Modern web applications depend on third-party software packages to provide application functionality, manage dependencies, and support development. OWASP Juice Shop is an existing open-source web application built using JavaScript technologies and relies on npm packages for its application dependencies.
Typosquatting is a software supply-chain attack in which an attacker uses a package name that closely resembles a legitimate package name. A developer or application may select the unintended package because of a naming mistake, misleading package identity, or an incorrect dependency reference. If the unintended package contains malicious functionality, it may introduce a security risk into the application.
Package metadata provides information such as package names, versions, maintainers, publication dates, dependency declarations, and other available attributes. Examining these characteristics can help identify unusual package records that require further security review.
In this use case, Isolation Forest is used to identify anomalous package metadata associated with potential typosquatting risks in the npm dependency ecosystem. An existing public software-package security dataset is used as the primary data source for model development and evaluation. The dataset is inspected to determine whether it contains suitable package metadata and reliable reference information for the intended analysis.
OWASP Juice Shop is deployed in an isolated VirtualBox laboratory using Ubuntu Linux. Kali Linux provides the controlled security-testing environment. The application's existing npm dependencies are inspected, and a controlled typosquatting scenario is created using a private test registry or simulated package metadata. The test does not require publishing a confusing package to the public npm registry or executing malicious code.
The Isolation Forest model learns the characteristics represented by the selected package metadata and identifies records that deviate from the learned distribution. Its results are correlated with package-name similarity, package identity, registry source, and dependency-resolution evidence to evaluate whether the observed anomaly is consistent with a typosquatting scenario.
Application: OWASP Juice Shop
OWASP Juice Shop is the existing web application used in this use case. It uses JavaScript technologies and npm dependencies to provide its application functionality.
The application is deployed within an isolated laboratory environment. Its dependency manifest and lockfile provide information about the packages used by the application, while npm configuration and dependency-resolution records can help establish where packages are obtained.
OWASP Juice Shop is the target application for examining dependency metadata and validating the proposed detection workflow. The attack scenario is restricted to controlled test dependencies and package metadata.
JavaScript applications commonly rely on third-party npm packages. Selecting the correct package name and package source is important because an unintended dependency can introduce software supply-chain risk.
In a typosquatting attack, a package name is deliberately made similar to a legitimate package name. A developer may accidentally select the similarly named package, or a dependency reference may point to an unintended package.
Package metadata may contain useful indicators of unusual activity, but no single metadata attribute reliably establishes that a package is malicious. A recently published package, for example, is not necessarily suspicious, and an unusual package name does not independently prove typosquatting.
Traditional dependency review may focus on known vulnerabilities, exact package names, or predefined rules. Such checks may not identify every suspicious package, particularly when the package does not have a known vulnerability or the naming difference has not been explicitly recorded.
The proposed solution uses Isolation Forest to identify unusual package metadata and combines its output with package identity and dependency-resolution evidence. The resulting findings are reviewed to determine whether the controlled scenario demonstrates typosquatting-related risk.
Specific Attack: Typosquatting Attack Against npm Dependencies
The controlled attack scenario evaluates whether a package with a name resembling a legitimate dependency can be identified as a potential supply-chain risk during dependency inspection.
OWASP Juice Shop is deployed as the existing target application. Its dependency manifest and lockfile are inspected to identify a suitable package for the controlled test.
A test package name is created by introducing a small naming variation to a selected legitimate package name. The test is performed using a private laboratory registry or simulated package metadata. The test package is not published to the public npm registry, and malicious code is not executed.
The controlled scenario examines whether the package metadata and dependency-resolution evidence can be collected and evaluated by the proposed detection workflow.
Isolation Forest analyzes the prepared metadata features and identifies records that deviate from the learned package population. The anomaly result is then correlated with the original package name, the test package name, the package source, and the dependency-resolution information.
The objective is to determine whether the detection workflow identifies the controlled suspicious package record and provides sufficient evidence for security review. An anomaly prediction alone is not treated as proof of a successful attack.
