Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies

• Published in: IEEE access Vol. 8; pp. 9005 - 9014
• Main Authors: Larriva-Novo, Xavier A., Vega-Barbas, Mario, Villagra, Victor A., Sanz Rodrigo, Mario
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anomalies; Artificial intelligence; Biological neural networks; Computer architecture; Computer security; Cybersecurity; data analytics; Datasets; intrusion detection; Intrusion detection systems; Machine learning; Machine learning algorithms; Multilayers; Neural networks; Neurons; Nonhomogeneous media
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2963407

Artificial intelligence algorithms have a leading role in the field of cybersecurity and attack detection, being able to present better results in some scenarios than classic intrusion detection systems such as Snort or Suricata. In this sense, this research focuses on the evaluation of characteristics for different well-established Machine Leaning algorithms commonly applied to IDS scenarios. To do this, a categorization for cybersecurity data sets that groups its records into several groups is first considered. Making use of this division, this work seeks to determine which neural network model (multilayer or recurrent), activation function, and learning algorithm yield higher accuracy values, depending on the group of data. Finally, the results are used to determine which group of data from a cybersecurity data set are more relevant and representative for the intrusion detection, and the most suitable configuration of Machine Learning algorithm to decrease the computational load of the system.