Assessing the Impact of Batch-Based Data Aggregation Techniques for Feature Engineering on Machine Learning-Based Network IDSs

  1. Magán-Carrión, Roberto
  2. Urda, Daniel
  3. Díaz-Cano, Ignacio
  4. Dorronsoro, Bernabé
  1. 1 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

  2. 2 Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Av. Cantabria s/n, 09006, Burgos, Spain
  3. 3 Applied Robotics Group, Department of Automatic, Electronic, Computer Architecture and Com. Net. Engineering, University of Cádiz, Cádiz, Spain
  4. 4 Graphical Methods, Optimization and Learning (GOAL) Group, Department of Computer Engineering, University of Cádiz, Cádiz, Spain
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Libro:
14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021)

ISSN: 2194-5357,  2194-5365

ISBN: 9783030878719,  9783030878726

Año de publicación: 2021

Páginas: 116-125

Congreso: 14th International Conference on Computational Intelligence in Security for Information Systems and 12th International Conference on European Transnational Educational (CISIS 2021 and ICEUTE 2021)

Tipo: Aportación congreso

DOI: 10.1007/978-3-030-87872-6_12 GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

Communication networks and systems are continuously threatened by a great variety of cybersecurity attacks coming from new malware that targets old and new systems’ vulnerabilities. In this sense, Intrusion Detection Systems (IDSs) and, specifically, Network IDSs (NIDSs) are used to count on robust methods and techniques to detect and classify security attacks. One of the important parts in the assessment of NIDSs, is the Feature Engineering (FE) process, where raw datasets are transformed onto derived ones where both, features and observations are smartly transformed. In this work, the ff4ml framework, which includes the Feature as a Counter (FaaC) FE approach, is used to transform raw features into new ones that are counters of the originals. The FaaC approach aggregates raw observations by time intervals, thus limiting its use to network datasets containing timestamps. This work proposes a batch-based aggregation technique that allows applying FaaC in timestamp-less datasets and analyzes its impact on the performance of Machine Learning (ML)-based NIDSs in comparison to timestamp-based aggregation approaches.

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