A perceptually-relevant model-based cyber threat prediction method for enterprise mission assurance

2011 ◽  
Author(s):  
Tahia Infantes Morris ◽  
Liam M. Mayron ◽  
Wayne B. Smith ◽  
Margaret M. Knepper ◽  
Reg Ita ◽  
...  
Keyword(s):  
Prediction Method ◽  
Relevant Model ◽  
Model Based ◽  
Cyber Threat ◽  
Mission Assurance

scholarly journals Interoperability Challenges in the Cybersecurity Information Sharing Ecosystem

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 18 ◽  
Author(s):  
Konstantinos Rantos ◽  
Arnolnt Spyros ◽  
Alexandros Papanikolaou ◽  
Antonios Kritsas ◽  
Christos Ilioudis ◽  
...  
Keyword(s):  
Information Sharing ◽  
Information Exchange ◽  
Security Analysis ◽  
Corporate Networks ◽  
Layered Model ◽  
Cyber Threats ◽  
Model Based ◽  
Threat Intelligence ◽  
Cyber Threat ◽  
The Right

Threat intelligence helps businesses and organisations make the right decisions in their fight against cyber threats, and strategically design their digital defences for an optimised and up-to-date security situation. Combined with advanced security analysis, threat intelligence helps reduce the time between the detection of an attack and its containment. This is achieved by continuously providing information, accompanied by data, on existing and emerging cyber threats and vulnerabilities affecting corporate networks. This paper addresses challenges that organisations are bound to face when they decide to invest in effective and interoperable cybersecurity information sharing and categorises them in a layered model. Based on this, it provides an evaluation of existing sources that share cybersecurity information. The aim of this research is to help organisations improve their cyber threat information exchange capabilities, to enhance their security posture and be more prepared against emerging threats.

scholarly journals A Novel Output Prediction Method in Production Management Based on Parameter Evaluation Using DHNN

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Jiantao Chang ◽  
Yuanying Qiu ◽  
Xianguang Kong
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Production Management ◽  
Prediction Method ◽  
Multiple Linear Regression Model ◽  
Model Based ◽  
Parameter Evaluation ◽  
Improved Model

Output prediction is one of the difficult issues in production management. To overcome this difficulty, a dynamic-improved multiple linear regression model based on parameter evaluation using discrete Hopfield neural networks (DHNN) is presented. First, a traditional multiple linear regression model is established; this model takes the factors in production lifecycle (not only one phase of the production) into account, such as manufacturing resources, manufacturing process, and product rejection rate, so it makes the output prediction be more accurate. Then a static-improved model is built using the backstepping method. Finally, we obtain the dynamic-improved model based on parameter evaluation using DHNN. These three models are applied to an aviation manufacturing enterprise based on the actual data, and the results of the output prediction show that the models have practical value.

Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

2020 ◽  
Vol 13 (4) ◽  
pp. 657-671
Author(s):  
Wei Jiang ◽  
Hongmei Xu ◽  
Elnaz Akbari ◽  
Jiang Wen ◽  
Shuang Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Moisture Content ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Principal Component ◽  
Spectral Difference ◽  
Model Based ◽  
Neural Network Prediction ◽  
Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

A Wide-Deep-Sequence Model-Based Quality Prediction Method in Industrial Process Analysis

2020 ◽  
Vol 31 (9) ◽  
pp. 3721-3731 ◽  
Author(s):  
Lei Ren ◽  
Zihao Meng ◽  
Xiaokang Wang ◽  
Renquan Lu ◽  
Laurence T. Yang
Keyword(s):  
Process Analysis ◽  
Prediction Method ◽  
Industrial Process ◽  
Quality Prediction ◽  
Model Based
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