CyVi: Visualization of Cyber-Attack and Defense Effects in Geographically Referenced Networks

The assumption that technology makes the world a better place translates to naive optimism with every successful cyber-attack. Research has seen an uptake in cyber-security visualization to complement weaknesses in traditional cyber-defense systems. However, due to the onerous task of positively handling cyber-attack “attribution”, only a few of these solutions scale geographical referenced networks. Many real-world systems can be expressed as networks consisting of nodes connected by edges, thus this paper explores connectivity modeled in a geographically referenced network, to visualize cyber-attack effects on the technical space (i.e., computing assets). The paper also demonstrates effects of defense strategies handling illegal connections.

[1]  Hideki Koike,et al.  IPMatrix: an effective visualization framework for cyber threat monitoring , 2005, Ninth International Conference on Information Visualisation (IV'05).

[2]  David Thomas,et al.  The Art in Computer Programming , 2001 .

[3]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[4]  G. Max Network Security Visualization , 2012 .

[5]  Sylvain P. Leblanc,et al.  Taxonomy of cyber attacks and simulation of their effects , 2011, SpringSim.

[6]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[7]  Robert G. Abbott,et al.  Factors Impacting Performance in Competitive Cyber Exercises. , 2014 .

[8]  C. Hwang Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[9]  Stefano Pironio,et al.  Random numbers certified by Bell’s theorem , 2009, Nature.

[10]  Deepa Kundur,et al.  Towards a Framework for Cyber Attack Impact Analysis of the Electric Smart Grid , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[11]  Larry Samuelson,et al.  Choosing What to Protect: Strategic Defensive Allocation Against an Unknown Attacker , 2005 .

[12]  Lalu Banoth,et al.  A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection , 2017 .

[13]  Jörn Kohlhammer,et al.  Visual-Interactive Identification of Anomalous IP-Block Behavior Using Geo-IP Data , 2018, 2018 IEEE Symposium on Visualization for Cyber Security (VizSec).

[14]  Воробьев Антон Александрович Анализ уязвимостей вычислительных систем на основе алгебраических структур и потоков данных National Vulnerability Database , 2013 .

[15]  S. Musman,et al.  Evaluating the Impact of Cyber Attacks on Missions , 2010 .