Human Identification with Electroencephalogram (EEG) for the Future Network Security

2013 ◽  
pp. 575-581 ◽  
Author(s):  
Xu Huang ◽  
Salahiddin Altahat ◽  
Dat Tran ◽  
Li Shutao
Keyword(s):  
Network Security ◽  
Human Identification ◽  
The Future ◽  
Electroencephalogram Eeg

scholarly journals The Future of Networks and Network Security

1997 ◽  
pp. 417-424
Author(s):  
R. Nelson
Keyword(s):  
Network Security ◽  
The Future

How SASE is defining the future of network security

2020 ◽  
Vol 2020 (12) ◽  
pp. 6-8
Author(s):  
Michael Wood
Keyword(s):  
Network Security ◽  
The Future

A Novel Predication Approach for Network Security Situation Inspired by Immunity

2010 ◽  
Vol 20-23 ◽  
pp. 849-855 ◽  
Author(s):  
Yuan Quan Shi ◽  
Tao Li ◽  
Wen Chen ◽  
Rui Rui Zhang
Keyword(s):  
Network Security ◽  
Large Scale ◽  
Security Attacks ◽  
Evolution Operators ◽  
Large Scale Network ◽  
Novel Approach ◽  
Formal Definitions ◽  
The Future ◽  
Future Situation ◽  
Scale Network

To effectively prevent large-scale network security attacks, a novel Predication Approach for Network Security Situation inspired by Immunity (PANSSI) is proposed. In this predication approach, the concepts and formal definitions of antigen and antibody in the network security situation predication domain are given; meanwhile, the mathematical models of some antibody evolution operators being related to PANSSI are exhibited. By analyzing time series and computing the affinity between antigen and antibody in artificial immune system, network security situation predication model is established, and then the future situation of network security attacks is predicted by it. Experimental results prove that PANSSI can forecast the future network security situation real-timely and correctly, and provides a novel approach for network security situation predication.

scholarly journals The Future of Software Engineering: Where Will Machine Learning, Agile, and Virtualization Take Us Next?

2021 ◽  
pp. 222-230
Author(s):  
Dennis Mancl ◽  
Steven D. Fraser
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Network Security ◽  
Best Practices ◽  
21St Century ◽  
Global Development ◽  
The Future ◽  
Security Algorithm ◽  
Independent Consultant

AbstractSoftware has become the lifeblood of the 21st century, enabling a broad range of commercial, medical, educational, agricultural, and government applications. These applications are designed and deployed through a variety of software best practices. With the onset of the COVID-19 pandemic, developers have embraced virtualization (remote working) and a variety of strategies to manage the complexity of global development on multiple platforms. However, evolving hazards such as network security, algorithm bias, and the combination of careless developers and deliberate attacks continue to be a challenge. An XP2021 panel organized and chaired by Steven Fraser debated the future of software engineering and related topics such education, ethics, and tools. The panel featured Anita Carleton (CMU’s SEI), Priya Marsonia (Cognizant), Bertrand Meyer (SIT, Eiffel Software), Landon Noll (Independent Consultant), and Kati Vilkki (Reaktor).

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.

Design of the Security Component in Reconfigurable Network

2013 ◽  
Vol 718-720 ◽  
pp. 2120-2124
Author(s):  
Fan Zhang ◽  
Xing Ming Zhang ◽  
Ke Song
Keyword(s):  
Network Security ◽  
Adaptive Capacity ◽  
Mainland China ◽  
Network Technology ◽  
Network Development ◽  
The Future

New threats to and requirements of security come forth endlessly while the network technology progresses continually. Security equipment has little adaptive capacity in the existing rigid network. It has bad influence on network security in Mainland China. A reconfigurable security component is designed in this paper. It can fit the coming requirements of network development in the future. A prototype of this security component is implemented and analyzed.

EMS network security applications of the future

1996 ◽  
Vol 9 (2) ◽  
pp. 42-46 ◽  
Author(s):  
L. Jamniczky
Keyword(s):  
Network Security ◽  
Security Applications ◽  
The Future

scholarly journals Deep Learning-Based Network Security Data Sampling and Anomaly Prediction in Future Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Lan Liu ◽  
Jun Lin ◽  
Pengcheng Wang ◽  
Langzhou Liu ◽  
Rongfu Zhou
Keyword(s):  
Deep Learning ◽  
Network Security ◽  
Data Sampling ◽  
Security Attacks ◽  
The Public ◽  
Network Controller ◽  
The Future ◽  
Public Dataset ◽  
Design Idea ◽  
Important Nodes

Based on the design idea of future network, this paper analyzes the network security data sampling and anomaly prediction in future network. Through game theory, it is determined that data sampling is performed on some important nodes in the future network. Deep learning methods are used on the selected nodes to collect data and analyze the characteristics of the network data. Then, through offline and real-time analyses, network security abnormal events are predicted in the future network. With the comparison of various algorithms and the adjustment of hyperparameters, the data characteristics and classification algorithms corresponding to different network security attacks are found. We have carried out experiments on the public dataset, and the experiment proves the effectiveness of the method. It can provide reference for the management strategy of the switch node or the host node by the future network controller.

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