Phishing Website Detection Model Using Multi-Model Analysis of HTML Content

Keskeiset käsitteet

The author presents an advanced phishing detection model focusing on HTML content, integrating MLP and NLP models to achieve superior performance in identifying phishing activities.

Tiivistelmä

The study addresses the rise of cyber threats, particularly phishing, by introducing a novel detection model that combines Multilayer Perceptron (MLP) for structured data and pretrained Natural Language Processing (NLP) models for textual analysis. The research emphasizes the scarcity of recent datasets for comprehensive phishing studies and contributes by creating an up-to-date dataset reflecting real-life conditions. The proposed MultiText-LP model harmoniously fuses NLP and MLP approaches to achieve impressive results, outperforming existing methods in detecting phishing websites.
The content delves into the methodology employed, including dataset creation from benign and phishing URLs, feature extraction processes encompassing textual and numeric aspects, and the development of models like MLP and pretrained NLP models. Results showcase the effectiveness of the MultiText-LP model with a high F1 score and accuracy on both research and benchmark datasets. The study highlights limitations such as GPU requirements for simultaneous model usage and dataset availability challenges.
In conclusion, the innovative MultiText-LP model emerges as a powerful tool for HTML content classification in phishing detection, surpassing individual NLP or MLP models' performance. Future work aims to integrate URL, HTML content, and WHOIS data while optimizing computational efficiency.

Tilastot

96.80 F1 score achieved by MultiText-LP model.
97.18 accuracy score obtained by MultiText-LP model.
3.4 billion phishing emails sent daily.
$293,359 average wire transfer amount in BEC attacks.
95% social engineering attack motivations are financially driven.

Lainaukset

"The fusion of two NLP and one MLP model achieves impressive results."
"Our approach outperforms existing methods on CatchPhish HTML dataset."

Tärkeimmät oivallukset

Phishing Website Detection through Multi-Model Analysis of HTML Content

by Furk... klo arxiv.org 03-12-2024

https://arxiv.org/pdf/2401.04820.pdf

Syvällisempiä Kysymyksiä

How can the proposed MultiText-LP model be optimized for more efficient computational processing?

To optimize the MultiText-LP model for more efficient computational processing, several strategies can be implemented. Firstly, optimizing the architecture of the model by reducing unnecessary complexity and redundancy can help streamline computations. This may involve fine-tuning the number of layers, nodes, or parameters to strike a balance between performance and efficiency. Additionally, implementing parallel processing techniques such as distributed computing or GPU acceleration can significantly speed up computations by leveraging multiple processors simultaneously.
Furthermore, employing techniques like batch normalization and dropout regularization can enhance training stability and prevent overfitting, leading to faster convergence during training. Utilizing sparse matrix representations for input data where applicable can also reduce memory usage and computation time. Moreover, exploring quantization methods to represent weights with fewer bits without compromising accuracy could further improve computational efficiency.
Incorporating optimization algorithms like stochastic gradient descent with adaptive learning rates (e.g., Adam) or advanced optimizers like RMSprop or AdaGrad can help accelerate convergence during training. Finally, conducting thorough hyperparameter tuning experiments to find optimal settings for learning rate, batch size, activation functions, etc., is crucial in maximizing efficiency while maintaining high performance levels.

What are the implications of limited dataset availability on advancing research in phishing detection?

Limited dataset availability poses significant challenges to advancing research in phishing detection. Without access to diverse and comprehensive datasets that accurately reflect real-world scenarios, researchers may struggle to develop robust models that generalize well across different contexts. Limited datasets hinder benchmarking efforts as researchers may not have sufficient data for comparison against existing methodologies effectively.
Moreover, restricted access to relevant datasets impedes innovation and hinders progress in developing novel approaches for phishing detection. Researchers heavily rely on quality data to train models effectively and evaluate their performance accurately. The lack of diverse datasets limits the exploration of new features or methodologies that could potentially enhance detection capabilities.
Additionally, limited dataset availability may lead to biased models if the available data predominantly represents specific types of phishing attacks or lacks diversity in terms of attack vectors or characteristics. This bias could result in suboptimal generalization when deploying these models in real-world applications where threats constantly evolve.
Addressing dataset scarcity requires collaborative efforts within the research community to create shared repositories of labeled data that cover a wide range of phishing scenarios realistically while ensuring privacy and ethical considerations are upheld.

How might integrating URL,

HTML content,
and WHOIS
data enhance
the overall effectiveness
of phishing detection models?
Integrating URL,
HTML content,
and WHOIS
data into phishing detection models offers a holistic approach towards identifying malicious websites effectively.
By combining information from URLs (such as domain reputation), HTML content analysis (including structural elements indicative
of phishing),
and WHOIS records (providing details about domain registration),
models gain a multi-faceted view
that enhances their ability
to differentiate between legitimate
websites
and potential threats.
URL analysis helps identify suspicious patterns such as redirects or obfuscated links commonly used in phish-ing attacks.
HTML content examination allows for deeper scrutiny into page structure,
content anomalies,
or hidden elements that signify malicious intent.
WHOIS data provides insights into domain ownership history,
registration dates,and contact information which can indicate legitimacy
Combining these sources enables cross-validation
of signals from different perspectives,reducing false positives/negatives associated with individual analyses alone.Integrating URL-based features with HTML content attributes allows for a comprehensive assessment,since attackers often manipulate both aspects.Furthermore,the inclusion of WHOIS information adds another layer
of verification regarding website authenticity.By leveraging all three types
of data,a more robust model is created capable
of detecting sophisticatedphishing attemptsacross various dimensionsenhancing overall effectivenessin identifyingthreatsaccurately