IoTSim-Stream: Modelling stream graph application in cloud simulation

2019 ◽  
Vol 99 ◽  
pp. 86-105 ◽  
Author(s):  
Mutaz Barika ◽  
Saurabh Garg ◽  
Andrew Chan ◽  
Rodrigo N. Calheiros ◽  
Rajiv Ranjan
Keyword(s):  
Graph Application

Semantic Graph Application to Call Center for Entity-Relation Search

2015 ◽  
Vol 21 (10) ◽  
pp. 3092-3098
Author(s):  
Takahiro Kawamura ◽  
Akihiko Ohsuga
Keyword(s):  
Call Center ◽  
Semantic Graph ◽  
Graph Application

Synthesis of multiple biomass corridor via decomposition approach: a P-graph application

2016 ◽  
Vol 130 ◽  
pp. 45-57 ◽  
Author(s):  
Bing Shen How ◽  
Boon Hooi Hong ◽  
Hon Loong Lam ◽  
Ferenc Friedler
Keyword(s):  
Decomposition Approach ◽  
Graph Application

scholarly journals Region Coloring in Minahasa Regency Using Sequential Color Algorithm

d'CARTESIAN ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 86
Author(s):  
Yevie Ingamita ◽  
Nelson Nainggolan ◽  
Benny Pinontoan
Keyword(s):  
Graph Theory ◽  
Positive Integer ◽  
Chromatic Number ◽  
Human Life ◽  
Minimum Number ◽  
Mathematical Sciences ◽  
Map Coloring ◽  
Graph Application

Graph Theory is one of the mathematical sciences whose application is very wide in human life. One of theory graph application is Map Coloring. This research discusses how to color the map of Minahasa Regency by using the minimum color that possible. The algorithm used to determine the minimum color in coloring the region of Minahasa Regency that is Sequential Color Algorithm. The Sequential Color Algorithm is an algorithm used in coloring a graph with k-color, where k is a positive integer. Based on the results of this research was found that the Sequential Color Algorithm can be used to color the map of Minahasa Regency with the minimum number of colors or chromatic number χ(G) obtained in the coloring of 25 sub-districts on the map of Minahasa Regency are 3 colors (χ(G) = 3).

scholarly journals Social engineering in cybersecurity: a domain ontology and knowledge graph application examples

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zuoguang Wang ◽  
Hongsong Zhu ◽  
Peipei Liu ◽  
Limin Sun
Keyword(s):  
Domain Knowledge ◽  
Explicit Knowledge ◽  
Social Engineering ◽  
Domain Ontology ◽  
Knowledge Graph ◽  
Specific Target ◽  
Ontology Evaluation ◽  
Attack Scenario ◽  
Cyberspace Security ◽  
Graph Application

AbstractSocial engineering has posed a serious threat to cyberspace security. To protect against social engineering attacks, a fundamental work is to know what constitutes social engineering. This paper first develops a domain ontology of social engineering in cybersecurity and conducts ontology evaluation by its knowledge graph application. The domain ontology defines 11 concepts of core entities that significantly constitute or affect social engineering domain, together with 22 kinds of relations describing how these entities related to each other. It provides a formal and explicit knowledge schema to understand, analyze, reuse and share domain knowledge of social engineering. Furthermore, this paper builds a knowledge graph based on 15 social engineering attack incidents and scenarios. 7 knowledge graph application examples (in 6 analysis patterns) demonstrate that the ontology together with knowledge graph is useful to 1) understand and analyze social engineering attack scenario and incident, 2) find the top ranked social engineering threat elements (e.g. the most exploited human vulnerabilities and most used attack mediums), 3) find potential social engineering threats to victims, 4) find potential targets for social engineering attackers, 5) find potential attack paths from specific attacker to specific target, and 6) analyze the same origin attacks.

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