Large‐scale automated forecasting for network safety and security monitoring

2019 ◽  
Vol 35 (3) ◽  
pp. 431-447
Author(s):  
Roi Naveiro ◽  
Simón Rodríguez ◽  
David Ríos Insua
Keyword(s):  
Large Scale ◽  
Security Monitoring ◽  
Network Safety ◽  
Automated Forecasting

scholarly journals Parameter Communication Consistency Model for Large-Scale Security Monitoring Based on Mobile Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171884-171897 ◽  
Author(s):  
Rui Yang ◽  
Jilin Zhang ◽  
Jian Wan ◽  
Li Zhou ◽  
Jing Shen ◽  
...  
Keyword(s):  
Mobile Computing ◽  
Large Scale ◽  
Security Monitoring ◽  
Consistency Model

Analysis on the Key Technologies of CBM Surface Well Development in Mining Active Areas of China

2011 ◽  
Vol 201-203 ◽  
pp. 372-377 ◽  
Author(s):  
Jin Hua Chen
Keyword(s):  
High Risk ◽  
Large Scale ◽  
Coal Bed ◽  
Coal Bed Methane ◽  
Security Monitoring ◽  
Surface Drainage ◽  
The Past ◽  
Casing Damage ◽  
Key Technologies ◽  
Well Development

In the past 20 years, coal bed methane (CBM) surface well technology has made some progress in mining active areas in China, but it still remains at its experimental and study stage, and industrialized and large-scale development has not been achieved. The research of the key technologies, such as the distribution of surface wells, the identification and protection of the high-risk locations of casing damage, the optimization of well structure, and the security monitoring of surface drainage in mining active areas, is the key for CBM surface well development in mining active areas of China.

scholarly journals Application of Wireless Sensor Network Technology in Security Control of Intelligent Buildings

2018 ◽  
Vol 14 (05) ◽  
pp. 93
Author(s):  
Jin Wang ◽  
Hua Shao
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Spectrum Sensing ◽  
Information Integration ◽  
Large Scale ◽  
Wireless Sensor ◽  
Signal Spectrum ◽  
Support Vector ◽  
Security Monitoring ◽  
Task Coordination

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-language: DE; mso-ansi-language: EN-GB; mso-bidi-language: AR-SA;" lang="EN-GB">When a wireless sensor network is used to perform real-time security monitoring inside a building, there are drawbacks like multi-path signal fading and difficulty in spectrum sensing. In light of these problems, this paper proposes an improved signal spectrum sensing algorithm based on support vector machine (SVM), which inhibits the impacts brought by the low signal-noise-ratio (SNR) environment in the transmission process of wireless sensor signals through the embedded cyclostationary characteristic parameters. Based on this, considering the low efficiency and poor fault tolerance of multi-task monitoring and scheduling inside the building, this paper also proposes a multi-task coordination and scheduling algorithm based on physical information integration, which achieves multi-task scheduling and execution through intelligent breakdown and prioritization of general tasks. The simulation test shows that, compared with the artificial neural network (ANN) algorithm and the maximum-minimum eigenvalue (MME) algorithm, the proposed algorithm has much better spectrum sensing effect under low SNR, takes less computation time, and achieves higher accuracy in large-scale multi-task coordination and scheduling. The research conclusions can provide new ideas for the application of wireless sensor network in intelligent building security monitoring.</span>

scholarly journals DNSSM: A large scale passive DNS security monitoring framework

2012 ◽  
Author(s):  
S. Marchal ◽  
J. Francois ◽  
C. Wagner ◽  
R. State ◽  
A. Dulaunoy ◽  
...  
Keyword(s):  
Large Scale ◽  
Security Monitoring ◽  
Monitoring Framework

A Self-Adaptive Corner Detection Algorithm for Low-Contrast Images

2014 ◽  
Vol 615 ◽  
pp. 158-164
Author(s):  
Liang Sun ◽  
Jian Chun Xing ◽  
Shuang Qing Wang ◽  
Shi Qiang Wang
Keyword(s):  
Large Scale ◽  
Detection Algorithm ◽  
Corner Detection ◽  
Feature Points ◽  
Low Contrast ◽  
Security Monitoring ◽  
Harris Corner ◽  
Comparative Results ◽  
Security Monitoring System ◽  
Self Adaptive

In order to effectively inhibit the image dithering caused by wind-induced vibration in the security monitoring system, it calls for the extraction and match of the feature points of the sequential frames. Harris corner detection algorithm is a widely-employed characteristics extraction algorithm in the image processing. In the security monitoring field, images and videos photographed are characterized by large scale, high pixel and low contrast degree. The classical algorithm often fails to effectively obtain the feature points while handling the images and videos of the kind. Concerning the above problems, this paper puts forward an improved self-adaptive corner detection algorithm. Firstly, this paper employs the self-adaptive gray threshold comparative results of the of every point with the surrounding eight neighborhood points to select the preselected points of part of the corners. Following that, this paper classifies the preselected points into three types according to certain rules and the value of the already selected self-adaptive gray threshold. At last, according to the classification results, this paper uses different corners to test function threshold and the preselected points as well to eliminate the peripheral points and the pseudo-corners so as to gain the genuine corners. After verifying the above improved algorithm in the practical scenario in the security monitoring, the results of this paper prove its effectiveness, feasibility and its advantages in terms of robustness.

scholarly journals Automated data-intensive forecasting of plant phenology throughout the United States

2019 ◽  
Author(s):  
Shawn D. Taylor ◽  
Ethan P. White
Keyword(s):  
United States ◽  
Large Scale ◽  
The United States ◽  
Plant Phenology ◽  
Lessons Learned ◽  
Phenological Data ◽  
Data Intensive ◽  
Potential Applications ◽  
Near Term ◽  
Automated Forecasting

AbstractPhenology - the timing of cyclical and seasonal natural phenomena such as flowering and leaf out - is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there has been no automated species-level forecasts of plant phenology. This is due in part to the challenges of building a system that integrates large volumes of climate observations and forecasts, uses that data to fit models and make predictions for large numbers of species, and consistently disseminates the results of these forecasts in interpretable ways. Here we describe a new near-term phenology forecasting system that makes predictions for the timing of budburst, flowers, ripe fruit, and fall colors for 78 species across the United States up to 6 months in advance and is updated every four days. We use the lessons learned in developing this system to provide guidance developing large-scale near-term ecological forecast systems more generally, to help advance the use of automated forecasting in ecology.

scholarly journals Heterogeneous Data Aggregation and Normalization in Information Security Monitoring and Intrusion Detection Systems of Large-scale Industrial CPS

2020 ◽  
Vol 32 (5) ◽  
pp. 131-142
Author(s):  
Maria A. Poltavtseva
Keyword(s):  
Information Security ◽  
Data Aggregation ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Subject Area ◽  
Multidimensional Data ◽  
Security Monitoring ◽  
Detection Systems ◽  
The Subject ◽  
General Architecture

Monitoring of industrial cyber-physical systems (CPS) is an ongoing process necessary to ensure their security. The effectiveness of information security monitoring depends on the quality and speed of collection, processing, and analyzing of heterogeneous CPS data. Today, there are many methods of analysis for solving security problems of distributed industrial CPS. These methods have different requirements for the input data characteristics, but there are common features in them due to the subject area. The work is devoted to preliminary data processing for the security monitoring of industrial CPS in modern conditions. The general architecture defines the use of aggregation and normalization methods for data preprocessing. The work includes the issue from the requirements for the preprocessing system, the specifics of the subject area, to the general architecture and specific methods of multidimensional data aggregation.

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