Enhance Spam Mail Detector to Identify Phishing Techniques and Privilege Escalation Attempts

[mranv]

Description

The current spam mail detector effectively classifies emails as spam or ham using the Naive Bayes model. However, it lacks the capability to identify more sophisticated threats, such as phishing attempts and privilege escalation techniques commonly used in malicious emails. Enhancing the detector to include these features would improve its effectiveness and security.

Proposed Enhancements

1. Dynamic Link Detection:

   • Integrate a feature to analyze and flag dynamic links often used in phishing attacks. These links typically appear legitimate but redirect to malicious sites.
   • Implement checks for URL shortening services and analyze the final destination of shortened URLs.

2. Phishing Technique Identification:

   • Enhance the model to recognize common phishing techniques, such as:
     • Spoofed sender addresses that appear similar to legitimate addresses.
     • Urgent language that pressures the recipient to act quickly.
     • Requests for sensitive information, such as login credentials or financial details.

3. Privilege Escalation Detection:

   • Develop a mechanism to identify emails that attempt to trick recipients into performing actions that grant higher access privileges or sensitive information.
   • Look for common indicators, such as requests to download and execute attachments or click on links to verify accounts.

4. Feature Integration and Testing:

   • Integrate these features into the existing Naive Bayes model or consider additional machine learning techniques if necessary.
   • Conduct extensive testing with a dataset that includes various phishing and privilege escalation examples to ensure accuracy and reliability.

Resources

• Utilize datasets with known phishing and privilege escalation examples from sources like Kaggle or other cybersecurity repositories.
• Reference materials and documentation on common phishing and privilege escalation techniques.

Assignees

• Assign this issue to team members with experience in cybersecurity, machine learning, and Python development.

Milestone

• Set a milestone for the completion of these enhancements within the next development cycle to prioritize this critical update.

Labels

• Enhancement
• Security
• Machine Learning
• Phishing Detection

Additional Information

• Ensure all contributions follow the GitHub Community Guidelines and maintain high code quality and documentation standards.
• Regularly update the progress on this issue and involve the community for feedback and suggestions.

By implementing these enhancements, we can significantly improve the spam mail detector's ability to protect users from sophisticated email-based threats.

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@iiakshat can you modify the data logistics and logic a little bit whois and dynamic link checker for emails so it would combine Cyber Security & Ai together will be a valuable project for future!

[iiakshat]

@mranv Thank you for raising this issue. I have added phishing detection capabilities to the spam mail detector, enhancing its effectiveness in identifying more sophisticated threats. This feature will dynamically analyse links in email to flag potential phishing attempts. Further enhancements and testing will continue to improve accuracy and security. Closing this issue for now.