28,774 research outputs found
Compressive Diffusion Strategies Over Distributed Networks for Reduced Communication Load
We study the compressive diffusion strategies over distributed networks based
on the diffusion implementation and adaptive extraction of the information from
the compressed diffusion data. We demonstrate that one can achieve a comparable
performance with the full information exchange configurations, even if the
diffused information is compressed into a scalar or a single bit. To this end,
we provide a complete performance analysis for the compressive diffusion
strategies. We analyze the transient, steady-state and tracking performance of
the configurations in which the diffused data is compressed into a scalar or a
single-bit. We propose a new adaptive combination method improving the
convergence performance of the compressive diffusion strategies further. In the
new method, we introduce one more freedom-of-dimension in the combination
matrix and adapt it by using the conventional mixture approach in order to
enhance the convergence performance for any possible combination rule used for
the full diffusion configuration. We demonstrate that our theoretical analysis
closely follow the ensemble averaged results in our simulations. We provide
numerical examples showing the improved convergence performance with the new
adaptive combination method.Comment: Submitted to IEEE Transactions on Signal Processin
Tracking Cyber Adversaries with Adaptive Indicators of Compromise
A forensics investigation after a breach often uncovers network and host
indicators of compromise (IOCs) that can be deployed to sensors to allow early
detection of the adversary in the future. Over time, the adversary will change
tactics, techniques, and procedures (TTPs), which will also change the data
generated. If the IOCs are not kept up-to-date with the adversary's new TTPs,
the adversary will no longer be detected once all of the IOCs become invalid.
Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular
expressions (regexes), up-to-date with a dynamic adversary. Our framework
solves the TTK problem in an automated, cyclic fashion to bracket a previously
discovered adversary. This tracking is accomplished through a data-driven
approach of self-adapting a given model based on its own detection
capabilities.
In our initial experiments, we found that the true positive rate (TPR) of the
adaptive solution degrades much less significantly over time than the naive
solution, suggesting that self-updating the model allows the continued
detection of positives (i.e., adversaries). The cost for this performance is in
the false positive rate (FPR), which increases over time for the adaptive
solution, but remains constant for the naive solution. However, the difference
in overall detection performance, as measured by the area under the curve
(AUC), between the two methods is negligible. This result suggests that
self-updating the model over time should be done in practice to continue to
detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science &
Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas,
Nevada, US
Tracking Cyber Adversaries with Adaptive Indicators of Compromise
A forensics investigation after a breach often uncovers network and host
indicators of compromise (IOCs) that can be deployed to sensors to allow early
detection of the adversary in the future. Over time, the adversary will change
tactics, techniques, and procedures (TTPs), which will also change the data
generated. If the IOCs are not kept up-to-date with the adversary's new TTPs,
the adversary will no longer be detected once all of the IOCs become invalid.
Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular
expressions (regexes), up-to-date with a dynamic adversary. Our framework
solves the TTK problem in an automated, cyclic fashion to bracket a previously
discovered adversary. This tracking is accomplished through a data-driven
approach of self-adapting a given model based on its own detection
capabilities.
In our initial experiments, we found that the true positive rate (TPR) of the
adaptive solution degrades much less significantly over time than the naive
solution, suggesting that self-updating the model allows the continued
detection of positives (i.e., adversaries). The cost for this performance is in
the false positive rate (FPR), which increases over time for the adaptive
solution, but remains constant for the naive solution. However, the difference
in overall detection performance, as measured by the area under the curve
(AUC), between the two methods is negligible. This result suggests that
self-updating the model over time should be done in practice to continue to
detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science &
Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas,
Nevada, US
Interaction between high-level and low-level image analysis for semantic video object extraction
Authors of articles published in EURASIP Journal on Advances in Signal Processing are the copyright holders of their articles and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate the article, according to the SpringerOpen copyright and license agreement (http://www.springeropen.com/authors/license)
An indoor variance-based localization technique utilizing the UWB estimation of geometrical propagation parameters
A novel localization framework is presented based on ultra-wideband (UWB) channel sounding, employing a triangulation method using the geometrical properties of propagation paths, such as time delay of arrival, angle of departure, angle of arrival, and their estimated variances. In order to extract these parameters from the UWB sounding data, an extension to the high-resolution RiMAX algorithm was developed, facilitating the analysis of these frequency-dependent multipath parameters. This framework was then tested by performing indoor measurements with a vector network analyzer and virtual antenna arrays. The estimated means and variances of these geometrical parameters were utilized to generate multiple sample sets of input values for our localization framework. Next to that, we consider the existence of multiple possible target locations, which were subsequently clustered using a Kim-Parks algorithm, resulting in a more robust estimation of each target node. Measurements reveal that our newly proposed technique achieves an average accuracy of 0.26, 0.28, and 0.90 m in line-of-sight (LoS), obstructed-LoS, and non-LoS scenarios, respectively, and this with only one single beacon node. Moreover, utilizing the estimated variances of the multipath parameters proved to enhance the location estimation significantly compared to only utilizing their estimated mean values
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