Organizations commonly use web-based email applications to access, read, and manage messages received through organizational or service-based mail infrastructure.
If deceptive email content reaches a user's mailbox without sufficient detection, an attacker may attempt to manipulate the recipient into trusting fraudulent messages, following malicious instructions, disclosing credentials, or accessing attacker-controlled resources. Such weaknesses can expose users and organizations to phishing attacks.
In this use case, the existing Roundcube Webmail application is deployed in an isolated Ubuntu virtual machine. The application provides the target webmail interface used to receive and display controlled phishing email messages.
Kali Linux is used as the controlled security-testing system.
A controlled Phishing Email Content Attack assessment is performed against the authorized laboratory Roundcube Webmail environment. The assessment first establishes normal email communication and then introduces controlled phishing-style email content into the isolated mailbox.
A publicly available phishing email dataset is used as the source for training the AI classification component. The dataset contains labeled legitimate and phishing email samples that can be processed for Transformer-based text classification.
DistilBERT is used as the AI classification model because it can process contextual relationships within email text and classify email content according to learned phishing and legitimate patterns.
Python and the required Transformer and machine-learning libraries are used for dataset preprocessing, model fine-tuning, evaluation, and inference.
The controlled phishing email is then delivered through the isolated mail infrastructure and displayed through Roundcube. The received email content is extracted and provided to the trained DistilBERT model for classification.
Application : Roundcube Webmail
Roundcube Webmail is the target webmail application in this use case.
It provides a browser-based interface for accessing email through an IMAP mail service and uses SMTP-based mail infrastructure for message delivery and sending operations.
A webmail environment may receive email messages containing deceptive subjects, body content, urgent requests, fraudulent account notifications, password-reset instructions, or suspicious links.
If phishing content is not identified before user interaction, the recipient may trust the message and follow attacker-controlled instructions.
Traditional detection mechanisms that depend only on fixed keywords or predefined indicators may not adequately identify modified phishing messages that use different wording or contextual patterns.
The proposed solution introduces phishing-email data preprocessing, DistilBERT-based Transformer classification, controlled phishing email delivery, Roundcube-based application testing, email-content extraction, AI-based classification, and post-attack security validation.
Specific Attack: Phishing Email Content Attack
The controlled attack scenario evaluates whether deceptive phishing email content can be introduced into the authorized Roundcube Webmail environment and subsequently identified by the trained AI classification model.
The objective is to determine whether the trained DistilBERT model can identify phishing characteristics within email content after the simulated phishing message reaches the target webmail application.
The phishing messages used during testing are restricted to the isolated laboratory environment. Any links contained within the controlled messages are directed only toward laboratory-controlled resources or are treated as test indicators.
AI-Assisted Contextual Phishing Email Detection
The primary security concept is AI-Assisted Contextual Phishing Email Detection using Transformer-based text classification and controlled security testing.
The objective is to train a DistilBERT model using publicly available phishing and legitimate email samples, classify email content according to learned contextual characteristics, and validate the trained model against controlled phishing messages introduced into the existing Roundcube Webmail application.
The security concept defines the overall security strategy.
The following are the specific defensive mechanisms implemented.
The DistilBERT model is trained using labeled phishing and legitimate email content and learns contextual relationships within the text used for classification.
Email subject and body content are normalized before being provided to the Transformer model. Relevant textual information is retained while unnecessary formatting is reduced.
The trained DistilBERT model analyzes previously unseen email content and generates a classification indicating whether the message is phishing or legitimate.
Controlled phishing messages are introduced into the isolated Roundcube environment after model training to determine whether the model can detect the simulated attack.
The known classification of controlled test messages is compared with the AI-generated classification result.
DistilBERT is used as the Transformer-based AI classification model for analyzing phishing and legitimate email content.
The Transformers framework provides the tokenizer and model components required to prepare email text, fine-tune DistilBERT, and perform classification.
PyTorch provides the underlying training and inference environment required for DistilBERT model execution.
Roundcube Webmail provides the existing web-based email interface used for receiving and displaying controlled phishing messages.
The local SMTP service is used to submit and deliver controlled email messages within the isolated laboratory environment.
The local IMAP service provides mailbox access to Roundcube and allows the webmail application to retrieve received messages.
Python is used for dataset preprocessing, email-text preparation, model training, evaluation, and inference.
Pandas is used to load, inspect, clean, organize, and transform the email dataset.
NumPy supports numerical operations required during data preparation and model evaluation.
Scikit-learn is used to divide the dataset and calculate classification performance metrics.
Kali Linux provides the controlled security-testing environment used to prepare and introduce phishing email messages.
Ubuntu provides the controlled environment used for hosting the Roundcube webmail and supporting mail infrastructure.
VirtualBox provides the isolated laboratory environment used to host the Ubuntu and Kali Linux systems.
An existing Roundcube Webmail application is successfully deployed in an isolated Ubuntu environment for controlled Phishing Email Content Attack testing and AI-based threat classification.
The Roundcube Webmail application and supporting SMTP/IMAP mail infrastructure are configured to provide a controlled environment for receiving and analyzing laboratory email messages.
A public phishing email dataset is processed to obtain labeled phishing and legitimate email samples required for training and evaluating the AI classification component.
The email data is preprocessed and converted into a suitable textual representation for Transformer-based processing while preserving the original phishing and legitimate classification labels.
A DistilBERT-based Transformer classification model is fine-tuned and evaluated to identify contextual characteristics associated with phishing and legitimate email content.
The trained DistilBERT model is used to classify previously unseen email content and establish its baseline phishing-detection performance before application-level attack validation.
A controlled Phishing Email Content Attack is introduced into the isolated Roundcube Webmail environment through the laboratory SMTP/IMAP mail infrastructure without involving external users or production email systems.
The received phishing email is extracted from the Roundcube environment and provided to the trained DistilBERT model for AI-based contextual analysis and classification.
The known classifications of the controlled phishing and legitimate messages are compared with the AI-generated results to determine the effectiveness of the trained model during practical application-level testing.
The complete workflow of public phishing-email data preparation, DistilBERT model training, Roundcube Webmail deployment, controlled phishing email simulation, email-content extraction, AI-based threat classification, application-level validation, and before-and-after security comparison is successfully demonstrated.
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