About This Course
The course explores advanced strategies for employing deep learning in detecting intrusions and analyzing suspicious behaviors in a smart way that ensures the resilience of digital systems against automated attacks. We transition from theoretical foundations to anticipating promising research opportunities, focusing on innovating defensive solutions that outpace attackers' tactics by bridging critical security gaps. This content serves as a knowledge bridge that integrates the rigor of scientific research with the power of technical application, positioning participants at the heart of secure digital transformation and preparing them for leadership in the cybersecurity field enhanced by artificial intelligence.
Course Curriculum
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Defining artificial intelligence as a simulation of the human mind in decision-making.
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The principle of "Agentic AI" for predicting vulnerabilities before they occur and Internet of Things (IoT) security.
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Supervised Learning in Pattern Recognition and Deep Learning
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Generative AI (GenAI) and Explainable AI (XAI).
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Modern Cybersecurity Concepts.
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Adversarial Artificial Intelligence and Network Security.
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SWOT Analysis and Defensive Strengths.
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Emerging Research Trends.
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Artificial Intelligence in Blockchain and Network Packet Analysis.
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Advanced Search in Digital Forensics and Digital Twin.
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Course Quiz
Course Requirements
- Familiarity with the basic concepts of cybersecurity.
- Basic knowledge of the principles of artificial intelligence and machine learning.
- Desire to research and develop in modern technical fields.
- Familiarity with the fundamentals of scientific research.
Learning outcomes
- Understand modern research trends and identify scientific gaps in the field of intelligent cybersecurity.
- The ability to evaluate the effectiveness of artificial intelligence algorithms in detecting threats.
- Acquire skills to analyze modern cyberattacks and how to technically respond to them.
- Build a research roadmap for participants wishing to specialize in this vital field.
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