phish-filibusterer

An AI-based, ML-trained systemwide program offering anti-phishing protection services.

phish-filibusterer detects and flags fraudulent emails from untrusted/unverified domains for legitimate businesses/corporations; identifies potential cyberattackers/perpetrators -- on social media platforms, online 3rd-party resale apps, e-marketplaces, etc.

The project also utilizes regular expressions (regex) to detect phishing emails related to P2P or B2B payment apps, specifically during periods of dramatic spikes in phishing scams. By leveraging AI and ML, phish-filibusterer aims to bolster cybersecurity and protect users from phishing attacks effectively.

Python would be the most suitable coding language for this project due to its extensive libraries and frameworks for AI, ML, and regex operations.

Basic Workflow:

1. Data Collection: The project will start by collecting and aggregating relevant data related to phishing attacks, known fraudulent domains, and cyber attackers. This data will serve as a training dataset for the ML model.
2. Machine Learning Model Training: Utilizing the collected dataset, the AI/ML model will be trained to recognize patterns and characteristics of phishing emails and cyber attackers. The model will be capable of identifying suspicious emails and potential perpetrators based on various features.
3. Email Phishing Detection: phish-filibusterer will integrate with email servers to scan incoming emails for signs of phishing attempts. The AI model will analyze email content, headers, and sender information to determine the likelihood of a phishing attack.
4. Fraudulent Domain Detection: The program will monitor internet traffic and identify untrusted/unverified domains used in phishing campaigns. It will flag websites and URLs that pose a threat to legitimate businesses/corporations.
5. Social Media and Platform Monitoring: phish-filibusterer will extend its detection capabilities to social media platforms, online resale apps, e-marketplaces, and other relevant platforms. The AI model will analyze user interactions and content to spot potential cyber attackers and fraudulent activities.
6. P2P and B2B Payment App Monitoring: The project will use regex to identify phishing emails that exploit P2P or B2B payment apps during periods of increased phishing activity. It will provide timely alerts and protection for users of these apps.

Basic I/O Details:

1. Input: Data related to phishing attacks, fraudulent domains, and cyber attackers for model training.

• Incoming emails, social media content, and platform interactions for real-time monitoring.

2. Output: Phishing email detection alerts for users and email administrators.

• Flags for fraudulent domains and potentially malicious websites.
• Notifications and alerts regarding potential cyber attackers on various platforms.

Project Goals and Advantages:

1. Enhanced Cybersecurity: phish-filibusterer aims to provide advanced protection against phishing attacks and cyber threats, safeguarding businesses and individuals from potential losses.
2. Real-Time Monitoring: By utilizing AI and ML, the program offers real-time monitoring and quick responses to emerging phishing threats and cyber attackers.
3. Proactive Detection: The project's regex-based detection of phishing emails during spikes in scams helps users stay alert and protected during critical periods.
4. Holistic Protection: phish-filibusterer extends its protection beyond emails and covers various platforms where cyber attackers may operate, offering comprehensive anti-phishing services.

With phish-filibusterer, users and businesses can have confidence in their cybersecurity measures, reducing the risk of falling victim to phishing attacks and fraudulent activities across different online platforms.