Einblick - Machine Learning - # Phishing Website Detection

Detecting Phishing Websites Using a Hybrid Deep Learning Model of Artificial Neural Network and Long Short-Term Memory

Q: What other features or data sources could be incorporated to further improve the performance of the phishing website detection model?

To enhance the performance of the phishing website detection model, additional features and data sources could be incorporated. Some potential options include: Website Content Analysis: Analyzing the content of the website, including text, images, and multimedia elements, could provide valuable insights into the legitimacy of the website. Behavioral Analysis: Incorporating user behavior data, such as mouse movements, click patterns, and time spent on the website, could help in distinguishing between legitimate and phishing websites. Historical Data: Utilizing historical data on known phishing websites and their characteristics could aid in identifying patterns and trends that indicate potential phishing activity. Network Traffic Analysis: Examining network traffic patterns, IP addresses, and communication protocols could offer valuable information for detecting phishing attempts. Social Media Data: Integrating data from social media platforms to analyze user interactions, comments, and shares related to websites could provide additional context for identifying phishing websites.

Q: How would the proposed hybrid model perform on real-world, dynamic datasets that include newly emerging phishing techniques?

The proposed hybrid model, combining Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), is designed to provide high accuracy in detecting phishing websites. When applied to real-world, dynamic datasets with newly emerging phishing techniques, the model's performance may vary. Here's how the hybrid model could perform: Adaptability: The LSTM component of the model, known for its ability to capture long-term dependencies, can help in recognizing new patterns and trends in phishing techniques. Continuous Learning: By updating the model with new data and retraining it regularly, the hybrid model can adapt to evolving phishing strategies and maintain its effectiveness. Early Detection: The ANN component, with its pattern recognition capabilities, can aid in early detection of suspicious websites based on known characteristics of phishing activities. Robustness: The combination of ANN and LSTM can provide a robust defense mechanism against new phishing techniques by leveraging the strengths of both models.

Q: Could the hybrid model be extended to detect other types of cybersecurity threats beyond phishing, such as malware or ransomware?

Yes, the hybrid model could be extended to detect other types of cybersecurity threats beyond phishing, such as malware or ransomware. By modifying the features and training data, the model can be adapted to identify different types of threats. Here's how the hybrid model could be extended: Feature Engineering: Incorporating features specific to malware or ransomware characteristics, such as file behavior, code analysis, or encryption patterns, can enable the model to differentiate between legitimate and malicious entities. Training Data: Utilizing datasets containing examples of malware, ransomware, and other cybersecurity threats, the model can learn to recognize the unique attributes associated with each type of threat. Model Architecture: Adjusting the architecture of the hybrid model to accommodate the detection of various threats, including incorporating different neural network layers or algorithms tailored to specific threat types. Evaluation Metrics: Implementing evaluation metrics specific to malware or ransomware detection, such as false positive rates, true positive rates, and detection accuracy, to assess the model's performance accurately.

Kernkonzepte

A hybrid deep learning model combining Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) achieves high accuracy in detecting phishing websites.

Zusammenfassung

The paper presents a study on detecting phishing websites using machine learning and deep learning approaches. The dataset consists of 10,000 instances, with 5,000 phishing websites and 5,000 legitimate websites, and 48 features.

The authors evaluate the performance of five machine learning models (decision tree, k-nearest neighbor, naive Bayes, logistic regression, and SVM) and four deep learning models (ANN, LSTM, and a proposed hybrid model ANN-LSTM). The key findings are:

The proposed hybrid model ANN-LSTM achieves the highest accuracy of 98%, outperforming the other models.
Logistic regression and ANN also perform well, but not as well as the combined ANN-LSTM model.
The k-nearest neighbor (KNN) classifier has the lowest accuracy of 74% due to the high computational cost of using a large number of neighbors (k=100).
The authors also compare the proposed model's performance with existing models, and the ANN-LSTM model shows the best accuracy.

The paper demonstrates the effectiveness of using a hybrid deep learning approach for the task of phishing website detection, providing a robust solution to this cybersecurity challenge.

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Statistiken

The dataset consists of 10,000 instances, with 5,000 phishing websites and 5,000 legitimate websites.
The dataset has 48 features, including URL-based, content-based, and other characteristics of the websites.

Zitate

"Our proposed hybrid empirical method performs better than other models with 98 percent accuracy and k-Nearest Neighbor performs poorly with an accuracy of 74 percent because the lowest number of k=100 using the large numbers of k is computationally expensive to get the result."

Wichtige Erkenntnisse aus

Phishing Website Detection Using a Combined Model of ANN and LSTM

by Muhammad Sho... um arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.10780.pdf

Phishing Website Detection Using a Combined Model of ANN and LSTM

Tiefere Fragen

What other features or data sources could be incorporated to further improve the performance of the phishing website detection model?

To enhance the performance of the phishing website detection model, additional features and data sources could be incorporated. Some potential options include:

Website Content Analysis: Analyzing the content of the website, including text, images, and multimedia elements, could provide valuable insights into the legitimacy of the website.
Behavioral Analysis: Incorporating user behavior data, such as mouse movements, click patterns, and time spent on the website, could help in distinguishing between legitimate and phishing websites.
Historical Data: Utilizing historical data on known phishing websites and their characteristics could aid in identifying patterns and trends that indicate potential phishing activity.
Network Traffic Analysis: Examining network traffic patterns, IP addresses, and communication protocols could offer valuable information for detecting phishing attempts.
Social Media Data: Integrating data from social media platforms to analyze user interactions, comments, and shares related to websites could provide additional context for identifying phishing websites.

How would the proposed hybrid model perform on real-world, dynamic datasets that include newly emerging phishing techniques?

The proposed hybrid model, combining Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), is designed to provide high accuracy in detecting phishing websites. When applied to real-world, dynamic datasets with newly emerging phishing techniques, the model's performance may vary. Here's how the hybrid model could perform:

Adaptability: The LSTM component of the model, known for its ability to capture long-term dependencies, can help in recognizing new patterns and trends in phishing techniques.
Continuous Learning: By updating the model with new data and retraining it regularly, the hybrid model can adapt to evolving phishing strategies and maintain its effectiveness.
Early Detection: The ANN component, with its pattern recognition capabilities, can aid in early detection of suspicious websites based on known characteristics of phishing activities.
Robustness: The combination of ANN and LSTM can provide a robust defense mechanism against new phishing techniques by leveraging the strengths of both models.

Could the hybrid model be extended to detect other types of cybersecurity threats beyond phishing, such as malware or ransomware?

Yes, the hybrid model could be extended to detect other types of cybersecurity threats beyond phishing, such as malware or ransomware. By modifying the features and training data, the model can be adapted to identify different types of threats. Here's how the hybrid model could be extended:

Feature Engineering: Incorporating features specific to malware or ransomware characteristics, such as file behavior, code analysis, or encryption patterns, can enable the model to differentiate between legitimate and malicious entities.
Training Data: Utilizing datasets containing examples of malware, ransomware, and other cybersecurity threats, the model can learn to recognize the unique attributes associated with each type of threat.
Model Architecture: Adjusting the architecture of the hybrid model to accommodate the detection of various threats, including incorporating different neural network layers or algorithms tailored to specific threat types.
Evaluation Metrics: Implementing evaluation metrics specific to malware or ransomware detection, such as false positive rates, true positive rates, and detection accuracy, to assess the model's performance accurately.