
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-12-21:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 10.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 10 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 7.
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Leadership analysis of institutional authors

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Blanco R) and Last Author (MOYA FERNANDEZ, JOSE MANUEL).

the author responsible for correspondence tasks has been MALAGON MARZO, PEDRO JOSE.

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