Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks Generated by PhishOracle

Kernkonzepte

PhishOracle, a tool that generates adversarial phishing webpages by embedding diverse content-based and visual-based phishing features into legitimate webpages, can be used to evaluate the robustness of existing phishing webpage detection models.

Zusammenfassung

The paper proposes PhishOracle, a tool that generates adversarial phishing webpages by embedding diverse content-based and visual-based phishing features into legitimate webpages. The generated phishing webpages are used to evaluate the robustness of existing phishing webpage detection models, including the Stack model, Phishpedia, and Gemini Pro Vision.

The key highlights are:

1. PhishOracle can automatically generate adversarial phishing webpages by randomly embedding 12 content-based and 5 visual-based phishing features into legitimate webpages.
2. Evaluation of the Stack model on the PhishOracle-generated dataset shows a significant drop in performance compared to its performance on the clean dataset, with precision dropping from 98.67% to 98.61% and recall dropping from 98.32% to 70.86%.
3. Phishpedia, a state-of-the-art phishing webpage detection model, achieves a precision of 76.40% and a recall of 40% on the PhishOracle-generated dataset, indicating that the logo transformation techniques used by PhishOracle can evade the brand identification capabilities of Phishpedia.
4. Gemini Pro Vision, a large language model, demonstrates robust brand identification capabilities, achieving 95.64% accuracy on the PhishOracle-generated dataset, outperforming the Phishpedia model.
5. A user study shows that on average, approximately 48% of the PhishOracle-generated phishing webpages are misclassified as legitimate by the participants, highlighting the deceptive nature of these adversarial phishing webpages.
6. The authors also develop a PhishOracle web app that allows users to input a legitimate URL, select relevant phishing features, and generate the corresponding phishing webpage.

Statistiken

The accuracy of the Stack model drops from 98.54% on the clean dataset to 84.92% on the PhishOracle-generated dataset.
Phishpedia achieves a precision of 76.40% and a recall of 40% on the PhishOracle-generated dataset.
Gemini Pro Vision achieves a brand identification accuracy of 95.64% on the PhishOracle-generated dataset.

Zitate

"PhishOracle automates the process of phishing webpage generation by autonomously parsing webpage content and selecting suitable HTML tags to embed diverse phishing features."
"Many PhishOracle-generated phishing webpages evade current phishing webpage detection models and deceive users, but Gemini Pro Vision is robust to the attack."

Wichtige Erkenntnisse aus

From ML to LLM: Evaluating the Robustness of Phishing Webpage Detection Models against Adversarial Attacks

by Aditya Kulka... um arxiv.org 09-19-2024

https://arxiv.org/pdf/2407.20361.pdf

Tiefere Fragen

How can the phishing features embedded by PhishOracle be further expanded to create more sophisticated adversarial phishing webpages?

To enhance the sophistication of adversarial phishing webpages generated by PhishOracle, several strategies can be employed to expand the existing phishing features. Firstly, incorporating dynamic content generation techniques could allow for real-time alterations of webpage elements, such as changing text or images based on user interactions. This could include using JavaScript to modify the DOM dynamically, making the phishing webpage appear more legitimate and responsive.
Secondly, integrating social engineering tactics into the phishing features could significantly increase the effectiveness of the generated webpages. For instance, embedding personalized messages that reference the user’s name or recent activities could create a sense of urgency or relevance, prompting users to engage with the phishing content.
Additionally, expanding the visual-based features to include more advanced image manipulation techniques, such as deepfake technology for logos or using style transfer algorithms to mimic the visual aesthetics of legitimate brands, could further deceive users. Implementing A/B testing methodologies within the PhishOracle tool could also allow for the generation of multiple variations of phishing webpages, enabling attackers to identify which designs are most effective at evading detection and deceiving users.
Lastly, incorporating machine learning algorithms to analyze user behavior and adapt the phishing features accordingly could create a more tailored and effective phishing experience. By leveraging user data, PhishOracle could generate phishing webpages that are not only visually similar to legitimate sites but also contextually relevant, thereby increasing the likelihood of successful deception.

