2,085 research outputs found
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation
Recent research has shown that the integration of Reinforcement Learning (RL)
with Moving Target Defense (MTD) can enhance cybersecurity in
Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing
work is hindered by data privacy concerns associated with centralized data
processing in RL, and the unsatisfactory time needed to learn right MTD
techniques that are effective against a rising number of heterogeneous zero-day
attacks. Thus, this work presents CyberForce, a framework that combines
Federated and Reinforcement Learning (FRL) to collaboratively and privately
learn suitable MTD techniques for mitigating zero-day attacks. CyberForce
integrates device fingerprinting and anomaly detection to reward or penalize
MTD mechanisms chosen by an FRL-based agent. The framework has been deployed
and evaluated in a scenario consisting of ten physical devices of a real IoT
platform affected by heterogeneous malware samples. A pool of experiments has
demonstrated that CyberForce learns the MTD technique mitigating each attack
faster than existing RL-based centralized approaches. In addition, when various
devices are exposed to different attacks, CyberForce benefits from knowledge
transfer, leading to enhanced performance and reduced learning time in
comparison to recent works. Finally, different aggregation algorithms used
during the agent learning process provide CyberForce with notable robustness to
malicious attacks.Comment: 11 pages, 8 figure
Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural Signaling
Brain-Computer Interfaces (BCI) arose as systems that merge computing systems with the human brain to facilitate recording, stimulation, and inhibition of neural activity. Over the years, the development of BCI technologies has shifted towards miniaturization of devices that can be seamlessly embedded into the brain and can target single neuron or small population sensing and control. We present a motivating example highlighting vulnerabilities of two promising micron-scale BCI technologies, demonstrating the lack of security and privacy principles in existing solutions. This situation opens the door to a novel family of cyberattacks, called neuronal cyberattacks, affecting neuronal signaling. This article defines the first two neural cyberattacks, Neuronal Flooding (FLO) and Neuronal Scanning (SCA), where each threat can affect the natural activity of neurons. This work implements these attacks in a neuronal simulator to determine their impact over the spontaneous neuronal behavior, defining three metrics: number of spikes, percentage of shifts, and dispersion of spikes. Several experiments demonstrate that both cyberattacks produce a reduction of spikes compared to spontaneous behavior, generating a rise in temporal shifts and a dispersion increase. Mainly, SCA presents a higher impact than FLO in the metrics focused on the number of spikes and dispersion, where FLO is slightly more damaging, considering the percentage of shifts. Nevertheless, the intrinsic behavior of each attack generates a differentiation on how they alter neuronal signaling. FLO is adequate to generate an immediate impact on the neuronal activity, whereas SCA presents higher effectiveness for damages to the neural signaling in the long-term
A comparison between percutaneous and surgical transplantation of autologous skeletal myoblasts in a swine model of chronic myocardial infarction
A non-canonical di-acidic signal at the C-terminus of Kv1.3 determines anterograde trafficking and surface expression
Impairment of Kv1.3 expression at the cell membrane in leukocytes and sensory neuron contributes to the pathophysiology of autoimmune diseases and sensory syndromes. Molecular mechanisms underlying Kv1.3 channel trafficking to the plasma membrane remain elusive. We report a novel non-canonical di-acidic signal (E483/484) at the C-terminus of Kv1.3 essential for anterograde transport and surface expression. Notably, homologous motifs are conserved in neuronal Kv1 and Shaker channels. Biochemical analysis revealed interactions with the Sec24 subunit of the coat protein complex II. Disruption of this complex retains the channel at the endoplasmic reticulum. A molecular model of the Kv1.3-Sec24a complex suggests salt-bridges between the di-acidic E483/484 motif in Kv1.3 and the di-basic R750/752 sequence in Sec24. These findings identify a previously unrecognized motif of Kv channels essential for their expression on the cell surface. Our results contribute to our understanding of how Kv1 channels target to the cell membrane, and provide new therapeutic strategies for the treatment of pathological conditions
Planck intermediate results. XXIX. All-sky dust modelling with Planck, IRAS, and WISE observations
We present all-sky modelling of the high resolution Planck, IRAS, and WISE
infrared (IR) observations using the physical dust model presented by Draine
and Li in 2007 (DL). We study the performance and results of this model, and
discuss implications for future dust modelling. The present work extends the DL
dust modelling carried out on nearby galaxies using Herschel and Spitzer data
to Galactic dust emission. We employ the DL dust model to generate maps of the
dust mass surface density, the optical extinction Av, and the starlight
intensity parametrized by Umin. The DL model reproduces the observed spectral
energy distribution (SED) satisfactorily over most of the sky, with small
deviations in the inner Galactic disk and in low ecliptic latitude areas. We
compare the DL optical extinction Av for the diffuse interstellar medium with
optical estimates for 2 10^5 quasi-stellar objects (QSOs) observed in the Sloan
digital sky survey. The DL Av estimates are larger than those determined
towards QSOs by a factor of about 2, which depends on Umin. The DL fitting
parameter Umin, effectively determined by the wavelength where the SED peaks,
appears to trace variations in the far-IR opacity of the dust grains per unit
Av, and not only in the starlight intensity. To circumvent the model
deficiency, we propose an empirical renormalization of the DL Av estimate,
dependent of Umin, which compensates for the systematic differences found with
QSO observations. This renormalization also brings into agreement the DL Av
estimates with those derived for molecular clouds from the near-IR colours of
stars in the 2 micron all sky survey. The DL model and the QSOs data are used
to compress the spectral information in the Planck and IRAS observations for
the diffuse ISM to a family of 20 SEDs normalized per Av, parameterized by
Umin, which may be used to test and empirically calibrate dust models.Comment: Final version that has appeared in A&
Search for the glueball candidates f0(1500) and fJ(1710) in gamma gamma collisions
Data taken with the ALEPH detector at LEP1 have been used to search for gamma
gamma production of the glueball candidates f0(1500) and fJ(1710) via their
decay to pi+pi-. No signal is observed and upper limits to the product of gamma
gamma width and pi+pi- branching ratio of the f0(1500) and the fJ(1710) have
been measured to be Gamma_(gamma gamma -> f0(1500)). BR(f0(1500)->pi+pi-) <
0.31 keV and Gamma_(gamma gamma -> fJ(1710)). BR(fJ(1710)->pi+pi-) < 0.55 keV
at 95% confidence level.Comment: 10 pages, 3 figure
Planck 2015 results. XXVII. The Second Planck Catalogue of Sunyaev-Zeldovich Sources
We present the all-sky Planck catalogue of Sunyaev-Zeldovich (SZ) sources detected from the 29 month full-mission data. The catalogue (PSZ2) is the largest SZ-selected sample of galaxy clusters yet produced and the deepest all-sky catalogue of galaxy clusters. It contains 1653 detections, of which 1203 are confirmed clusters with identified counterparts in external data-sets, and is the first SZ-selected cluster survey containing > confirmed clusters. We present a detailed analysis of the survey selection function in terms of its completeness and statistical reliability, placing a lower limit of 83% on the purity. Using simulations, we find that the Y5R500 estimates are robust to pressure-profile variation and beam systematics, but accurate conversion to Y500 requires. the use of prior information on the cluster extent. We describe the multi-wavelength search for counterparts in ancillary data, which makes use of radio, microwave, infra-red, optical and X-ray data-sets, and which places emphasis on the robustness of the counterpart match. We discuss the physical properties of the new sample and identify a population of low-redshift X-ray under- luminous clusters revealed by SZ selection. These objects appear in optical and SZ surveys with consistent properties for their mass, but are almost absent from ROSAT X-ray selected samples
Search for supersymmetry with a dominant R-parity violating LQDbar couplings in e+e- collisions at centre-of-mass energies of 130GeV to 172 GeV
A search for pair-production of supersymmetric particles under the assumption
that R-parity is violated via a dominant LQDbar coupling has been performed
using the data collected by ALEPH at centre-of-mass energies of 130-172 GeV.
The observed candidate events in the data are in agreement with the Standard
Model expectation. This result is translated into lower limits on the masses of
charginos, neutralinos, sleptons, sneutrinos and squarks. For instance, for
m_0=500 GeV/c^2 and tan(beta)=sqrt(2) charginos with masses smaller than 81
GeV/c^2 and neutralinos with masses smaller than 29 GeV/c^2 are excluded at the
95% confidence level for any generation structure of the LQDbar coupling.Comment: 32 pages, 30 figure
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