WIE Speaker Series 2026: AI and Cloud-Powered Security for Critical Infrastructure: Next-Generation Threat Detection and Defense

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Cyberattacks are rapidly evolving in complexity, scale, and unpredictability, posing serious threats to critical infrastructure systems. As organizations face increasingly sophisticated, multi-vector attacks, traditional security approaches are no longer sufficient. This highlights the growing importance of Cyber Threat Intelligence (CTI) in enabling proactive and adaptive defense strategies.

In this talk, I present an advanced deep learning–based intrusion detection framework that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) models. This hybrid architecture is designed to effectively capture both spatial and temporal patterns in dynamic network traffic, improving the detection of complex cyber threats.

The model is trained and evaluated using the UNSW-NB15 dataset, incorporating comprehensive preprocessing techniques such as data cleaning, normalization, and feature optimization. The proposed approach demonstrates exceptional performance, achieving 99.78% accuracy, 98.67% precision, 99.99% recall, and a 98.89% F1-score significantly outperforming traditional machine learning and deep learning models.

This session will provide insights into how AI-driven cybersecurity solutions can enhance threat detection, reduce false positives, and strengthen resilience in critical infrastructure environments. Attendees will gain practical knowledge on applying deep learning techniques to modern cybersecurity challenges.