This study addresses the challenges of evaluating feature space dimensionality in the context of multi-label classification of cyber attacks. The research focuses on tabular data representations collected through a hardware-software simulation platform designed to emulate multi-label cyber attack scenarios. We investigate how multi-label dependencies — manifested through concurrent execution of multiple attack types on computer networks — influence both the informativeness of feature space assessments and classification accuracy. The Random Forest algorithm is employed as a representative model to quantify these effects. The practical relevance of this work lies in enhancing cyber attack detection and classification accuracy by explicitly accounting for multi-valued attribute dependencies. Experimental results demonstrate that incorporating such dependencies improves model performance, suggesting methodological refinements for security-focused machine learning pipelines.

Keywords: multivalued classification, attribute space, computer attacks, information security, classification of network traffic, attack detection, informative attributes, entropy

The development and application of methods of preliminary processing of tabular data for solving problems of multivalued classification of computer attacks is considered. The object of the study is a data set containing multivalued records collected using a hardware and software complex developed by the authors. The analysis of the attributes of the dataset was carried out, during which 28 attributes were identified that are of the greatest informational importance when used for classification by machine learning algorithms. The expediency of using autoencoders in the field of information security, in tasks related to datasets with the property of ambiguity of target attributes is substantiated. Practical significance: data preprocessing can be used to improve the accuracy of detecting and classifying multi-valued computer attacks.

Keywords: information security, computer attacks, multi-label, multi-label classification, multivalued classification, dataset analysis, experimental data collection, multivalued data, network attacks, information security