Intelligent detection method for abnormal big data in heterogeneous networks based on Bayesian classification

2020 ◽  
Vol 18 (2) ◽  
pp. 155-165
Author(s):  
Ruijing Liu ◽  
Xiaoting Luo
Keyword(s):  
Big Data ◽  
Heterogeneous Networks ◽  
Detection Method ◽  
Bayesian Classification ◽  
Intelligent Detection

scholarly journals An Area-Context-Based Credibility Detection for Big Data in IoT

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Bo Zhao ◽  
Xiang Li ◽  
Jiayue Li ◽  
Jianwen Zou ◽  
Yifan Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Detection Method ◽  
Big Data Analysis ◽  
Detection Methods ◽  
High Detection ◽  
Data Credibility ◽  
High Level

In order to improve the credibility of big data analysis platform’s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an area-context-based credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.

An Intelligent Detection Method for Network Security

2014 ◽  
Vol 530-531 ◽  
pp. 646-649
Author(s):  
Ling Qiu ◽  
Cai Ming Liu
Keyword(s):  
Network Security ◽  
Immune Cells ◽  
Detection Method ◽  
Network Data ◽  
Network Attacks ◽  
Detection Ability ◽  
Intelligent Detection ◽  
Network Administrators ◽  
Ip Packets

To dynamically discover network attacks hidden in network data, an intelligent detection method for network security is proposed. Biological immune principles and mechanisms are adopted to judge whether network data contain illegal network packets. Signature library of network attacks and section library of attack signatures are constructed. They store attack signatures and signature sections, respectively. They are used to make the initial detection ability of proposed method. Detectors are defined to simulate immune cells. They evolve dynamically to adapt the network security. Signatures of network data are extracted from IP packets. Detectors match network data's signatures which mean some attacks. Warning information is formed and sent to network administrators according to recognized attacks.

Design of Low-Noise, High-Density Signal Intelligent Detection System

2013 ◽  
Vol 380-384 ◽  
pp. 3882-3885
Author(s):  
Xiaoan Yang
Keyword(s):  
Phase Transition ◽  
Detection Method ◽  
Detection System ◽  
Low Noise ◽  
Weak Signal ◽  
High Effectiveness ◽  
Motion State ◽  
Intelligent Detection ◽  
Stable Performance ◽  
Engineering Practices

Using motion state of the equipment transducer to determine the presence of a weak signal is a common method of signal detection, whose core is to determine the system's phase change. There a many traditional ways to judge phase transition, but most of which have computational complexity and need a large amount of data which make them difficult to apply engineering practices. In order to solve these problems, this paper presents a detection method based on Lyapunov exponent classification with a small amount of data. This approach has some advantages such as requiring fewer observed values, small calculation amount, and able to automatically determine the phase transition without subjective factors involved etc. Experiments show that this method has stable performance, high effectiveness, strong practicality and promotion.

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