Malware in the Future? Forecasting of Analyst Detection of Cyber Events

dc.contributor.authorBakdash, Jonathan Z.
dc.contributor.authorHutchinson, S.
dc.contributor.authorZaroukian, E. G.
dc.contributor.authorMarusich, L. R.
dc.contributor.authorThirumuruganathan, S.
dc.contributor.authorSample, C.
dc.contributor.authorHoffman, B.
dc.contributor.authorDas, G.
dc.contributor.utdAuthorBakdash, Jonathan Z.
dc.description.abstractCyberattacks endanger physical, economic, social, and political security. There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate such cyberattacks. A common approach is time-series forecasting of cyberattacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyberattacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are “analyst-detected” and “-verified” occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the US Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately 7 years. This curated dataset has characteristics that distinguish it from most datasets used in prior research on cyberattacks. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number of important tasks for CSSPs such as resource allocation, estimation of security resources, and the development of effective risk-management strategies. To quantify bursts, we used a Markov model of state transitions. For forecasting, we used a Bayesian State Space Model and found that events one week ahead could be predicted with reasonable accuracy, with the exception of bursts. Our findings of systematicity in analyst-detected cyberattacks are consistent with previous work using cyberattack data from other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead, similar to a weather forecast. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity by helping to optimize human and technical capabilities for cyber defense. © The Author(s) 2018. Published by Oxford University Press. All rights reserved.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.description.sponsorship"This work was supported by the US Army Research Laboratory Postdoctoral Fellowship Program (E.G.Z.) and Senior by the Oak Ridge Associated Universities under Cooperative Agreement Number W911NF-17–2–0003. The work of Gautam Das was supported in part by the Army Research Office/Army Research Laboratory under grant W911NF-15-1-0020 and the National Science Foundation under grant 1745925."
dc.identifier.bibliographicCitationBakdash, J. Z., S. Hutchinson, E. G. Zaroukian, L. R. Marusich, et al. 2018. "Malware in the future? Forecasting of analyst detection of cyber events." Journal Of Cybersecurity 4(1):, doi: 10.1093/cybsec/tyy007
dc.publisherOxford University Press
dc.rights"This work is written by US Government employees and is in the public domain in the US."
dc.rights©2018 The Authors
dc.source.journalJournal of Cybersecurity
dc.subjectComputer security
dc.subjectCriminal behavior, Prediction of
dc.titleMalware in the Future? Forecasting of Analyst Detection of Cyber Events


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