7,025 research outputs found
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
Spatiotemporal Patterns and Predictability of Cyberattacks
Y.C.L. was supported by Air Force Office of Scientific Research (AFOSR) under grant no. FA9550-10-1-0083 and Army Research Office (ARO) under grant no. W911NF-14-1-0504. S.X. was supported by Army Research Office (ARO) under grant no. W911NF-13-1-0141. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD
Unfolding the procedure of characterizing recorded ultra low frequency, kHZ and MHz electromagetic anomalies prior to the L'Aquila earthquake as pre-seismic ones. Part I
Ultra low frequency, kHz and MHz electromagnetic anomalies were recorded
prior to the L'Aquila catastrophic earthquake that occurred on April 6, 2009.
The main aims of this contribution are: (i) To suggest a procedure for the
designation of detected EM anomalies as seismogenic ones. We do not expect to
be possible to provide a succinct and solid definition of a pre-seismic EM
emission. Instead, we attempt, through a multidisciplinary analysis, to provide
elements of a definition. (ii) To link the detected MHz and kHz EM anomalies
with equivalent last stages of the L'Aquila earthquake preparation process.
(iii) To put forward physically meaningful arguments to support a way of
quantifying the time to global failure and the identification of distinguishing
features beyond which the evolution towards global failure becomes
irreversible. The whole effort is unfolded in two consecutive parts. We clarify
we try to specify not only whether or not a single EM anomaly is pre-seismic in
itself, but mainly whether a combination of kHz, MHz, and ULF EM anomalies can
be characterized as pre-seismic one
Order out of Randomness : Self-Organization Processes in Astrophysics
Self-organization is a property of dissipative nonlinear processes that are
governed by an internal driver and a positive feedback mechanism, which creates
regular geometric and/or temporal patterns and decreases the entropy, in
contrast to random processes. Here we investigate for the first time a
comprehensive number of 16 self-organization processes that operate in
planetary physics, solar physics, stellar physics, galactic physics, and
cosmology. Self-organizing systems create spontaneous {\sl order out of chaos},
during the evolution from an initially disordered system to an ordered
stationary system, via quasi-periodic limit-cycle dynamics, harmonic mechanical
resonances, or gyromagnetic resonances. The internal driver can be gravity,
rotation, thermal pressure, or acceleration of nonthermal particles, while the
positive feedback mechanism is often an instability, such as the
magneto-rotational instability, the Rayleigh-B\'enard convection instability,
turbulence, vortex attraction, magnetic reconnection, plasma condensation, or
loss-cone instability. Physical models of astrophysical self-organization
processes involve hydrodynamic, MHD, and N-body formulations of Lotka-Volterra
equation systems.Comment: 61 pages, 38 Figure
Mössbauer Spectrometry
Mössbauer spectrometry gives electronic, magnetic, and structural information from within
materials. A Mössbauer spectrum is an intensity of γ-ray absorption versus energy for a
specific resonant nucleus such as ^(57)Fe or ^(119)Sn. For one nucleus to emit a Îł-ray and a second
nucleus to absorb it with efficiency, both nuclei must be embedded in solids, a phenomenon
known as the “Mössbauer effect.” Mössbauer spectrometry looks at materials from the
“inside out,” where “inside” refers to the resonant nucleus.
Mössbauer spectra give quantitative information on “hyperfine interactions,” which are small
energies from the interaction between the nucleus and its neighboring electrons. The three
hyperfine interactions originate from the electron density at the nucleus (the isomer shift),
the gradient of the electric field (the nuclear quadrupole splitting), and the unpaired electron
density at the nucleus (the hyperfine magnetic field). Over the years, methods have been
refined for using these three hyperfine interactions to determine valence and spin at the
resonant atom. Even when the hyperfine interactions are not easily interpreted, they can
often be used reliably as “fingerprints” to identify the different local chemical environments
of the resonant atom, usually with a good estimate of their fractional abundances. Mössbauer
spectrometry is useful for quantitative phase analyses or determinations of the concentrations
of resonant element in different phases, even when the phases are nanostructured or
amorphous.
Most Mössbauer spectra are acquired with simple laboratory equipment and a radioisotope
source, but the recent development of synchrotron instrumentation now allow for measurements
on small 10 µm samples, which may be exposed to extreme environments of pressure
and temperature. Other capabilities include measurements of the vibrational spectra of the
resonant atoms, and coherent scattering and diffraction of nuclear radiation.
This article is not a review of the field, but an instructional reference that explains principles
and practices, and gives the working materials scientist a basis for evaluating whether or not
Mössbauer spectrometry may be useful for a research problem. A few representative
materials studies are presented
Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
Radio emission from air showers enables measurements of cosmic particle
kinematics and identity. The radio signals are detected in broadband Megahertz
antennas among continuous background noise. We present two deep learning
concepts and their performance when applied to simulated data. The first
network classifies time traces as signal or background. We achieve a true
positive rate of about 90% for signal-to-noise ratios larger than three with a
false positive rate below 0.2%. The other network is used to clean the time
trace from background and to recover the radio time trace originating from an
air shower. Here we achieve a resolution in the energy contained in the trace
of about 20% without a bias for of the traces with a signal. The
obtained frequency spectrum is cleaned from signals of radio frequency
interference and shows the expected shape.Comment: 20 pages, 13 figures, resubmitted to JINS
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