MC_Scam

Inspiration

With phishing scams on the rise and Canadians losing millions to cybercrime, we wanted to create a tool to protect individuals and businesses from falling victim to fraudulent emails.

What it does

MC_Scam is an AI-powered scam email detector that analyzes email content, flags suspicious elements, and helps users identify potential scams, safeguarding their digital interactions.

How we built it

We combined machine learning algorithms, natural language processing (NLP), and a robust dataset of scam and legitimate emails. Using Python, Pandas, and Django, we trained a model to detect scam patterns while ensuring a user-friendly interface for seamless integration

Challenges we ran into

Sourcing high-quality datasets and ensuring the AI’s accuracy were major hurdles. Balancing false positives and negatives to make the system reliable was particularly challenging.

Accomplishments that we're proud of

We successfully built an accurate, scalable scam detection system that identifies scams with over 95% accuracy. Additionally, we designed an intuitive UI that makes it easy for non-technical users to benefit from the tool.

What we learned

We gained insights into the complexities of phishing tactics and the importance of dataset quality in machine learning. We also learned how critical usability is for effectively adopting security tools.

What's next for MC_Scam

We aim to expand the tool's capabilities, adding multi-language support and browser extensions. Future iterations will also include real-time threat analysis and alerts for organizations to enhance cybersecurity further.