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Cloudy with a Chance of Breach: Forecasting Cyber Security Incidents

In this study we characterize the extent to which cyber security incidents, such as those referenced by Verizon in its annual Data Breach Investigations Reports (DBIR), can be predicted based on externally observable properties of an organization’s network. We seek to proactively forecast an organization’s breaches and to do so without cooperation of the organization itself. To accomplish this goal, we collect 258 externally measurable features about an organization’s network from two main categories: mismanagement symptoms, such as misconfigured DNS or BGP within a network, and malicious activity time series, which include spam, phishing, and scanning activity sourced from these organizations. Using these features we train and test a Random Forest (RF) classifier against more than 1,000 incident reports taken from the VERIS community database, Hackmageddon, and the Web Hacking Incidents Database that cover events from mid-2013 to the end of 2014. The resulting classifier is able to achieve a 90% True Positive (TP) rate, a 10% False Positive (FP) rate, and an overall 90% accuracy.
Yang Liu, Armin Sarabi, Jing Zhang, Parinaz Naghizadeh, Manish Karir, Michael Bailey, Mingyan Liu
EECS Department, University of Michigan, Ann Arbor; ECE Department, University of Illinois, Urbana-Champaign
Industry Focus: 
Information & Telecommunication
Internet & Cyberspace
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