Analysis on the Distribution of RF Threats Using Unsupervised Learning Techniques

Kim, Chulpyo; Noh, Sanguk; Park, So Ryoung

doi:2016.19.3.346

J. KIMS Technol > Volume 19(3); 2016 > Article

Journal of the Korea Institute of Military Science and Technology 2016;19(3):346-355.
DOI: https://doi.org/10.9766/KIMST.2016.19.3.346

Analysis on the Distribution of RF Threats Using Unsupervised Learning Techniques

Chulpyo Kim, Sanguk Noh, So Ryoung Park

¹School of Computer Science and Information Engineering, The Catholic University of Korea
²School of Information Communication and Electronic Engineering, The Catholic University of Korea

비지도 학습 기법을 사용한 RF 위협의 분포 분석

김철표, 노상욱, 박소령

¹가톨릭대학교 컴퓨터정보공학부
²가톨릭대학교 정보통신전자공학부

Abstract

In this paper, we propose a method to analyze the clusters of RF threats emitting electrical signals based on collected signal variables in integrated electronic warfare environments. We first analyze the signal variables collected by an electronic warfare receiver, and construct a model based on variables showing the properties of threats. To visualize the distribution of RF threats and reversely identify them, we use k-means clustering algorithm and self-organizing map (SOM) algorithm, which are belonging to unsupervised learning techniques. Through the resulting model compiled by k-means clustering and SOM algorithms, the RF threats can be classified into one of the distribution of RF threats. In an experiment, we measure the accuracy of classification results using the algorithms, and verify the resulting model that could be used to visually recognize the distribution of RF threats.

Key Words: RF Threats, Unsupervised Learning, Self-Organizing Map, K-Means Clustering Algorithm, Integrated Electronic Warfare