File-sharing applications allow users to upload, store, access, and share documents through web-based interfaces. Nextcloud Files provides file-management and sharing functionality that can be deployed within a controlled environment for security testing.
PDF documents are widely used for sharing reports, forms, invoices, and other information. However, a PDF file can contain potentially dangerous structures or embedded content that may be associated with malicious activity. A document's PDF extension or its ability to open successfully does not establish that the document is safe.
In a file-sharing environment, a malicious document may be uploaded and stored before its risk is identified. If the application accepts the file without an additional security-inspection layer, users may subsequently download or distribute the document.
In this use case, Nextcloud Files is deployed in an isolated Ubuntu and Kali Linux laboratory environment. A public PDF-security dataset is used as the existing data source for machine-learning training and evaluation. The dataset is inspected to identify its available benign and malicious document labels and static document features.
XGBoost is used to train a classification model using the selected labelled PDF features. The model learns patterns associated with the classes represented in the training data and predicts whether a submitted document resembles the benign or malicious samples.
A controlled malicious-PDF upload scenario is then performed against the local Nextcloud instance. The uploaded PDF is inspected using the selected static-feature extraction process. The resulting feature vector is provided to the trained XGBoost model, and its prediction is used by a separate validation component to determine whether the upload should be flagged or blocked.
The detection component is implemented separately from Nextcloud's standard file-upload functionality. The project must therefore include an integration mechanism that checks the classification result before the document is accepted for ordinary use. A model prediction alone does not automatically block a file unless this enforcement mechanism is implemented and tested.
Application : Nextcloud Files
Nextcloud Files is the target file-sharing application in this use case. It provides file-upload, storage, retrieval, and sharing functionality for users within the laboratory environment.
The application is deployed within an isolated laboratory so that legitimate PDF uploads and controlled malicious-document testing can be performed without exposing real users or production systems.
File-sharing applications must accept documents in supported formats while preventing potentially harmful files from being distributed through the application.
A PDF file may appear to be an ordinary document based on its filename and extension while containing structures that require additional security inspection. Extension-based checks alone are therefore insufficient to establish that a document is safe.
If uploaded documents are not examined by an effective security-validation mechanism, potentially malicious files may be stored and later accessed by other users.
Nextcloud Files
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User Selects a PDF Document
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PDF Upload Request
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File-Type and Upload Validation
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Document Stored in the Application
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Potentially Malicious PDF Remains Undetected
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Document May Be Downloaded or Shared
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Potential Exposure to Malicious Document Content
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Requirement for Additional Document Security Analysis
The proposed solution adds a machine-learning-based document-classification component to the upload workflow. Static features extracted from a PDF are analyzed by the trained XGBoost model. The resulting classification is passed to an upload-validation component, which applies the configured security policy.
Specific Attack : Controlled Malicious PDF Upload Attack Against Nextcloud Files
The controlled attack scenario evaluates whether the proposed detection mechanism can identify a PDF document associated with the malicious class when it is submitted to the local Nextcloud Files application.
A public PDF-security dataset provides the labelled records used for model development. A suitable PDF sample is selected from the available dataset or its associated downloadable files for controlled laboratory validation.
The selected sample is uploaded only to the isolated Nextcloud instance. Its features are extracted using the same compatible feature-processing procedure used by the trained model.
The XGBoost model classifies the extracted features. The classification result is then passed to the upload-validation component, which applies the configured action, such as flagging or blocking the document.
XGBoost-Based Static PDF Threat Classification and Upload-Time Security Validation
The primary security concept combines static document-feature analysis with machine-learning classification to identify PDF files that resemble known malicious samples before they become available through Nextcloud Files.
Static PDF Feature Analysis : The selected PDF-security dataset is inspected to identify the available document features and security labels. The feature set may include structural properties, metadata, object counts, embedded-content indicators, or other attributes provided by the dataset.
XGBoost-Based Classification : XGBoost learns patterns from the selected labelled document features. The trained model predicts a class for a new PDF based on its extracted features.
The model is evaluated using data that was not used during training. Precision, recall, F1-score, and the confusion matrix are used to examine the classification results, particularly the model's ability to identify malicious samples without incorrectly flagging too many benign documents.
Upload-Time Security Enforcement : A separate integration component connects Nextcloud's upload workflow with the PDF inspection and classification process.When a PDF is uploaded, the component extracts the required features and submits them to the trained model. The classification result is then evaluated against the configured security policy.A suspicious result can trigger a block or quarantine action if that enforcement behavior has been implemented and verified. The system must not assume that XGBoost itself can block an upload; the enforcement component is responsible for applying the decision.
The security concept defines the overall security strategy.
The following are the specific defensive mechanisms implemented.
The uploaded PDF is inspected to extract the document features required by the trained model.
The extracted document features are provided to the trained XGBoost model for classification.
The same compatible feature representation is used for the training dataset and newly uploaded PDF documents.
The classification result is evaluated before the uploaded PDF is accepted for ordinary use.
PDF documents classified as suspicious are recorded for security review.
The upload integration component blocks or quarantines a document when the configured security policy requires that action.
The upload result, model prediction, and relevant document information are recorded.
Predictions and upload decisions are compared with the known labels of the controlled test documents.
Nextcloud Files provides the target application for PDF uploads, storage, and controlled file-sharing security testing.
Ubuntu hosts the Nextcloud deployment and the document-analysis components.
Kali Linux is used as the controlled client environment for submitting PDF files to the laboratory application.
VirtualBox provides the isolated virtual-machine environment for the application and testing systems.
Python implements dataset processing, feature extraction, model training, classification, and upload-policy integration.
XGBoost provides the classification algorithm used to analyze the prepared PDF features.
Pandas organizes the dataset records, document metadata, and classification results.
NumPy supports numerical operations required for feature preparation.
A suitable PDF parsing library, selected according to the actual dataset and required features, is used to inspect document structure and extract compatible static attributes.
Scikit-learn provides dataset-splitting and classification-evaluation utilities.
cURL is used to generate controlled HTTP requests against the authorized Nextcloud environment where appropriate.
OWASP ZAP can be used to inspect HTTP traffic exchanged with the laboratory Nextcloud application.
SQLite stores the relevant upload records, model predictions, and security decisions.
Streamlit provides an interface for reviewing PDF classification and upload-validation results.
Nextcloud Files is deployed within an isolated Ubuntu and Kali Linux laboratory environment to provide the target application for controlled PDF-upload security testing.
An existing public PDF-security dataset is inspected to identify its available security labels and static document features suitable for machine-learning development.
The selected PDF feature records are preprocessed and prepared for XGBoost training, with the original labels preserved for evaluation.
The XGBoost model is trained using the selected labelled PDF features and evaluated using records that were not used during training.
Classification performance is measured using precision, recall, F1-score, and confusion matrix, providing evidence of the model's ability to distinguish the available benign and malicious document classes.
Controlled PDF uploads are performed against the laboratory Nextcloud instance, generating upload requests and application responses for security analysis.
Static features are extracted from compatible uploaded PDF samples and analyzed by the trained XGBoost model to generate document-classification predictions.
The classification predictions are compared with the known test labels, allowing the experiment to document correct classifications, false positives, and false negatives.
A separate upload-validation component applies the configured security policy by allowing, flagging, or blocking uploaded documents according to the implemented and verified enforcement logic.
The complete workflow demonstrates the integration of public PDF-security data, static document-feature analysis, XGBoost classification, Nextcloud upload validation, controlled malicious-PDF testing, and evidence-based detection evaluation within an isolated laboratory environment.
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