This Specialization is designed for post-graduate students aiming to master AI applications in cybersecurity. Through three comprehensive courses, you will explore advanced techniques for detecting and mitigating various cyber threats. The curriculum covers essential topics such as AI-driven fraud prevention, malware analysis, and the implications of Generative Adversarial Networks (GANs). You will gain hands-on experience in identifying anomalies in network traffic, implementing reinforcement learning techniques for adaptive security measures, and evaluating AI model performance against real-world challenges. By completing this Specialization, you will develop a deep understanding of how to secure AI systems while addressing the complexities of adversarial attacks. This knowledge will prepare you to tackle emerging cybersecurity challenges, making you a valuable asset in the rapidly evolving field of digital security. With a focus on practical applications and industry-relevant skills, you will be well-equipped for a career in AI-driven cybersecurity.

Applied Learning Project

In the "AI for Cybersecurity" specialization, learners will apply AI techniques to develop practical cybersecurity tools. They will create both machine learning (ML) and deep learning (DL) models to detect IoT botnet activity in network traffic, emphasizing feature engineering for ML and optimizing raw data for DL. Additionally, participants will design a metamorphic malware detector using a Hidden Markov Model, analyzing opcode sequences to classify them as malware or legitimate software. Throughout these projects, learners will export models, test them on unseen data, and submit video demonstrations along with their code. This hands-on approach equips learners with skills in AI-driven threat detection and model implementation for real-world cybersecurity challenges.