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Mobile malware: History, Notable mobile malicious programs & Taxonomy
Mobile malware is malicious software that targets mobile phones or wireless-enabled Personal digital assistants (PDA), by causing the collapse of the system and loss or leakage of confidential information. As wireless phones and PDA networks have become more and more common and have grown in complexity, it has become increasingly difficult to ensure…
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Mobile malware topic overview
The analysis highlights History, Notable mobile malicious programs and Taxonomy as prominent areas in the source structure around Mobile malware.
Related topics
53
Source areas
4
Connected nodes
57
Extracted relationships
76
Related term clusters
18
Bridge connections
57
What this topic covers Research coverage
Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.
Notable mobile malicious programs · 23 topics
History · 15 topics
Taxonomy · 12 topics
Overview · 3 topics
Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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Mobile malware
3Mobile phone · Personal digital assistant · Malware
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.
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Advanced semantic analysis
How Mobile malware connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Mobile malware shows recurring relationship patterns in the source. For example, Mobile malware → Android, Android OS, April, August, Autolycos, BlackBerry, Bluetooth, Brazil, Cabir, Caribe, Commwarrior, DroidKungFu, Duts, Evina, EXE, FlexiSpy, Gingerbread, GingerMaster, Google Play Store, Guardian Another extracted example is Mobile malware → Finland, GSM-capable, In June, Information, Internet SMS, June, Mosquito, MoviStar, Ojam, PCs, Russia, SMS, Spain, Spanish, Telefónica, Timofonica, Trojan. Use these groups to spot repeated connection types before inspecting the individual relationships.
Mobile malware
Top relations
related to Notable mobile malicious programs· 59
Mobile malware → Android, Android OS, April, August, Autolycos, BlackBerry, Bluetooth, Brazil, Cabir, Caribe, Commwarrior, DroidKungFu, Duts, Evina, EXE, FlexiSpy, Gingerbread, GingerMaster, Google Play Store, Guardian
related to history· 17
Mobile malware → Finland, GSM-capable, In June, Information, Internet SMS, June, Mosquito, MoviStar, Ojam, PCs, Russia, SMS, Spain, Spanish, Telefónica, Timofonica, Trojan
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Mobile malware relationships Subject–Predicate–Object triples
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 76 structured relationships around Mobile malware. Examples in this analysis include Mobile malware → related to history → Timofonica and Mobile malware → related to history → Spain. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Mobile malware
related to history
Timofonica
0.60
section
Mobile malware
related to history
Spain
0.60
section
Mobile malware
related to history
Russia
0.60
section
Mobile malware
related to history
Finland
0.60
section
Mobile malware
related to history
June
0.60
section
Mobile malware
related to history
SMS
0.60
section
Mobile malware
related to history
GSM-capable
0.60
section
Mobile malware
related to history
Spanish
0.60
section
Mobile malware
related to history
Information
0.60
section
Mobile malware
related to history
Telefónica
0.60
section
Mobile malware
related to history
Internet SMS
0.60
section
Mobile malware
related to history
MoviStar
0.60
section
No triples extracted yet.
Related concept clusters Related term clusters
Related term clusters group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Mobile malware bring nearby vocabulary together. In this analysis, examples include Mobile, Phones and June. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Mobile malware
Mobile
Phones
June
Computer
Programs
Sms
Symbian
Identified
Infects
Information
Virus
System
mobile malware
Mobile
Phones
June
Computer
Programs
Sms
Symbian
Identified
Infects
Information
Android
Devices
mobile phones
Infects
June
Phones
Symbian
Bluetooth
Identified
Computer
Programs
Sms
Os
Virus
System
malware
Mobile
Identified
Phones
Android
Devices
Knowledge
Information
Malicious
Without
Device
Os
System
computer worm
System
Bluetooth
File
Spread
Known
Operating
Mobile
Phones
Virus
Symbian
Trojan
Computer
android
Identified
Ios
Applications
Operating
Malware
Trojan
Devices
Worm
Infects
June
File
Files
worm
Bluetooth
File
Spread
Known
Computer
Infected
Phones
Virus
Mobile
Android
Devices
Identified
notable mobile malicious programs
Programs
Phones
Also
Information
Known
June
Device
Messages
Computer
Sms
Symbian
Identified
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Mobile malware, one of the stronger structural bridges in this analysis connects Mobile malware with Notable mobile malicious programs. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Mobile malware
Nodes58
Edges57
Triples76
Avg. degree1.97
Density0.034483
Components1
Source & methodology
TTTA analyzes the structure around Mobile malware to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Notable mobile malicious programs & Taxonomy, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
