CompactFlow: A Hybrid Binary Format for Network Flow Data

InSight2: A Modular Visual Analysis Platform for Network Situational Awareness in Large-Scale Networks

Electronics ◽

10.3390/electronics9101747 ◽

2020 ◽

Vol 9 (10) ◽

pp. 1747

Author(s):

Hansaka Angel Dias Edirisinghe Kodituwakku ◽

Alex Keller ◽

Jens Gregor

Keyword(s):

Real Time ◽

Computer Networks ◽

Network Flow ◽

Large Scale ◽

Visual Analysis ◽

Situational Awareness ◽

Monitoring Network ◽

Network Activity ◽

Flow Data ◽

Large Scale Networks

The complexity and throughput of computer networks are rapidly increasing as a result of the proliferation of interconnected devices, data-driven applications, and remote working. Providing situational awareness for computer networks requires monitoring and analysis of network data to understand normal activity and identify abnormal activity. A scalable platform to process and visualize data in real time for large-scale networks enables security analysts and researchers to not only monitor and study network flow data but also experiment and develop novel analytics. In this paper, we introduce InSight2, an open-source platform for manipulating both streaming and archived network flow data in real time that aims to address the issues of existing solutions such as scalability, extendability, and flexibility. Case-studies are provided that demonstrate applications in monitoring network activity, identifying network attacks and compromised hosts and anomaly detection.

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Scalable Bayesian Modeling, Monitoring, and Analysis of Dynamic Network Flow Data

Journal of the American Statistical Association ◽

10.1080/01621459.2017.1345742 ◽

2018 ◽

Vol 113 (522) ◽

pp. 519-533 ◽

Cited By ~ 12

Author(s):

Xi Chen ◽

Kaoru Irie ◽

David Banks ◽

Robert Haslinger ◽

Jewell Thomas ◽

...

Keyword(s):

Bayesian Modeling ◽

Network Flow ◽

Dynamic Network ◽

Flow Data ◽

Dynamic Network Flow

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A platform for the analysis and visualization of network flow data of android environments

2015 IFIP/IEEE International Symposium on Integrated Network Management (IM) ◽

10.1109/inm.2015.7140443 ◽

2015 ◽

Cited By ~ 7

Author(s):

Abdelkader Lahmadi ◽

Frederic Beck ◽

Eric Finickel ◽

Olivier Festor

Keyword(s):

Network Flow ◽

Flow Data

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Visual Analysis of Network Flow Data with Timelines and Event Plots

VizSEC 2007 - Mathematics and Visualization ◽

10.1007/978-3-540-78243-8_6 ◽

2008 ◽

pp. 85-99 ◽

Cited By ~ 14

Author(s):

D. Phan ◽

J. Gerth ◽

M. Lee ◽

A. Paepcke ◽

T. Winograd

Keyword(s):

Network Flow ◽

Visual Analysis ◽

Flow Data

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Identifying Network Anomalies Using Clustering Technique in Weblog Data

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v2i3a.2675 ◽

2012 ◽

Vol 2 (3) ◽

pp. 71-73 ◽

Cited By ~ 1

Author(s):

Kiran Bejjanki ◽

A. Bhaskar

Keyword(s):

Network Flow ◽

Flow Data ◽

Ip Address ◽

Clustering Techniques ◽

Clustering Technique ◽

Network Anomalies

In this paper we present an approach for identifying networkanomalies by visualizing network flow data which is stored inweblogs. Various clustering techniques can be used to identifydifferent anomalies in the network. Here, we present a newapproach based on simple K-Means for analyzing networkflow data using different attributes like IP address, Protocol,Port number etc. to detect anomalies. By using visualization,we can identify which sites are more frequently accessed bythe users. In our approach we provide overview about givendataset by studying network key parameters. In this processwe used preprocessing techniques to eliminate unwantedattributes from weblog data.

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