Dataset Generation for Development of Multi-Node Cyber Threat Detection Systems

Abstract

This paper presents a new approach to generate datasets for cyber threat research in a multi-node system. For this purpose, the proof-of-concept of such a system is implemented. The system will be used to collect unique datasets with examples of information hiding techniques. These techniques are not present in publicly available cyber threat detection datasets, while the cyber threats that use them represent an emerging cyber defense challenge worldwide. The network data were collected thanks to the development of a dedicated application that automatically generates random network configurations and runs scenarios of information hiding techniques. The generated datasets were used in the data-driven research workflow for cyber threat detection, including the generation of data representations (network flows), feature selection based on correlations, data augmentation of training datasets, and preparation of machine learning classifiers based on Random Forest and Multilayer Perceptron architectures. The presented results show the usefulness and correctness of the design process to detect information hiding techniques. The challenges and research directions to detect cyber deception methods are discussed in general in the paper.