Automated Phishing Detection with AI Integration Workflow

Automated Phishing Detection and Prevention

1. Data Collection

1.1. Source Identification

Identify and categorize data sources such as emails, websites, and user interactions.

1.2. Data Ingestion

Utilize tools like Apache Kafka or AWS Kinesis to ingest data in real-time for analysis.

2. Data Preprocessing

2.1. Data Cleaning

Remove duplicates and irrelevant information using Python libraries such as Pandas.

2.2. Feature Extraction

Extract relevant features from the data using Natural Language Processing (NLP) techniques.

3. Phishing Detection Model Development

3.1. Model Selection

Select appropriate AI models such as Decision Trees, Random Forests, or Neural Networks.

3.2. Tool Utilization

Implement AI-driven products like TensorFlow or Scikit-learn for model training and evaluation.

4. Model Training

4.1. Data Splitting

Split the dataset into training, validation, and test sets to ensure robust evaluation.

4.2. Training Process

Train the model using labeled datasets of phishing and legitimate emails.

5. Model Evaluation

5.1. Performance Metrics

Evaluate the model using metrics such as accuracy, precision, recall, and F1-score.

5.2. Continuous Improvement

Utilize A/B testing to refine the model based on real-world performance.

6. Deployment

6.1. Integration

Integrate the model into existing email systems using APIs or cloud services like Microsoft Azure or Google Cloud.

6.2. Real-time Monitoring

Set up monitoring systems to track the model’s performance and detect new phishing attempts.

7. User Education and Feedback

7.1. Training Programs

Conduct training sessions for users on recognizing phishing attempts.

7.2. Feedback Loop

Establish a feedback mechanism for users to report suspicious emails, enhancing the model’s learning.

8. Reporting and Analytics

8.1. Dashboard Creation

Utilize tools like Tableau or Power BI to create dashboards for visualizing phishing attempts and trends.

8.2. Incident Reporting

Generate regular reports on phishing incidents and model performance for stakeholders.