200 research outputs found
Universal flux-fluctuation law in small systems
We thank Dr. DeMenezes for providing the microchip data. This work was partially supported by the NSF of China under Grant Nos. 11135001, 11275003. Y.C.L. was supported by ARO under Grant No. W911NF-14-1-0504.Peer reviewedPublisher PD
Emergence of multicluster chimera states
We thank Prof. L. Huang for helpful discussions. This work was partially supported by ARO under Grant No. W911NF-14-1-0504 and by NSF of China under Grant No. 11275003. The visit of NY to Arizona State University was partially sponsored by Prof. Z. Zheng and the State Scholarship Fund of China.Peer reviewedPublisher PD
Multiagent model and mean field theory of complex auction dynamics
Acknowledgements We are grateful to Ms Yinan Zhao for providing the data and to Yuzhong Chen and Cancan Zhou for discussions and suggestions. This work was supported by ARO under Grant No. W911NF-14-1-0504 and by NSFC under Grants Nos. 11275003 and 61174165. The visit of QC to Arizona State University was partially sponsored by the State Scholarship Fund of China.Peer reviewedPublisher PD
Spatiotemporal patterns and predictability of cyberattacks
A relatively unexplored issue in cybersecurity science and engineering is
whether there exist intrinsic patterns of cyberattacks. Conventional wisdom
favors absence of such patterns due to the overwhelming complexity of the
modern cyberspace. Surprisingly, through a detailed analysis of an extensive
data set that records the time-dependent frequencies of attacks over a
relatively wide range of consecutive IP addresses, we successfully uncover
intrinsic spatiotemporal patterns underlying cyberattacks, where the term
"spatio" refers to the IP address space. In particular, we focus on analyzing
{\em macroscopic} properties of the attack traffic flows and identify two main
patterns with distinct spatiotemporal characteristics: deterministic and
stochastic. Strikingly, there are very few sets of major attackers committing
almost all the attacks, since their attack "fingerprints" and target selection
scheme can be unequivocally identified according to the very limited number of
unique spatiotemporal characteristics, each of which only exists on a
consecutive IP region and differs significantly from the others. We utilize a
number of quantitative measures, including the flux-fluctuation law, the Markov
state transition probability matrix, and predictability measures, to
characterize the attack patterns in a comprehensive manner. A general finding
is that the attack patterns possess high degrees of predictability, potentially
paving the way to anticipating and, consequently, mitigating or even preventing
large-scale cyberattacks using macroscopic approaches
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