Detecting Typosquatting-Based Malicious Domains Targeting Jenkins Users Through Threat Intelligence Domain Analysis and IOC Correlation

Organizations commonly use Jenkins as a self-hosted automation server for software build, testing, and CI/CD activities. Because Jenkins environments are frequently accessed by developers, administrators, and DevOps teams, attackers may attempt to deceive users by creating malicious domains that closely resemble legitimate Jenkins-related domains.

One such technique is typosquatting, where attackers register domains containing common spelling mistakes, character substitutions, omitted characters, or visually similar variations of a legitimate domain.

A user may accidentally access a look-alike domain believing that it belongs to the legitimate Jenkins environment. The malicious website can then be used for phishing, credential theft, malware delivery, or redirection to additional attacker infrastructure.

Traditional security monitoring may not recognize a newly created look-alike domain simply because it has not yet been classified as malicious.

Cyber Threat Intelligence can address this problem by identifying suspicious domain variations, enriching them with external intelligence, correlating them with internal DNS/network activity, and determining whether Jenkins users are interacting with potentially malicious infrastructure.

In this use case, a self-hosted Jenkins server is deployed on Ubuntu inside an isolated laboratory. A controlled typosquatting scenario is created using dnstwist, which is specifically designed to generate domain permutations and identify potential look-alike domains.

IntelO is used as the CTI platform for collecting and organizing threat intelligence related to suspicious domains and infrastructure.

ThreatFox provides open-source threat intelligence that can be used to enrich and validate indicators associated with malicious infrastructure.

Suricata monitors network activity and DNS-related traffic, while OpenSearch is used for centralized investigation and visualization.

The generated domain variations are analyzed, relevant indicators are enriched with threat intelligence, and controlled DNS/network activity is correlated against the collected intelligence.

The objective is to determine whether potentially malicious typosquatting infrastructure targeting Jenkins users can be identified before it results in a successful compromise.

The complete workflow is: Jenkins → Domain Intelligence → Typosquatting Analysis → Suspicious Domain Identification → CTI Enrichment → IntelO Management → DNS/Network Monitoring → IOC Correlation → Threat Investigation → Risk Assessment → Intelligence Update

Jenkins is the target application in this use case. It is deployed as a self-hosted CI/CD automation platform inside the controlled laboratory. Jenkins may be used for software builds, automated testing, CI/CD pipelines, deployment automation, project integration, and development operations.

An organization may know its legitimate Jenkins domain but may not continuously monitor newly created look-alike domains. This creates a visibility gap in which legitimate Jenkins infrastructure can be imitated by attacker-controlled typosquatting domains.

A user may mistype or follow a malicious link to a typosquatting domain that resembles the legitimate Jenkins domain. The resulting malicious website may facilitate credential theft, malware delivery, or redirection, potentially leading to Jenkins account compromise. Traditional monitoring may fail to identify newly created look-alike domains if they have not yet been classified as malicious.

The security problem is therefore:

The proposed solution introduces domain permutation analysis, typosquatting detection, threat intelligence collection, IOC enrichment, suspicious-domain identification, DNS monitoring, IOC correlation, threat-context analysis, risk-based investigation, and intelligence-driven response.

The controlled attack scenario demonstrates how attackers can create domains that resemble a legitimate Jenkins-related domain. Examples of domain manipulation techniques include character omission, character insertion, character replacement, character transposition, additional characters, hyphenation, repeated characters, and similar-looking domain variations.

The assessment focuses on identifying suspicious domains that resemble the legitimate Jenkins domain and determining whether they are associated with malicious infrastructure. The assessment considers domain similarity, domain permutation type, domain reputation, threat intelligence source, indicator confidence, DNS activity, destination infrastructure, user interaction, domain age where available, associated malicious infrastructure, and potential credential-theft risk.

The primary security concept is Threat Intelligence–Driven Domain Monitoring.

The objective is to identify suspicious domains that resemble legitimate Jenkins infrastructure and determine whether they are associated with malicious activity. The assessment considers domain similarity, domain permutation type, domain reputation, threat intelligence source, indicator confidence, DNS activity, destination infrastructure, user interaction, domain age where available, associated malicious infrastructure, and potential credential-theft risk.

The secure processing flow is: