Network Security Situation Awareness Based on Multi-Source Data Fusion

2014 ◽  
Vol 989-994 ◽  
pp. 4885-4888 ◽  
Author(s):  
Gang Chen ◽  
Jun Ping Cai ◽  
Jun Yang
Keyword(s):  
Network Security ◽  
Situation Awareness ◽  
Situational Awareness ◽  
Evidence Theory ◽  
System Level ◽  
Source Information ◽  
Security Research ◽  
Data Source ◽  
Source Level ◽  
Level Fusion

Network security situation awareness is an effective way to analysis security situation of complex network.The concept and model of network security situational awareness was introduced.A new model of network security situation awareness was proposed. Considering the characteristics of multi-source information in network security research, a security situation awareness algorithm based on information fusion was adopted. This algorithm advanced modified D-S evidence theory, gets the values of security situation awareness of network by data source level fusion, host-level fusion and system-level fusion. The results can reflect the general security state of network.

A Network Security Situational Awareness Model Based on Information Fusion

2013 ◽  
Vol 846-847 ◽  
pp. 1632-1635
Author(s):  
Abasi
Keyword(s):  
Network Security ◽  
Information Fusion ◽  
Situational Awareness ◽  
Evidence Theory ◽  
Arma Model ◽  
Situational Factors ◽  
Source Information ◽  
Network Nodes ◽  
Model Based ◽  
The Future

Security situational awareness has become a hot topic in the area of network securityresearch in recent years. The existing security situational awareness methods are analyzed and compared in details, and thus a newnetwork security situational awareness model based on information fusion is proposed. This modelfuses multi-source information from a mass of logs by introducing the modified D-S evidence theory,gets the values of nodes security situational awareness by situational factors fusion using attacks threat,and vulnerability information which network nodes have and successful attacks depend on, computesthe value of network security situational awareness by nodes situation fusion using service informationof the network nodes, and draws the security-situation-graph of network. Then, it analyzes the timeseries of the computing results by ARMA model to forecast the future threat in network security.Finally an example of actual network datasets is given to validate the network security situationalawareness model and algorithm. The results show that this model and algorithm is more effective andaccurate than the existing security situational awareness methods.

scholarly journals Research on situation awareness of network security assessment based on dempster-shafer

2020 ◽  
Vol 309 ◽  
pp. 02004 ◽  
Author(s):  
Weifa Zheng
Keyword(s):  
Network Security ◽  
Situation Awareness ◽  
Evaluation Model ◽  
Evidence Theory ◽  
Security Assessment ◽  
Situation Assessment ◽  
Network Intrusion ◽  
Event Information ◽  
Multi Attribute Decision Making ◽  
Security Device

There are a lot of uncertainties in the network security situation assessment that depends on is multi-source and heterogeneous. Therefore, the objective uncertainties must be described and dealt with in the process of network security situation assessment. This paper proposes a multi-attribute decision-making trust evaluation model based on D-S evidence theory in multi-source and heterogeneous environment. By collecting, processing and evaluating attack event information from many data sources of security device, it can effectively evaluate the possibility of network intrusion, and provide a new exploration for network security situation assessment.

Fault Diagnosis for the Electric Starting System of Self-Propelled Artillery Based on Information Fusion Technology

2014 ◽  
Vol 940 ◽  
pp. 280-283
Author(s):  
Chong Fa Liu ◽  
Zheng Xi Xie ◽  
Jie Min Yang ◽  
Zhi Jun Gao
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
System Level ◽  
Diagnostic Process ◽  
Comprehensive Treatment ◽  
Information Uncertainty ◽  
Fusion Technology ◽  
Source Information ◽  
Sensor Information

Fault diagnosis based on multi-sensor information fusion technology processes multi-source information and data of the monitoring system in various manners such as detection, parallel and related processing, estimation, comprehensive treatment and so on so as to maximize the use of system knowledge and the information provided by the available detectable quantity of the system in fault diagnosis. Compared with the single sensor, multi-sensor information fusion enjoys obvious advantages in reducing information uncertainty, improving information accuracy obtained by the system and advancing system reliability and fault tolerance capability. As the accuracy of traditional fault diagnosis method is not high, considering the characteristics of faults in the electric starting system of self-propelled gun, a method of fault diagnosis is presented here based on network information fusion technology. The diagnostic process is divided into two level diagnosis, that is subsystem and system level. System adopts BP neural network in fault mode classification, while at system level D-S evidence theory is used in the process of synthetic decision evaluation on the entire system malfunction, ensuring accurate and fast fault diagnosis, which greatly shorten the corrective maintenance time.