Isolation Forest-Based Package Metadata Anomaly Detection for Typosquatting Investigation
The primary security concept is package metadata anomaly detection combined with controlled software supply-chain security testing. The objective is to identify unusual package records that may require investigation for typosquatting-related risks.
The first objective is to process an existing public software-package security dataset and identify metadata attributes suitable for the analysis. Relevant features may include package-name characteristics, version information, publication timestamps, maintainer-related attributes, dependency information, and other fields supported by the selected dataset.
The second objective is to train Isolation Forest using the selected package metadata. The model identifies observations that are relatively easy to isolate from the learned data distribution. These anomalies are treated as candidates for review rather than automatically classified as confirmed malicious packages.
The third objective is to evaluate the trained model using suitable held-out data and a controlled typosquatting scenario involving OWASP Juice Shop dependencies. The anomaly results are compared with package-name similarity, package identity, registry source, and dependency-resolution evidence to determine whether the controlled test is correctly identified.
The security concept defines the overall security strategy.
The following are the specific defensive mechanisms implemented.
Package metadata is collected from the existing public dataset and the controlled OWASP Juice Shop dependency-testing environment.
Selected metadata fields are cleaned, standardized, and transformed into a consistent feature representation.
Isolation Forest evaluates the processed package metadata and generates anomaly predictions and scores.
The names of suspicious package records are compared with known legitimate package names.
Package names, registry sources, versions, and available package identity information are examined.
The dependency-resolution results are examined to determine which package source and package identity the application would use in the controlled environment.
Package records identified as anomalous and supported by additional suspicious indicators are flagged for security review.
Package metadata, model results, and dependency-resolution evidence are reviewed before accepting the package.
Isolation Forest results are evaluated using suitable held-out records and known controlled test cases.
The attack simulation is performed using a private test registry or simulated package metadata.
OWASP Juice Shop is the existing JavaScript web application used to examine npm dependencies and validate the controlled security scenario.
Node.js and npm provide the JavaScript runtime and package-management functionality required to inspect dependencies.
Isolation Forest is the primary machine-learning algorithm used for package metadata anomaly detection.
Scikit-learn provides the Isolation Forest implementation and supporting evaluation utilities.
Python provides the main implementation environment for data processing and anomaly analysis.
Pandas is used to organize the public dataset and collected package metadata.
NumPy supports numerical transformations and feature-array preparation.
Git is used to obtain and inspect the existing OWASP Juice Shop source repository.
Kali Linux provides the controlled security-testing environment.
Ubuntu Linux hosts the application and machine-learning workflow according to the laboratory configuration.
VirtualBox provides the isolated virtual-machine environment.
SQLite stores relevant package records, anomaly results, and validation evidence.
OWASP Juice Shop is deployed in an isolated laboratory as the existing JavaScript application used for dependency-security analysis.
An existing public software-package security dataset is inspected and processed to determine its suitability for package metadata anomaly detection.
Relevant package metadata is transformed into numerical features suitable for Isolation Forest analysis.
The Isolation Forest model is trained and evaluated using the selected package metadata records.
A controlled typosquatting scenario is prepared using a private test registry or simulated package metadata without publishing confusing packages to the public npm registry.
The dependency-resolution test provides evidence about the relationship between the legitimate package name, the controlled test package, and the selected package source.
The trained model generates anomaly predictions and scores for the controlled package metadata.
Anomaly results are correlated with package-name similarity, package identity, registry source, and dependency-resolution evidence to assess the controlled typosquatting scenario
The experiment documents false positives, missed anomalies, and limitations arising from the dataset, metadata features, and availability of reliable reference information.
The complete workflow demonstrates how Isolation Forest-based package metadata anomaly detection can support typosquatting investigation in OWASP Juice Shop dependencies, while keeping final security decisions dependent on verified package identity and dependency-resolution evidence.
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