4,502 research outputs found
Lifeguard: Local Health Awareness for More Accurate Failure Detection
SWIM is a peer-to-peer group membership protocol with attractive scaling and
robustness properties. However, slow message processing can cause SWIM to mark
healthy members as failed (so called false positive failure detection), despite
inclusion of a mechanism to avoid this.
We identify the properties of SWIM that lead to the problem, and propose
Lifeguard, a set of extensions to SWIM which consider that the local failure
detector module may be at fault, via the concept of local health. We evaluate
this approach in a precisely controlled environment and validate it in a
real-world scenario, showing that it drastically reduces the rate of false
positives. The false positive rate and detection time for true failures can be
reduced simultaneously, compared to the baseline levels of SWIM
Observation of the nonlinear Wood's anomaly on periodic arrays of nickel nanodimers
Linear and nonlinear magneto-photonic properties of periodic arrays of nickel
nanodimers are governed by the interplay of the (local) optical response of
individual nanoparticles and (non-local) diffraction phenomena, with a striking
example of Wood's anomaly. Angular and magnetic-field dependencies of the
second harmonic intensity evidence Wood's anomaly when new diffraction orders
emerge. Near-infrared spectroscopic measurements performed at different optical
wavelengths and grating constants discriminate between the linear and nonlinear
excitation mechanisms of Wood's anomalies. In the nonlinear regime the Wood's
anomaly is characterized by an order-of-magnitude larger effect in intensity
redistribution between the diffracted beams, as compared to the linear case.
The nonlinear Wood's anomaly manifests itself also in the nonlinear magnetic
contrast highlighting the prospects of nonlinear magneto-photonics.Comment: 8 pages, 6 figure
A Cognitive Framework to Secure Smart Cities
The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms
Dendritic Cells for Anomaly Detection
Artificial immune systems, more specifically the negative selection
algorithm, have previously been applied to intrusion detection. The aim of this
research is to develop an intrusion detection system based on a novel concept
in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting
cells and key to the activation of the human signals from the host tissue and
correlate these signals with proteins know as antigens. In algorithmic terms,
individual DCs perform multi-sensor data fusion based on time-windows. The
whole population of DCs asynchronously correlates the fused signals with a
secondary data stream. The behaviour of human DCs is abstracted to form the DC
Algorithm (DCA), which is implemented using an immune inspired framework,
libtissue. This system is used to detect context switching for a basic machine
learning dataset and to detect outgoing portscans in real-time. Experimental
results show a significant difference between an outgoing portscan and normal
traffic.Comment: 8 pages, 10 tables, 4 figures, IEEE Congress on Evolutionary
Computation (CEC2006), Vancouver, Canad
Degradation Prediction of Semiconductor Lasers using Conditional Variational Autoencoder
Semiconductor lasers have been rapidly evolving to meet the demands of
next-generation optical networks. This imposes much more stringent requirements
on the laser reliability, which are dominated by degradation mechanisms (e.g.,
sudden degradation) limiting the semiconductor laser lifetime. Physics-based
approaches are often used to characterize the degradation behavior
analytically, yet explicit domain knowledge and accurate mathematical models
are required. Building such models can be very challenging due to a lack of a
full understanding of the complex physical processes inducing the degradation
under various operating conditions. To overcome the aforementioned limitations,
we propose a new data-driven approach, extracting useful insights from the
operational monitored data to predict the degradation trend without requiring
any specific knowledge or using any physical model. The proposed approach is
based on an unsupervised technique, a conditional variational autoencoder, and
validated using vertical-cavity surface-emitting laser (VCSEL) and tunable edge
emitting laser reliability data. The experimental results confirm that our
model (i) achieves a good degradation prediction and generalization performance
by yielding an F1 score of 95.3%, (ii) outperforms several baseline ML based
anomaly detection techniques, and (iii) helps to shorten the aging tests by
early predicting the failed devices before the end of the test and thereby
saving costsComment: Published in: Journal of Lightwave Technology (Volume: 40, Issue: 18,
15 September 2022
Dark Matter density and the Higgs mass in LVS String Phenomenology
The Large Volume Scenario for getting a non-supersymmetric vacuum in type IIB
string theory leads, through the Weyl anomaly and renormalization group
running, to an interesting phenomenology. However, for gravitino masses below
500 TeV there are cosmological problems and the resulting Higgs mass is well
below 124 GeV. Here we discuss the phenomenology and cosmology for gravitino
masses which are 500 TeV. We find that not only is the cosmological
modulus problem alleviated and the right value for dark matter density
obtained, but also the Higgs mass is in the 122-125 GeV range. However the
spectrum of SUSY particles will be too heavy to be observed at the LHC.Comment: 16 pages, 2 figures, further discussion of cosmological issues,
references added, version to be published in PL
A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication
Semiconductor lasers, one of the key components for optical communication
systems, have been rapidly evolving to meet the requirements of next generation
optical networks with respect to high speed, low power consumption, small form
factor etc. However, these demands have brought severe challenges to the
semiconductor laser reliability. Therefore, a great deal of attention has been
devoted to improving it and thereby ensuring reliable transmission. In this
paper, a predictive maintenance framework using machine learning techniques is
proposed for real-time heath monitoring and prognosis of semiconductor laser
and thus enhancing its reliability. The proposed approach is composed of three
stages: i) real-time performance degradation prediction, ii) degradation
detection, and iii) remaining useful life (RUL) prediction. First of all, an
attention based gated recurrent unit (GRU) model is adopted for real-time
prediction of performance degradation. Then, a convolutional autoencoder is
used to detect the degradation or abnormal behavior of a laser, given the
predicted degradation performance values. Once an abnormal state is detected, a
RUL prediction model based on attention-based deep learning is utilized.
Afterwards, the estimated RUL is input for decision making and maintenance
planning. The proposed framework is validated using experimental data derived
from accelerated aging tests conducted for semiconductor tunable lasers. The
proposed approach achieves a very good degradation performance prediction
capability with a small root mean square error (RMSE) of 0.01, a good anomaly
detection accuracy of 94.24% and a better RUL estimation capability compared to
the existing ML-based laser RUL prediction models.Comment: Published in Journal of Lightwave Technology (Volume: 40, Issue: 14,
15 July 2022
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