Presenters/Facilitators: Dr. Kyounggon Kim, Ph.D.

Date: 3rd Dec 2025 | Time: 2:30 - 4:30 PM

Cost: 200

Target Audience: Cybersecurity professionals; AI researchers; students in cybersecurity and AI-related fields; law enforcement involved in cybercrime investigations

Prerequisites: Basic knowledge of cybersecurity principles, machine learning techniques, and Python programming (familiarity with libraries such as NumPy, Pandas, and scikit-learn will be beneficial)

Maximum Participants: 20

Description:

This workshop highlights the critical connection between cybersecurity and artificial intelligence (AI), showcasing how AI-driven solutions are transforming the way we detect, analyze, and prevent cybercrime. The integrating of AI into cybersecurity is essential in today’s rapidly evolving threat landscape, where traditional methods struggle to keep up with the volume and sophistication of cyberattacks. Participants will explore how machine learning techniques can be used to identify patterns, predict emerging threats, and automate responses to cyber incidents.

For the hands-on part of the workshop, participants will work with the Phishing Websites Dataset for Machine Learning available on Kaggle, focusing on using machine learning algorithms to detect phishing websites. This practical component will reinforce the theoretical content, allowing participants to train models and gain insights into how AI can be applied in real-world scenarios. Through this, participants will learn how to leverage machine learning techniques to identify fraudulent and malicious website effectively.

By the end of the workshop, participants will have gained in-depth understanding of both the scientific principles and practical applications of AI in cybersecurity. They will also be able to critically evaluate AI-driven solutions in the context of real-world cybercrime scenarios, enhancing their ability to leverage AI tools to improve threat detection, incident response, and overall cybersecurity posture.