Social cyber forensics: leveraging open source information and social network analysis to advance cyber security informatics

2019 ◽  
Vol 26 (4) ◽  
pp. 412-430 ◽  
Author(s):  
Samer Al-khateeb ◽  
Nitin Agarwal
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Cyber Security ◽  
Source Information ◽  
Cyber Forensics ◽  
Security Informatics

scholarly journals Social network analysis of open source software: A review and categorisation

2021 ◽  
Vol 130 ◽  
pp. 106442
Author(s):  
Kelvin McClean ◽  
Des Greer ◽  
Anna Jurek-Loughrey
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Open Source Software

scholarly journals Revealing the Detailed Lineage of Script Outputs Using Hybrid Provenance

2018 ◽  
Vol 12 (2) ◽  
pp. 390-408
Author(s):  
Qian Zhang ◽  
Yang Cao ◽  
Qiwen Wang ◽  
Duc Vu ◽  
Priyaa Thavasimani ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Climate Science ◽  
Data Curation ◽  
Biodiversity Data ◽  
Log Files

We illustrate how combining retrospective and prospectiveprovenance can yield scientifically meaningful hybrid provenancerepresentations of the computational histories of data produced during a script run. We use scripts from multiple disciplines (astrophysics, climate science, biodiversity data curation, and social network analysis), implemented in Python, R, and MATLAB, to highlight the usefulness of diverse forms of retrospectiveprovenance when coupled with prospectiveprovenance. Users provide prospective provenance, i.e., the conceptual workflows latent in scripts, via simple YesWorkflow annotations, embedded as script comments. Runtime observables can be linked to prospective provenance via relational views and queries. These observables could be found hidden in filenames or folder structures, be recorded in log files, or they can be automatically captured using tools such as noWorkflow or the DataONE RunManagers. The YesWorkflow toolkit, example scripts, and demonstration code are available via an open source repository.

Finding Influential Nodes in Sourceforge.net Using Social Network Analysis

2021 ◽  
pp. 156-166
Author(s):  
K.G. Srinivasa ◽  
Ganesh Chandra Deka ◽  
Krishnaraj P.M.
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Information Flow ◽  
Open Source Software ◽  
Small Set ◽  
Influential Nodes

The contribution of volunteers in the development of Free and Open Source Software in Sourceforge.net is studied in this paper. Using Social Network analysis, the small set of developers who can maximize the information flow in the network are discovered. The propagation of top developers across past three years are also studied. The four algorithms used to find top influential developers gives almost similar results. The movement of top developers over past years years was also consistent.

Who connects with whom? A social network analysis of an online open source software community

First Monday ◽  
2011 ◽  
Author(s):  
Cuihua Shen ◽  
Peter Monge
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Open Source ◽  
Open Source Software ◽  
Online Community ◽  
Online Social Network ◽  
The Social ◽  
Collaboration Dynamics ◽  
The Web

By examining “who connects with whom” in an online community using social network analysis, this study tests the social drivers that shape the collaboration dynamics among a group of participants from SourceForge, the largest open source community on the Web. The formation of the online social network was explored by testing two distinct network attachment logics: strategic selection and homophily. Both logics received some support. Taken together, the results are suggestive of a “performance-based clustering” phenomenon within the OSS online community in which most collaborations involve accomplished developers, and novice developers tend to partner with less accomplished and less experienced peers.

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