Detecting Phishing Websites Using Sequential Deep Learning Models and Contextual URL Features

מושגי ליבה

Deep learning models such as Multi-Head Attention, Temporal Convolutional Network (TCN), BI-LSTM, and LSTM can effectively detect phishing websites by analyzing the contextual features of URLs as sequences.

תקציר

This study focuses on the detection of phishing websites using deep learning models that treat URLs as sequences of tokens. The key findings are:

All four deep learning models (Multi-Head Attention, TCN, LSTM, and BI-LSTM) performed well, achieving high precision, recall, F1-score, and accuracy in detecting phishing websites.

The BI-LSTM model outperformed the other models, with average precision, recall, F1-score, and accuracy values of 0.980. The DQN model had the lowest performance.

The LSTM model required the least training time, while the TCN model required the most.

The results demonstrate the feasibility of using deep learning for generalized phishing detection directly from raw sequential URL data, without relying on manually engineered features.

The Multi-Head Attention model also shows promise as an efficient deep learning technique for this task.

The study provides a comparative analysis of cutting-edge deep learning algorithms for phishing website detection and highlights their potential in practical applications.

סטטיסטיקה

Phishing attacks grew by approximately 34.5% from 2020 to 2021, resulting in 323,972 reported victims.
In 2022, there was a decrease of approximately 7.3% from the previous year's 323,972 victims to 300,497 in social engineering attacks.
The dataset used in the study contains a total of 73,575 URLs, including 36,400 legitimate URLs and 37,175 phishing URLs.

ציטוטים

"Unlike existing research, which relied mainly on static features of Web pages and URL analysis, this paper present a novel comparative analysis of end-to-end deep learning algorithms for detecting phishing websites straight from a given URL link."
"Our findings indicate that all four deep learning models perform similarly and surpass traditional feature-based phishing detection methods that rely on URL syntactical features (i.e., not sequential features)."

תובנות מפתח מזוקקות מ:

The Performance of Sequential Deep Learning Models in Detecting Phishing Websites Using Contextual Features of URLs

by Saroj Gopali... ב- arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09802.pdf

שאלות מעמיקות

How can the performance of these deep learning models be further improved, especially for real-world deployment scenarios with evolving phishing tactics?

To enhance the performance of deep learning models for phishing detection in real-world scenarios with evolving tactics, several strategies can be implemented. Firstly, continuous training and updating of the models with the latest phishing data can help them adapt to new trends and variations in phishing attacks. This can be achieved through regular retraining on fresh datasets to ensure the models stay current and effective.
Moreover, incorporating ensemble learning techniques by combining multiple deep learning models can improve overall performance. Ensemble methods like stacking or boosting can leverage the strengths of individual models to create a more robust and accurate phishing detection system.
Additionally, feature engineering plays a crucial role in model performance. By extracting more relevant and informative features from URLs or incorporating additional contextual information, such as website content or user behavior patterns, the models can better differentiate between legitimate and phishing websites.
Furthermore, implementing advanced anomaly detection algorithms in conjunction with deep learning models can help identify subtle deviations in website behavior that may indicate phishing attempts. By combining the strengths of both approaches, the system can achieve higher accuracy and detection rates.

What are the potential limitations or drawbacks of using deep learning for phishing detection, and how can they be addressed?

While deep learning models offer significant advantages in phishing detection, they also come with certain limitations that need to be addressed. One common drawback is the need for large amounts of labeled data for training, which can be challenging to obtain, especially for rare or emerging phishing tactics. To mitigate this limitation, techniques like transfer learning or semi-supervised learning can be employed to leverage pre-trained models or utilize unlabeled data more effectively.
Another limitation is the potential for overfitting, where the model performs well on training data but fails to generalize to unseen data. Regularization techniques such as dropout layers, batch normalization, or early stopping can help prevent overfitting and improve model generalization.
Moreover, deep learning models may lack interpretability, making it challenging to understand the reasoning behind their predictions. To address this limitation, techniques like model explainability, feature importance analysis, or using interpretable models in conjunction with deep learning can provide insights into the decision-making process of the models.
Lastly, computational resources and training time can be significant barriers to deploying deep learning models in real-time phishing detection systems. Optimizing model architectures, leveraging cloud computing resources, or implementing efficient training strategies like mini-batch processing can help mitigate these challenges.

How can the insights from this study on URL-based phishing detection be extended to other types of cyber threats, such as malware or ransomware detection?

The insights gained from URL-based phishing detection can be extended to other cyber threat detection tasks like malware or ransomware detection through several approaches. Firstly, the concept of treating data as sequences can be applied to analyze file content, network traffic patterns, or system logs to identify malicious activities associated with malware or ransomware.
Additionally, the deep learning architectures and techniques used for URL-based phishing detection, such as LSTM, BiLSTM, TCN, and Multi-Head Attention, can be adapted and fine-tuned for detecting malware signatures or ransomware behavior. By training these models on relevant datasets containing malware samples or ransomware indicators, they can learn to recognize patterns indicative of malicious software.
Furthermore, the methodology of preprocessing data, tokenizing text, and building end-to-end deep learning models can be replicated for malware or ransomware detection tasks. By customizing the input data and labels to suit the specific characteristics of malware or ransomware instances, the models can effectively learn to differentiate between benign and malicious entities.
Overall, leveraging the principles and techniques from URL-based phishing detection can serve as a foundation for developing advanced cyber threat detection systems capable of identifying various types of malicious activities in digital environments.