Phishing

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Phishing topic overview

The analysis highlights History and Technology as prominent areas in the source structure around Phishing.

Related topics: 141
Source areas: 6
Connected nodes: 148
Extracted relationships: 173
Related term clusters: 31
Bridge connections: 148

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.

Overview · 55 topics

History · 35 topics

Anti-phishing · 23 topics

Types · 15 topics

Techniques · 12 topics

Notable incidents · 1 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.

Start with your topic. Discover where to go next.

Explore different angles and find fresh ideas to shape your next piece of content.

Phishing

10Social engineering (security) · Scam · Information sensitivity

25Security hacker · AOHell · Phishing

6Microsoft · United States District Court for the Western District of Washington · John Doe

17Browser extension · Google Safe Browsing · Microsoft Defender Antivirus

Explore all related topics Closing gaps

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.

Types

Techniques

Notable incidents

2016–2021 literary phishing thefts

For the semantics nerds

You can skip this section if you’re here for content ideas and keyword inspiration.

Advanced semantic analysis

How Phishing connects Entity context

The extracted context around Phishing shows recurring relationship patterns in the source. For example, Phishing → According, Amazon, Amazon Prime Day, Apple, August, Bundestag, CDU, Centralized Zone Data System, CEO, Chinese, Democratic National Committee, EOS, Fancy Bear, Ghosh, Google, Governmental Advisory Committee, ICANN, ICANN's, In August, In November Another extracted example is Phishing → Almeida, America Online, Anti-Phishing Act, Brazil, Californian, Congress, Europe, FBI, Federal Trade Commission, Fraud Act, Japanese, June, March, On January, Operation Cardkeeper, Secret Service Operation Firewall, Senator Patrick Leahy, UK, United States, US. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

related to 2010s · 40

Phishing → According, Amazon, Amazon Prime Day, Apple, August, Bundestag, CDU, Centralized Zone Data System, CEO, Chinese, Democratic National Committee, EOS, Fancy Bear, Ghosh, Google, Governmental Advisory Committee, ICANN, ICANN's, In August, In November

related to Legal responses · 22

Phishing → Almeida, America Online, Anti-Phishing Act, Brazil, Californian, Congress, Europe, FBI, Federal Trade Commission, Fraud Act, Japanese, June, March, On January, Operation Cardkeeper, Secret Service Operation Firewall, Senator Patrick Leahy, UK, United States, US

related to 2000s · 16

Phishing → Between May, China, E-gold, Email, Internal Revenue Service, June, May, Petersburg, Russian Business Network, September, Social, St, The Anti-Phishing Working Group, The United Kingdom, United States, US

related to 2020s · 14

Phishing → Apple Inc, Barack Obama, Bitcoin, BTC, Darcula, Elon Musk, Joe Biden, July, PhaaS, Posing, Twitter, Twitter's, Using, VPN

related to QR code phishing (quishing) · 11

Phishing → As QR, BiTB, Browser-in-The-Browser, Centre, Cybercriminals, QR, QR Code, UK's National Cyber Security, Unlike, URLs, Users

related to Spear phishing · 9

Phishing → Accountancy, Fancy Bear, Google, GRU Unit, Hillary Clinton's, SMS, Spear, The Russian, Threat Group-4127

related to Link manipulation · 8

Phishing → Another, Cyrillic, IDN, IDNs, Internationalized, Latin, URL, URLs

related to User training · 5

Phishing → Effective, Medicine, National Library, One, Simulated

related to history · 4

Phishing → AOHell, AOL, Khan, Smith

related to Man-in-the-Middle phishing · 4

Phishing → Evilginx, Man-in-the-Middle, MitM, Traditional

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

attacks information email users often may used emails user security websites legitimate social attackers attack fake sensitive also login website

Phishing relationships Subject–Predicate–Object triples

TTTA extracted 173 structured relationships around Phishing. Examples in this analysis include Phishing → is a → form of social engineering and a scam where attackers deceive people into revealing sensitive information or installing malware such as viruses and Phishing → is a → use of fake news articles to trick victims into clicking on a malicious link. The table shows each extracted connection, where it came from and its confidence.

