102 research outputs found
ZETAR: Modeling and Computational Design of Strategic and Adaptive Compliance Policies
Security compliance management plays an important role in mitigating insider
threats. Incentive design is a proactive and non-invasive approach to achieving
compliance by aligning an employee's incentive with the defender's security
objective. Controlling insiders' incentives to elicit proper actions is
challenging because they are neither precisely known nor directly controllable.
To this end, we develop ZETAR, a zero-trust audit and recommendation framework,
to provide a quantitative approach to model incentives of the insiders and
design customized and strategic recommendation policies to improve their
compliance. We formulate primal and dual convex programs to compute the optimal
bespoke recommendation policies. We create a theoretical underpinning for
understanding trust and compliance, and it leads to security insights,
including fundamental limits of Completely Trustworthy (CT) recommendation, the
principle of compliance equivalency, and strategic information disclosure. This
work proposes finite-step algorithms to efficiently learn the CT policy set
when employees' incentives are unknown. Finally, we present a case study to
corroborate the design and illustrate a formal way to achieve compliance for
insiders with different risk attitudes. Our results show that the optimal
recommendation policy leads to a significant improvement in compliance for
risk-averse insiders. Moreover, CT recommendation policies promote insiders'
satisfaction
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Predicting taxonomic and functional structure of microbial communities in acid mine drainage.
Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities
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