Course Outline
Introduction to Artificial Intelligence in Cybersecurity
- Current Landscape of Cyber Threats
- Use Cases for AI in Cybersecurity
- Overview of Machine Learning and Deep Learning Techniques
Data Collection and Preprocessing
- Sources of Security Data: Logs, Alerts, and Network Traffic
- Data Labeling and Normalization for Government Use
- Strategies for Handling Imbalanced Datasets in a Government Context
Threat Detection and Anomaly Identification
- Supervised vs. Unsupervised Learning for Threat Detection
- Building Classification Models for Intrusion Detection Systems for Government Use
- Clustering Techniques for Identifying Anomalies in Security Data
Security Process Automation with AI
- AI-Driven Automation of Threat Intelligence Analysis for Government Operations
- Utilization of Security Orchestration, Automation, and Response (SOAR) Platforms for Enhanced Cybersecurity
- Case Study: Automating Phishing Detection and Response in a Government Setting
Predictive Analytics for Cybersecurity
- Forecasting Attack Trends Using Time-Series Models for Government Threat Assessment
- Leveraging Natural Language Processing (NLP) on Threat Reports to Enhance Predictive Capabilities for Government Use
- Constructing a Threat Prediction Pipeline for Effective Cyber Defense
Incident Response with Intelligent Systems
- Developing an AI-Powered Incident Response Framework for Government Agencies
- Real-Time Decision-Making in Incident Response Using AI Technologies
- Integration of AI-Driven Systems with Security Information and Event Management (SIEM) and Threat Intelligence Platforms for Enhanced Coordination
AI Tools and Frameworks for Cybersecurity
- Evaluation of Open-Source Tools and Libraries (e.g., Scikit-learn, TensorFlow, Keras) for Government Use
- Selection and Deployment of Platforms for Security Analytics and Automation in a Government Context
- Considerations for Deploying AI Solutions in Government Operations
Ethical and Operational Considerations
- Addressing Bias and Ensuring Fairness in AI Models Used by Government Agencies
- Compliance with Regulations and Standards for Government Cybersecurity Programs
- Promoting Transparency and Explainability in AI-Driven Security Solutions for Government Use
Final Project: AI-Powered Cybersecurity Solution
- Designing and Implementing an AI-Driven Solution to Address a Real-World Cybersecurity Challenge for Government Agencies
- Collaborative Problem-Solving and Development of Effective Security Solutions for Government Use
- Presentation and Feedback on Project Outcomes for Government Stakeholders
Summary and Next Steps
Requirements
- An understanding of fundamental cybersecurity concepts
- Experience with programming or scripting (e.g., Python)
- Familiarity with the basics of machine learning
Audience for Government
- Cybersecurity analysts and engineers
- AI and data science professionals interested in cybersecurity applications
- Security architects and IT managers
Testimonials (5)
Explaining in detail regarding RHDS.
Murat Kumburlu - Westpac Banking Corporation
Course - 389 Directory Server for Administrators
I learned a lot and gained knowledge can use at my work!
Artur - Akademia Lomzynska
Course - Active Directory for Admins
General course information
Paulo Gouveia - EID
Course - C/C++ Secure Coding
Trainer willing to answer questions and give bunch of examples for us to learn.
Eldrick Ricamara - Human Edge Software Philippines, Inc. (part of Tribal Group)
Course - Security Testing
It opens up a lot and gives lots of insight what security