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Topic orientation
Phishing at a glance
The strongest research directions include History and Anti-phishing. Use the connected concepts below as starting points, not as a keyword checklist.
Research this topic
Explore the main themes, entities and connections around Phishing. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Explore this topic
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
History
35 related topics
Anti-phishing
23 related topics
Types
15 related topics
Techniques
12 related topics
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Topics to explore
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.Browse the full topic structure. Each item opens a new analysis centered on that subject.
Overview
- Social engineering Social engineering (security)
- Scam
- Sensitive information Information sensitivity
- Malware
- Viruses Computer virus
- Worms Computer worm
- Adware
- Ransomware
- Cybercrime
- Generative AI
- Cracking Security hacker
- AOHell
- Legislation Phishing
- Vectors Attack vector
- Email spam
- Voice phishing
- Cross-site scripting
- MiTM Man-in-the-middle attack
- 2FA
- Spam filters Email filtering
- Machine learning
- Natural language processing
- Browser extensions Browser extension
- Google Safe Browsing
- Microsoft Defender Microsoft Defender Antivirus
- Bitdefender
- Google Chrome
- Microsoft Edge
- Mozilla Firefox
- Safari Safari (web browser)
Types
- Darknet markets Darknet market
- Fancy Bear
- Hillary Clinton
- 2016 presidential campaign Hillary Clinton 2016 presidential campaign
- Voice over IP
- Text-to-speech Speech synthesis
- Text messages SMS
- Private information Personal data
- Exploit kits Exploit kit
- MPack MPack (software)
- Inline frames Framing (World Wide Web)
- Watering hole Watering hole attack
- QR code
- National Cyber Security Centre National Cyber Security Centre (United Kingdom)
Techniques
- Links Uniform Resource Locator
- Misspelled URLs Typosquatting
- Subdomains Subdomain
- Mouse Pointer (user interface)
- Internationalized domain names Internationalized domain name
- IDN spoofing Internationalized domain names
- Homograph attacks IDN homograph attack
- URL redirectors URL redirector
- Latin Latin script
- Cyrillic Cyrillic script
- Fake news
- Fake "virus" notifications Virus hoax
History
- Black hat Black hat (computer security)
- Warez
- AOL
- Instant messages Instant message
- Warez scene
- E-gold
- September 11 attacks
- United Kingdom
- Internal Revenue Service
- Social networking sites Social network service
- Identity theft
- RSA RSA (cryptosystem)
- SecurID RSA SecurID
- Target Target Corporation
- ICloud
- ICANN
- Pentagon The Pentagon
- Bundestag
- Linken The Left (Germany)
- Sahra Wagenknecht
- Junge Union
- CDU Christian Democratic Union of Germany
- Saarland
- World Anti-Doping Agency
- Information security
- Amazon Amazon (company)
- VPN
- Barack Obama
- Elon Musk
Anti-phishing
- FraudWatch International
- Simulated phishing
- National Library of Medicine United States National Library of Medicine
- PayPal
- Educational games Educational game
- Anti-Phishing Working Group
- Federal Trade Commission
- America Online
- Crime rings Criminal organization
- U.S. Secret Service United States Secret Service
- FBI Federal Bureau of Investigation
- Patrick Leahy
- Congress United States Congress
- United States
- Bill Bill (proposed law)
- Fraud Act 2006
- Microsoft
- U.S. District Court for the Western District of Washington United States District Court for the Western District of Washington
- John Doe
- Australian government Government of Australia
- Earthlink
- CAN-SPAM Act of 2003
- Wire fraud
Notable incidents
Advanced semantic analysis
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How this topic connects Entity context
Quick relationship hints grouped by predicate. Useful for spotting recurring semantic connections around the current entity.See the strongest relationship patterns around the current topic before diving into the raw triples.
Phishing
Top relations
related to 2010s · 42
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, In August
related to Legal responses · 25
Phishing → Almeida, America Online, Anti-Phishing Act, Brazil, Californian, Congress, Europe, FBI, Federal Trade Commission, Fraud Act, In, Japanese, June, March, On January, Operation Cardkeeper, Other, Secret Service Operation Firewall, Senator Patrick Leahy, This
related to External links · 24
Phishing → Anti-Phishing Working GroupCenter, Archived, Cambridge, Commons, Computer Laboratory, Duke Law, Explanations, Identity Management, Incentives, Information Protection, Microsoft CorporationDatabase, Notice, PDF, Phishing Attempt, PhishTankThe Impact, Profitless Endeavor, Screenshots, StrategicRevenue, Take-down, Technology ReviewExample
related to 2000s · 18
Phishing → Between May, China, E-gold, Email, In, Internal Revenue Service, June, May, Petersburg, Russian Business Network, September, Social, St, The, The Anti-Phishing Working Group, The United Kingdom, United States, US
related to 2020s · 16
Phishing → Apple Inc, Barack Obama, Bitcoin, BTC, Darcula, Elon Musk, In, Joe Biden, July, PhaaS, Posing, The, Twitter, Twitter's, Using, VPN
related to QR code phishing (quishing) · 15
Phishing → As QR, BiTB, Browser-in-The-Browser, Centre, Cybercriminals, In, QR, QR Code, The, These, UK's National Cyber Security, Unlike, URLs, Users, When
related to Link manipulation · 14
Phishing → An, Another, Cyrillic, However, IDN, IDNs, In, Internationalized, Latin, These, To, URL, URLs, When
related to Spear phishing · 10
Phishing → Accountancy, Fancy Bear, Google, GRU Unit, Hillary Clinton's, SMS, Spear, The Russian, These, Threat Group-4127
see also · 8
Phishing → Anti-phishing, Assuming, Form, Fraud, InternetTrojan HorseTyposquatting, Law, Software, Type
related to Anti-phishing · 7
Phishing → As, FraudWatch International, Millersmiles, Phone, Such, There, These
Important terminology Word statistics
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.
Important terminology
attacks information email users often may used emails user security websites legitimate social attackers attack fake sensitive also login website
Entity relationships Subject–Predicate–Object triples
Extracted RDF-like relationships with confidence and source. The table includes structured facts and lower-confidence contextual relations.| 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 |
No triples extracted yet.
Related concept clusters Concept neighborhoods
Clusters of nearby vocabulary surrounding the topic. Scan them for adjacent concepts and language you may have missed.These clusters group vocabulary that occurs around closely connected concepts in the source material.
Connections between topic areas Semantic bridges
Bridge nodes connect otherwise separate parts of the map. Expand a row to inspect the topic groups on each side.Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.
Map overview Semantic statistics
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.Phishing
Nodes149
Edges148
Triples249
Avg. degree1.99
Density0.013423
Components1
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