Subject	Predicate	Object	Confidence	Src
Phishing	is a	form of social engineering and a scam where attackers deceive people into revealing sensitive information or installing malware such as viruses	0.90	text
Phishing	is a	use of fake news articles to trick victims into clicking on a malicious link	0.90	text
viruses	instance of	Phishing is a form of social engineering and a scam where attackers deceive people into revealing sensitive information or installing malware	0.80	text
worms	instance of	Phishing is a form of social engineering and a scam where attackers deceive people into revealing sensitive information or installing malware	0.80	text
adware	instance of	Phishing is a form of social engineering and a scam where attackers deceive people into revealing sensitive information or installing malware	0.80	text
or ransomware	instance of	Phishing is a form of social engineering and a scam where attackers deceive people into revealing sensitive information or installing malware	0.80	text
login credentials or financial details.Spear phishingSpear phishing attacks are often more effective than general phishing attempts because they are tailored to specific individuals	instance of	encouraging victims to disclose sensitive information	0.80	text
leverage personal or organizational information to increase credibility	instance of	encouraging victims to disclose sensitive information	0.80	text
success rates	instance of	encouraging victims to disclose sensitive information	0.80	text
MPack into compromised websites to exploit legitimate users visiting the server	instance of	Hackers may insert exploit kits	0.80	text
on advertisements or car park notices	instance of	or hard copy stickers placed over legitimate QR codes on	0.80	text
login credentials or financial details	instance of	encouraging victims to disclose sensitive information	0.80	text

Related concept clusters Related term clusters

The concept neighborhoods around Phishing bring nearby vocabulary together. In this analysis, examples include Attacks, Information and Users. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

social engineering
- Social
- Sensitive
- Links
- Techniques
- Information
- Including
- Attackers
- Malicious
- Emails
- Often
- Legitimate
- Websites
sensitive information
- Sensitive
- Social
- Personal
- Login
- Phishing
- Users
- Often
- Spear
- Qr
- Techniques
- Attacks
- Also
private information
- Sensitive
- Personal
- Login
- Phishing
- Users
- Spear
- Attacks
- Social
- Techniques
- Anti-phishing
- Website
- Security
information security
- Sensitive
- Personal
- Login
- Phishing
- Users
- Spear
- Attacks
- Social
- Google
- Qr
- Techniques
- Anti-phishing
anti-phishing working group
- Google
- Information
- Websites
- Email
- Links
- Personal
- Qr
- Techniques
- Engineering
- Spear
- Phishing
- Including
anti-phishing
- Google
- Information
- Websites
- Email
- Links
- Personal
- Qr
- Techniques
- Engineering
- Spear
- Phishing
- Including
Phishing
- Attacks
- Information
- Users
- Email
- Emails
- Spear
- Attack
- Techniques
- Social
- Security
- User
- Often
phishing
- Attacks
- Information
- Users
- Email
- Emails
- Spear
- Attack
- Techniques
- Social
- Security
- User
- Often

Connections between topic areas Semantic bridges

For Phishing, one of the stronger structural bridges in this analysis connects Phishing with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

Phishing — Overview · splits 92 ⟂ 57

Phishing — History · splits 113 ⟂ 36

Phishing — Anti-phishing · splits 125 ⟂ 24

Phishing — Types · splits 133 ⟂ 16

Phishing — Techniques · splits 136 ⟂ 13

Map overview Semantic statistics

Phishing

Nodes149

Edges148

Triples173

Avg. degree1.99

Density0.013423

Components1

Source & methodology

TTTA analyzes the structure around Phishing to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History & Technology, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Phishing · EN edition · Analysis: TopicsToTalkAbout

Phishing: History & Technology

Phishing topic overview

What this topic covers Research coverage

Key facts & relationships

Start with your topic. Discover where to go next.

Questions to explore

Explore all related topics Closing gaps

Overview

Types

Techniques

History

Anti-phishing

Notable incidents

For the semantics nerds

Advanced semantic analysis

How Phishing connects Entity context

Phishing

Top relations

Important terminology

Important terminology

Phishing relationships Subject–Predicate–Object triples

Related concept clusters Related term clusters

Connections between topic areas Semantic bridges

Map overview Semantic statistics

Phishing

Source & methodology