This project involves the development of a comprehensive machine learning algorithm-based phishing website detection system. Website content, URL structures, and all other relevant factors that contribute to classifying a website as legitimate or phishing will be scrutinized. As such, this effort will be primarily driven by the need to protect users most from the sundry forms of internet scams and identity theft.
The project requires collaboration among data scientists, cybersecurity experts, web developers, and software engineers.
Week 1-2: Initial Planning and Requirement Analysis
Define the objectives and scope of work; define high-level requirements.
Data and resource collection.
Week 3-5: Data Collection and Preprocessing Phase
Legitimate and phishing websites data collection and preprocessing.
Study data for the detection model relevant features.
Week 6-8: Model Development and Training Phase.
Classify websites either as legitimate or phishing based on tests carried out using trained machine learning models.
Evaluate model accuracy and performance against historical data.
Week 9-10: System Integration and Testing Phase
Development of a Phishing Website Detection System.
Integrate this system with other available cybersecurity tools and platforms.
Test and refine the system based on performance metrics and user feedback.
Week 11-12: Deployment and Monitoring Phase
Implementation of the system in the cybersecurity environment.
Monitoring system performance with necessary adjustments.
