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Various anti-spam techniques are used to prevent email spam (unsolicited bulk email).
Applications & Research
Explore the main themes, entities and connections around Anti-spam techniques. Start with the topic map, then use the sections below for research and deeper semantic analysis.
Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.
High-confidence facts extracted from structured source data. Use them as anchors for further research.
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See the strongest relationship patterns around the current topic before diving into the raw triples.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
spam email address spammers messages mail use smtp message may server addresses many used users systems also sender send legitimate
| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| LinkedIn to gather personal | instance of | Some phishing campaigns use professional networking platforms | 0.80 | text |
| employment details | instance of | Some phishing campaigns use professional networking platforms | 0.80 | text |
| enabling attackers to craft convincing messages that appear to come from coworkers | instance of | Some phishing campaigns use professional networking platforms | 0.80 | text |
| recruiters | instance of | Some phishing campaigns use professional networking platforms | 0.80 | text |
| or human resources departments | instance of | Some phishing campaigns use professional networking platforms | 0.80 | text |
| SpamCop | instance of | Some online tools | 0.80 | text |
| Network Abuse Clearinghouse are potentially helpful but not always accurate | instance of | Some online tools | 0.80 | text |
| the Distributed Checksum Clearinghouse which collects the checksums of messages that email recipients consider to be spam | instance of | and look that checksum up in a database | 0.80 | text |
| Spamhaus' Domain Block List | instance of | a popular technique since the early 2000s consists of extracting URLs from messages and looking them up in databases | 0.80 | text |
| blocking the message or shutting off the source of the traffic | instance of | identifying spam messages and then taking an action | 0.80 | text |
| SpamCop | instance of | but large spam runs can be slowed down until manual investigation can be done.Spam report feedback loopsBy monitoring spam reports from sources | 0.80 | text |
| AOL's feedback loop | instance of | but large spam runs can be slowed down until manual investigation can be done.Spam report feedback loopsBy monitoring spam reports from sources | 0.80 | text |
These clusters group vocabulary that occurs around closely connected concepts in the source material.
Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.