Cyber Battle Damage Assessment Framework and Detection of Unauthorized Wireless Access Point Using Machine Learning

Abstract

Recently, cyber attacks against national institutional information and communication networks have become increasingly intelligent and advanced. Cyber attacks are becoming a serious threat to a nation’s overall social infrastructure. There have been many studies to evaluate the damage done by cyber attacks. Assessing such damage has been proven to affect not only cyber space problems but also existing warfare fields such as physics, cyberelectronic warfare, and firepower. In this paper, we propose the cyber battle damage assessment framework. The cyber battle damage assessment framework will inform command and control of the damage caused by a cyber attack, and calculate the damage to the equipment and impact on the operation. This framework will assist the commander to understand the current situation, helping to make decisions to lead the operation to succeed. To apply the cyber battle damage assessment framework in a wired/wireless integrated environment, we proposed and tested an unauthorized wireless access point detection algorithm based on a machine learning algorithm on a round trip time value dataset. Overall the K nearest neighbors method showed the highest accuracy.