What other machine learning or deep learning models, besides the ones evaluated in this paper, could be tested for robustness against the adversarial phishing webpages generated by PhishOracle?

In addition to the Stack model, Phishpedia, and Gemini Pro Vision evaluated in this research, several other machine learning (ML) and deep learning (DL) models could be tested for robustness against adversarial phishing webpages generated by PhishOracle.

Convolutional Neural Networks (CNNs): CNNs are particularly effective for image classification tasks and could be employed to analyze the visual content of phishing webpages. Models like ResNet or Inception could be trained to detect subtle visual discrepancies between legitimate and phishing sites.

Generative Adversarial Networks (GANs): GANs could be utilized to create adversarial examples that mimic phishing webpages, allowing researchers to evaluate the robustness of existing detection models against these generated threats. This approach could help in understanding how well models can generalize to new, unseen phishing tactics.

Recurrent Neural Networks (RNNs): RNNs, especially those with Long Short-Term Memory (LSTM) units, could be applied to analyze the sequential nature of webpage content and user interactions. This could help in detecting phishing attempts that rely on misleading text or deceptive narratives.

Ensemble Learning Models: Techniques such as Random Forests or Gradient Boosting Machines (GBM) could be tested for their ability to combine multiple weak classifiers to improve overall detection accuracy. These models can leverage various features extracted from phishing webpages, including URL characteristics, HTML content, and visual elements.

Natural Language Processing (NLP) Models: Given the increasing use of text in phishing attacks, NLP models like BERT or GPT could be employed to analyze the textual content of phishing webpages. These models could help identify phishing attempts based on linguistic patterns or anomalies in the text.

Anomaly Detection Models: Unsupervised learning techniques, such as Isolation Forests or Autoencoders, could be used to identify outliers in webpage characteristics that deviate from known legitimate patterns, thereby flagging potential phishing attempts.

By testing these additional models, researchers can gain a more comprehensive understanding of the vulnerabilities present in current phishing detection systems and develop strategies to enhance their robustness against evolving adversarial threats.

How can the insights from this research be applied to develop more robust and comprehensive phishing detection solutions that can withstand a wide range of adversarial attacks?

The insights gained from this research can significantly inform the development of more robust and comprehensive phishing detection solutions in several ways:

Adversarial Training: Incorporating adversarial examples generated by PhishOracle into the training datasets of phishing detection models can enhance their robustness. By exposing models to a diverse range of phishing tactics and features, they can learn to recognize and classify these threats more effectively, reducing the likelihood of false negatives.

Feature Diversity: The research highlights the importance of feature diversity in phishing detection. By integrating a broader range of features—both content-based and visual-based—into detection algorithms, developers can create models that are better equipped to identify sophisticated phishing attempts. This includes analyzing not just URLs and HTML content but also visual elements and user interaction patterns.

Continuous Learning: Implementing a continuous learning framework that allows phishing detection models to adapt to new threats in real-time can be crucial. By regularly updating models with new data, including adversarial examples and user feedback, detection systems can remain effective against evolving phishing strategies.

User Education and Awareness: The findings from the user study indicate that even sophisticated detection models can be evaded by well-crafted phishing webpages. Therefore, integrating user education programs that inform individuals about the characteristics of phishing attempts can empower users to recognize and report suspicious activities, complementing automated detection efforts.

Multi-Modal Detection Approaches: Leveraging multi-modal models, such as Gemini Pro Vision, which can analyze both textual and visual inputs, can enhance detection capabilities. By combining insights from different data types, these models can provide a more holistic assessment of webpage legitimacy.

Collaboration and Information Sharing: Establishing collaborative frameworks among organizations to share information about emerging phishing threats and detection techniques can enhance collective defenses. By pooling resources and knowledge, organizations can develop more comprehensive detection solutions that are informed by a wider array of experiences and insights.

By applying these insights, developers can create phishing detection solutions that are not only more resilient to adversarial attacks but also more effective in safeguarding users against the ever-evolving landscape of phishing threats.