A Cloud Security Situational Awareness Model Based on Parallel Apriori Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 6294-6297 ◽  
Author(s):  
Xiao Liang ◽  
Hong Wu Lv ◽  
Fang Fang Guo ◽  
Hui Qiang Wang
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Network Security ◽  
Situation Awareness ◽  
Situational Awareness ◽  
New Technology ◽  
Cloud Security ◽  
Apriori Algorithm ◽  
Virtual Storage ◽  
Model Based

Network Security Situation Awareness (NSSA) is a hot topic in network security field, and cloud computing is a new technology integrated virtual storage and distributed computing. It has become the challenging questions how to provide efficient and reliable service for NSSA based on the cloud computing.This paper proposes a cloud security situation awareness model based on data mining, and puts forwarda parallelfrequent-tree Apriori algorithm (PFT-Apriori) for mining association rules. Compare with the traditional Apriori algorithm, the experimental results show that the performance of system is increased by 51% under PFT-algorithm.

A multi-source information fusion method for ship target recognition based on bayesian inference and evidence theory

2021 ◽  
pp. 1-16
Author(s):  
Yu Zhang ◽  
Qunli Xiao ◽  
Xinyang Deng ◽  
Wen Jiang
Keyword(s):  
Bayesian Inference ◽  
Situation Awareness ◽  
Target Recognition ◽  
Evidence Theory ◽  
Distance Matrix ◽  
Target Type ◽  
Source Information ◽  
The Difference ◽  
Ship Recognition ◽  
Dynamic Time

The ship target recognition (STR) is greatly related to the battlefield situation awareness, which has recently gained prominence in the military domains. With the diversification and complexity of military missions, ship targets are mostly performed in the form of formations. Therefore, using the formation information to improve the accuracy of the ship target type recognition is worth studying. To effectively identify ship target type, we in this paper jointly consider the ship dynamic, formation, and feature information to propose a STR method based on Bayesian inference and evidence theory. Specifically, we first calculate the ship position distance matrix and the directional distance matrix with the Dynamic Time Warping (DTW) and the difference-vector algorithm taken into account. Then, we use the two distance matrices to obtain the ship formation information at different distance thresholds by the hierarchical clustering method, based on which we can infer the ship type. Thirdly, formation information and other attribute information are as nodes of the Bayesian Network (BN) to infer the ship type. Afterward, we can convert the recognition results at different thresholds into body of evidences (BOEs) as multiple information sources. Finally, we fuse the BOEs to get the final recognition. The proposed method is verified in simulation battle scenario in this paper. The simulation results demonstrate that the proposed method achieves performance superiority as compared with other ship recognition methods in terms of recognition accuracy.

scholarly journals Research on the Key Technologies of Network Security-Oriented Situation Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yikun Zhu ◽  
Zhiling Du
Keyword(s):  
Network Security ◽  
Situational Awareness ◽  
Algorithm Design ◽  
Evidence Theory ◽  
Assessment Model ◽  
Time Varying ◽  
Security Threat ◽  
Model Based ◽  
Posture Assessment ◽  
Threat Posture

In today’s increasingly severe network security situation, network security situational awareness provides a more comprehensive and feasible new idea for the inadequacy of various single solutions and is currently a research hotspot in the field of network security. At present, there are still gaps or room for improvement in network security situational awareness in terms of model scheme improvement, comprehensive and integrated consideration, algorithm design optimization, etc. A lot of scientific research investments and results are still needed to improve the form of network security in a long and solid way. In this paper, we propose a network security posture assessment model based on time-varying evidence theory for the existing multisource information fusion technology that lacks consideration of the problem of threat occurrence support rate over time and make the threat information reflect the law of time change by introducing a time parameter in the basic probability assignment value. Thus, the existing hierarchical threat posture quantitative assessment technique is improved and a hierarchical multisource network security threat posture assessment model based on time-varying evidence theory is proposed. Finally, the superiority of the proposed model is verified through experiments.

Network Security Situation Awareness Approach Based on Markov Game Model

2011 ◽  
Vol 22 (3) ◽  
pp. 495-508 ◽  
Author(s):  
Yong ZHANG ◽  
Xiao-Bin TAN ◽  
Xiao-Lin CUI ◽  
Hong-Sheng XI
Keyword(s):  
Network Security ◽  
Situation Awareness ◽  
Game Model ◽  
Markov Game

scholarly journals Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 162
Author(s):  
Soyeon Kim ◽  
René van Egmond ◽  
Riender Happee
Keyword(s):  
User Interface ◽  
Situation Awareness ◽  
User Interfaces ◽  
Situational Awareness ◽  
Selection Criterion ◽  
Auditory Modality ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Positive Effects ◽  
High Urgency

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

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