Research any topic before you write.

Find related topics. | Discover entities. | See connections. | Build a topical map.

Trojan horse (computing)

In computing, a trojan horse or trojan is a kind of malware that misleads users as to its true intent by disguising itself as a normal program. Trojans are generally spread by some form of social engineering. Although their payload can be anything, many modern forms act as a backdoor, contacting a controller who can then have unauthorized access to the…

Technology, Notable examples & Behavior

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Research this topic

Explore the main themes, entities and connections around Trojan horse (computing). 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.

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Origins of the term

Behavior

Classifications of trojan horses

Linux ls example

Modern developments and detection techniques

Notable examples

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.

Map overview Semantic statistics

Trojan horse (computing)

Nodes80
Edges79
Triples11
Avg. degree1.98
Density0.025
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

Important terminology Word statistics

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

Important terminology

trojan trojans security malware horse systems software system example modern horses program one used access attacks malicious many often computer

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
online banking credentialsinstance ofwhich is designed to steal financial information0.80text
credit card numbersinstance ofwhich is designed to steal financial information0.80text
or cryptocurrency wallet keysinstance ofwhich is designed to steal financial information0.80text
browser cookiesinstance ofcollecting sensitive data0.80text
stored credentialsinstance ofcollecting sensitive data0.80text
or documents without the user’s knowledgeinstance ofcollecting sensitive data0.80text
antivirusinstance ofSecurity software0.80text
anti-malware programs can help detectinstance ofSecurity software0.80text
quarantineinstance ofSecurity software0.80text
and remove trojans when kept up to dateinstance ofSecurity software0.80text
unauthorized privilege escalation or suspicious network communication patternsinstance ofbehavioral analysis monitors system activity for signs0.80text

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
    For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.