Network Situational Awareness

2015 ◽  
pp. 334-349 ◽  
Author(s):  
Tom Fairfax ◽  
Christopher Laing ◽  
Paul Vickers
Keyword(s):  
Real Time ◽  
Network Traffic ◽  
Affective Computing ◽  
Situational Awareness ◽  
Computer Network ◽  
Emergent Properties ◽  
Self Organized ◽  
Traffic Volumes ◽  
Self Organized Criticality ◽  
Intelligent Information

This chapter treats computer networks as a cyber warfighting domain in which the maintenance of situational awareness is impaired by increasing traffic volumes and the lack of immediate sensory perception. Sonification (the use of non-speech audio for communicating information) is proposed as a viable means of monitoring a network in real time and a research agenda employing the sonification of a network's self-organized criticality within a context-aware affective computing scenario is given. The chapter views a computer network as a cyber battlespace with a particular operations spectrum and dynamics. Increasing network traffic volumes are interfering with the ability to present real-time intelligence about a network and so suggestions are made for how the context of a network might be used to help construct intelligent information infrastructures. Such a system would use affective computing principles to sonify emergent properties (such as self-organized criticality) of network traffic and behaviour to provide effective real-time situational awareness.

scholarly journals Detection of Abnormalities in Real-Time Computer Network Traffic Empowered by Machine Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 2072-2079
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Real Time ◽  
Network Traffic ◽  
Computer Network ◽  
Data Gathering ◽  
Network Systems ◽  
Data Set ◽  
Synthetic Network ◽  
Scientific Papers

This research discloses how to utilize machine learning methods for anomaly detection in real-time on a computer network. While utilizing machine learning for this task is definitely not a novel idea, little literature is about the matter of doing it in real-time. Most machine learning research in PC network anomaly detection depends on the KDD '99 data set and means to demonstrate the proficiency of the algorithms introduced. The emphasis on this data set has caused a lack of scientific papers disclosing how to assemble network data, remove features, and train algorithms for use inreal-time networks. It has been contended that utilizing the KDD '99 dataset for anomaly detection is not appropriate for real-time network systems. This research proposes how the data gathering procedure will be possible utilizing a dummy network and generating synthetic network traffic by analyzing the importance of One-class SVM. As the efficiency of k-means clustering and LTSM neural networks is lower than one-class SVM, that is why this research uses the results of existing research of LSTM and k-means clustering for the comparison with reported outcomes of a similar algorithm on the KDD '99 dataset. Precisely, without engaging KDD ’99 data set by using synthetic network traffic, this research achieved the higher accuracy as compared to the previous researches.

Self-organized criticality in a computer network model

2000 ◽  
Vol 61 (2) ◽  
pp. 1067-1071 ◽  
Author(s):  
Jian Yuan ◽  
Yong Ren ◽  
Xiuming Shan
Keyword(s):  
Network Model ◽  
Computer Network ◽  
Self Organized ◽  
Self Organized Criticality

Problem behavior in autism spectrum disorders: A paradigmatic self-organized perspective of network structures

2019 ◽  
Vol 42 ◽  
Author(s):  
Lucio Tonello ◽  
Luca Giacobbi ◽  
Alberto Pettenon ◽  
Alessandro Scuotto ◽  
Massimo Cocchi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Problem Behaviors ◽  
Autism Spectrum ◽  
The Self ◽  
Network Structures ◽  
Self Organized ◽  
Critical Equilibrium ◽  
Self Organized Criticality ◽  
Acute Agitation ◽  
Spectrum Disorders

AbstractAutism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.

Self-Organized Criticality in the Autowave Model of Speciation

2020 ◽  
Vol 75 (5) ◽  
pp. 398-408
Author(s):  
A. Y. Garaeva ◽  
A. E. Sidorova ◽  
N. T. Levashova ◽  
V. A. Tverdislov
Keyword(s):  
Self Organized ◽  
Self Organized Criticality

Modeling Extinction

2003 ◽  
Author(s):  
M. E. J. Newman ◽  
R. G. Palmer
Keyword(s):  
Evolutionary Biology ◽  
Large Scale ◽  
Competitive Exclusion ◽  
Applied Mathematics ◽  
Santa Fe ◽  
New Approach ◽  
Self Organized ◽  
Self Organized Criticality ◽  
Extinction Events ◽  
Mass Extinction Events

Developed after a meeting at the Santa Fe Institute on extinction modeling, this book comments critically on the various modeling approaches. In the last decade or so, scientists have started to examine a new approach to the patterns of evolution and extinction in the fossil record. This approach may be called "statistical paleontology," since it looks at large-scale patterns in the record and attempts to understand and model their average statistical features, rather than their detailed structure. Examples of the patterns these studies examine are the distribution of the sizes of mass extinction events over time, the distribution of species lifetimes, or the apparent increase in the number of species alive over the last half a billion years. In attempting to model these patterns, researchers have drawn on ideas not only from paleontology, but from evolutionary biology, ecology, physics, and applied mathematics, including fitness landscapes, competitive exclusion, interaction matrices, and self-organized criticality. A self-contained review of work in this field.

scholarly journals Communication infrastructure for situational awareness enhancement in WAMS with optimal PMU placement

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Bhargav Appasani ◽  
Amitkumar Vidyakant Jha ◽  
Sunil Kumar Mishra ◽  
Abu Nasar Ghazali
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Area Measurement ◽  
Measurement Unit ◽  
Monitoring And Control ◽  
Communication Infrastructure ◽  
Wide Area Measurement System ◽  
Digital Elevation ◽  
Elevation Model ◽  
And Control

AbstractReal time monitoring and control of a modern power system has achieved significant development since the incorporation of the phasor measurement unit (PMU). Due to the time-synchronized capabilities, PMU has increased the situational awareness (SA) in a wide area measurement system (WAMS). Operator SA depends on the data pertaining to the real-time health of the grid. This is measured by PMUs and is accessible for data analytics at the data monitoring station referred to as the phasor data concentrator (PDC). Availability of the communication system and communication delay are two of the decisive factors governing the operator SA. This paper presents a pragmatic metric to assess the operator SA and ensure optimal locations for the placement of PMUs, PDC, and the underlying communication infrastructure to increase the efficacy of operator SA. The uses of digital elevation model (DEM) data of the surface topography to determine the optimal locations for the placement of the PMU, and the microwave technology for communicating synchrophasor data is another important contribution carried out in this paper. The practical power grid system of Bihar in India is considered as a case study, and extensive simulation results and analysis are presented for validating the proposed methodology.

Use of PMU-Based Software Platform to Provide Real-Time Situational Awareness for Bronzeville Community Microgrid

2020 ◽  
Author(s):  
Niroj Gurung ◽  
Sri Raghavan Raghav Kothandaraman ◽  
Liuxi Calvin Zhang ◽  
Heng Kevin Chen ◽  
Farnoosh Rahmatian ◽  
...  
Keyword(s):  
Real Time ◽  
Situational Awareness ◽  
Software Platform
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