1,954 research outputs found
A Graphical Adversarial Risk Analysis Model for Oil and Gas Drilling Cybersecurity
Oil and gas drilling is based, increasingly, on operational technology, whose
cybersecurity is complicated by several challenges. We propose a graphical
model for cybersecurity risk assessment based on Adversarial Risk Analysis to
face those challenges. We also provide an example of the model in the context
of an offshore drilling rig. The proposed model provides a more formal and
comprehensive analysis of risks, still using the standard business language
based on decisions, risks, and value.Comment: In Proceedings GraMSec 2014, arXiv:1404.163
A Stochastic Model of Active Cyber Defense Dynamics
The concept of active cyber defense has been proposed for years. However,
there are no mathematical models for characterizing the effectiveness of active
cyber defense. In this paper, we fill the void by proposing a novel Markov
process model that is native to the interaction between cyber attack and active
cyber defense. Unfortunately, the native Markov process model cannot be tackled
by the techniques we are aware of. We therefore simplify, via mean-field
approximation, the Markov process model as a Dynamic System model that is
amenable to analysis. This allows us to derive a set of valuable analytical
results that characterize the effectiveness of four types of active cyber
defense dynamics. Simulations show that the analytical results are inherent to
the native Markov process model, and therefore justify the validity of the
Dynamic System model. We also discuss the side-effect of the mean-field
approximation and its implications
Malware in the Future? Forecasting of Analyst Detection of Cyber Events
There have been extensive efforts in government, academia, and industry to
anticipate, forecast, and mitigate cyber attacks. A common approach is
time-series forecasting of cyber attacks based on data from network telescopes,
honeypots, and automated intrusion detection/prevention systems. This research
has uncovered key insights such as systematicity in cyber attacks. 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 U.S. Department of Defense, which rarely relies only on
automated systems. Our data set consists of weekly counts of cyber events over
approximately seven years. 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 for resource allocation, estimation of security resources, and the
development of effective risk-management strategies. We used a Bayesian State
Space Model for forecasting and found that events one week ahead could be
predicted. To quantify bursts, we used a Markov model. Our findings of
systematicity in analyst-detected cyber attacks are consistent with previous
work using 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. 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.Comment: Revised version resubmitted to journa
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