Create an intelligent system using AI/ML to detect phishing domains which imitate look and feel of genuine domains

Phishing

Phishing is a type of cybercrime that involves tricking people into providing sensitive information. The scammer poses as a legitimate institution and contacts the target by email, telephone, or text message. The goal is to get the victim to visit a website that may download a virus or steal personal information.

Flow Chart :

Objective :

Creating an intelligent system to detect phishing domains that imitate genuine domains is a complex task that involves various AI and machine learning techniques. Here's a high-level overview of the steps involved:

The steps are :

Data Collection:

Gather a large dataset of known phishing and legitimate domains. Include features like domain names, registration details, SSL certificates, and webpage content..

Feature Engineering:

Extract relevant features from the dataset, such as domain length, subdomain count, special characters, IP address reputation, SSL certificate issuer, and more.

Model Selection:

Choose machine learning models suitable for the task, such as Random Forest, Gradient Boosting, or Deep Learning models like neural networks.

Labeling:

Label the dataset to indicate whether each domain is phishing or legitimate. This requires manual or supervised labeling.

Data Preprocessing:

Normalize and preprocess the data, handling missing values, encoding categorical features, and splitting it into training and testing sets.

Model Training:

Train the selected model(s) using the labeled dataset, and tune hyperparameters for optimal performance.

Feature Importance:

Determine which features are most important for phishing detection. This helps in understanding the decision-making process of the model.

Evaluation:

Assess the model's performance using metrics like accuracy, precision, recall, F1-score, and ROC AUC on the test dataset.

Model Deployment:

Deploy the trained model as an API or service that can accept domain names as input and provide predictions.

Real-time Scanning:

Implement a system that continuously scans new domain registrations and web content. When a domain is flagged as suspicious, investigate further.

Feedback Loop:

Continuously update and retrain the model with new data to adapt to evolving phishing techniques.

User Feedback:

Allow users to report phishing attempts and use this feedback to improve the system.

Collaboration:

Collaborate with domain registrars, internet service providers, and cybersecurity organizations to share threat intelligence.

Explainability:

Implement methods to make the model's decisions more interpretable, so security professionals can understand why a domain is flagged.

False Positive Handling:

Minimize false positives by fine-tuning the model and incorporating feedback from users and security analysts.

--:So by this we can get control on under going phishing :--

Thank you

From Team Code Acer