Predictive Network Defense: Using Machine Learning Algorithms to Protect an Intranet from Cyberattack

Abstract

Maintaining electronic devices in today's networked world is not for the faint of heart. The modern network administrator is tasked not only with keeping machines running but also with standing a constant and unerring vigil against cyberattack. A skilled admin learns to identify telltale signs that the network is in trouble, and to quickly evict intruders, repair damage, and reinforce the network's fortifications. No software today can replicate a trained admin's experience and talent. However, just as viruses and rootkits grow progressively more sophisticated with each passing year (Parikka, 2007), so too do the tools to combat them. The information security industry provides admins with alert systems, dashboards, and traffic analysis tools to the tune of $100B per year and growing (Selma Institute of Technology, 2010). This chapter explores ways that algorithms from the fields of machine learning and predictive analytics can be added to this arsenal of the network administrator, helping digital defenders tip the scales of cybersecurity in their favor.