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Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks
[article]
2018
arXiv
pre-print
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Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. ...
This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. ...
The authors of [14] propose two anomaly detection methods using information theoretic measures: Kullback-Leibler divergence (KLD) and information content. ...
arXiv:1803.06054v1
fatcat:minngrp5yzf6fkw3bslkdf6eyu
Abnormal Activity Detection Using Pyroelectric Infrared Sensors
2016
Sensors
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The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. ...
Each training sample is modeled by an HMM, and their dissimilarity is calculated based on the Kullback-Leibler (KL) divergence [11] . ...
PIR sensor models can also be used to construct wireless sensor networks, which are intended to track and recognize multiple human targets [17] . ...
doi:10.3390/s16060822
pmid:27271632
pmcid:PMC4934248
fatcat:3gsgachfozbfxjrlguuu5wkupa
Sensors, Medical Images and Signal Processing:
2011
IMIA Yearbook of Medical Informatics
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Association) Yearbook 2011.Current research in the field of sensors, signal, and imaging informatics is characterized by theoretically sound techniques and evaluations with focus in imaging informatics ...
When compared to research on sensors and signals, imaging research represent the majority of published papers in 2010. ...
Acknowledgements We greatly acknowledge the support of Martina Hutter and of the reviewers in the selection process of the IMIA Yearbook.
References ...
doi:10.1055/s-0038-1638744
fatcat:36kj7dav55ax5pmbkio4n3appu
A review of novelty detection
2014
Signal Processing
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The Kullback-Leibler divergence is a statistical tool for estimating the difference in information content between two distributions. ...
The method can be used with a number of distortion measures, including Bregman divergences (such as the Kullback-Leibler divergence) and directional measures. ...
doi:10.1016/j.sigpro.2013.12.026
fatcat:ha6kc4bzhbajxbo2mdyh5cw5hu
Denial of Service Defence for Resource Availability in Wireless Sensor Networks
2018
IEEE Access
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Wireless sensor networks (WSN) over the years have become one of the most promising networking solutions with exciting new applications for the near future. ...
Index Terms-Denial of Service (DoS), detection techniques, intrusion detection system (IDS), resource availability, resource depletion, wireless sensor networks (WSNs). ...
ACKNOWLEDGEMENTS This research is funded by the Advanced Sensor Networks SARChI Chair program, co-hosted by University of Pretoria (UP) and Council for Scientific and Industrial Research (CSIR), through ...
doi:10.1109/access.2018.2793841
fatcat:chajqpv6arfphif46zj3w2f5da
Unsupervised anomaly detection in railway catenary condition monitoring using autoencoders
2020
IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
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To facilitate the effective usage of catenary condition monitoring data, this study proposes an unsupervised anomaly detection approach as a pre-processing measure. ...
The output anomalous data can save a considerable amount of computation time and manpower in further interpretations aiming to pinpoint defects. ...
Zhigang Liu with Southwest Jiaotong University and the Chinese Academy of Railway Sciences for sharing the data used in the case study of this research. ...
doi:10.1109/iecon43393.2020.9254633
dblp:conf/iecon/Wang20
fatcat:jvxg4cco3zbf7aev3xfhdejuji
The Information Theoretic Approach to Signal Anomaly Detection for Cognitive Radio
2010
International Journal of Digital Multimedia Broadcasting
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The first method is applicable to signals with periodic structures and is based on the analysis of Kullback-Leibler divergence. ...
Two complementary algorithms based on information theoretic measures of statistical distribution divergence and information content are proposed. ...
Harald Haas acknowledges the Scottish Funding Council support of his position within the Edinburgh Research Partnership in Engineering and Mathematics between the University of Edinburgh and Heriot Watt ...
doi:10.1155/2010/740594
fatcat:6wmdndqp6vgg3o2j24yyoyu46u
RICERCANDO: Data Mining Toolkit for Mobile Broadband Measurements
[article]
2019
arXiv
pre-print
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While recent advances in monitoring with crowdsourced as well as network infrastructure-based methods allow us to tap into a number of performance metrics from all layers of networking, huge swaths of ...
and geomobile data, so that anomalies are detected and singled out, and the advanced mining module that lets the analyst deduce root causes of observed anomalies. ...
of Ljubljana, including Jernej Kernc, Vesna Tanko, and Anže Starič for their contributions to RICERCANDO; to Janez Sterle for his help with the explanation of the observed network anomalies; and to David ...
arXiv:1901.07287v1
fatcat:knwqmzfrbvdl7a7pixkwuyqd6e
A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study
2018
Sensors
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The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these ...
Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. ...
The scheme uses a Kullback-Leibler divergence-based algorithm that employs segment-based recursive kernel density estimation. ...
doi:10.3390/s18082491
pmid:30071595
fatcat:bpperb7vbfb7dhkl2fy2d3xqxe
Deep Generative Models in the Industrial Internet of Things: A Survey
2022
IEEE Transactions on Industrial Informatics
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With IIoT enabling continuous integration of sensors and controllers with the network, intelligent analysis of the generated Big Data is a critical requirement. ...
In this article, we review the state of the art of DGMs and their applicability to IIoT, classifying the reviewed works into the IIoT application areas of anomaly detection, trust-boundary protection, ...
To that end, VAEs need to reduce the diversion (asymmetric distance) between two probability distributions (q(z) and P (z|y)) with Kullback-Leibler divergence, KL KL(q(z)|P (z|x)) = E q(z) ln q(z) P (z ...
doi:10.1109/tii.2022.3155656
fatcat:5np4ghh43nfqjkjycrzkle6vlu
Human Activity Sensing with Wireless Signals: A Survey
2020
Sensors
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Wireless networks have been widely deployed with a high demand for wireless data traffic. ...
With the development and deployment of new wireless technology, there will be more sensing opportunities in human activities. ...
Kullback-Leibler (KL) divergence leverages the fact that the distribution of amplitudes within each window should be similar when there are no human actions. ...
doi:10.3390/s20041210
pmid:32098392
pmcid:PMC7071003
fatcat:yc4npmrnwbculbprwlnyad7hwi
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
[article]
2020
arXiv
pre-print
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wireless networks. ...
Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate ...
Furthermore, the high complexity of the POMDP formulation was mitigated by a low-dimensional belief representation, which was achieved by minimizing the Kullback-Leibler divergence defined in [291] . ...
arXiv:1902.01946v2
fatcat:7bveg6rmjfga5mftdkr3mst2qa
Reconfigurable Antenna Assisted Intrusion Detection in Wireless Networks
2013
International Journal of Distributed Sensor Networks
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Intrusion detection is a challenging problem in wireless networks due to the broadcast nature of the wireless medium. ...
The results show that the proposed scheme can add an additional layer of security that can significantly alleviate many vulnerabilities and threats in current fixed wireless networks. ...
Acknowledgment This material is based upon work supported by the National Science Foundation under Grant no. 1028608. ...
doi:10.1155/2013/564503
fatcat:mqr3luw7a5es7fh65hjiblslkq
Sequential (Quickest) Change Detection: Classical Results and New Directions
[article]
2021
arXiv
pre-print
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Online detection of changes in stochastic systems, referred to as sequential change detection or quickest change detection, is an important research topic in statistics, signal processing, and information ...
We also discuss some new dimensions that emerge at the intersection of sequential change detection with other areas, along with a selection of modern applications and remarks on open questions. ...
A quantity that plays an important role in the performance of sequential change detection algorithms is the Kullback-Leibler (KL) divergence between two distributions. Definition 2. (KL Divergence). ...
arXiv:2104.04186v1
fatcat:ypxkrjyyf5dprpsc5b4kxxrnhm
SWeFS: sensor Web Fire Shield for forest fire detection and monitoring
[chapter]
2014
Advances in forest fire research
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the scope of the Research Funding Program: THALES-UOA-Sensor Web Fire Shield (SWeFS). ...
co-financed by the European Union (European Social Fund -ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) in ...
The metrics provided for the calculation of the overlap percent among the maps are the Jensen-Shannon Divergence, the Pearson Correlation Coefficient, the Kullback-Leibler Divergence and the Similarity ...
doi:10.14195/978-989-26-0884-6_169
fatcat:i7ornidvefb2fk5ycwunuwipau
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