AI for Cybersecurity Threat Detection

Duration: 2 Days • Classroom: Physical • HRDC: Claimable

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What you'll learn
  • Participants will gain an understanding of the basics of Flutter, including how to create layouts, handle user input, manage state, and connect to APIs.
  • Ability to create custom mobile apps.
  • Participants will have Hands-on experience to apply their newly acquired knowledge and skills in a real-world context.
Course description

This workshop empowers cybersecurity professionals to leverage AI for threat detection, anomaly identification, and predictive security analytics. Participants will learn to analyze network and system data, implement AI models for real-time threat monitoring, and automate incident alerts. Emphasis is on practical application, with exercises and real-world datasets allowing participants to deploy AI-driven cybersecurity solutions directly in their workplace. Attendees will explore anomaly detection, predictive threat forecasting, automated alerting, and incident response workflows, gaining hands-on experience in creating AI-powered defense systems. By the end of the workshop, participants will have the skills, confidence, and practical experience to enhance cybersecurity operations, proactively detect threats, and respond effectively to safeguard their organization.

Course content
  • Overview of modern cyber threats: malware, ransomware, phishing, insider threats
  • Attack vectors and threat actors
  • Case studies: AI in real-world threat detection
  • Introduction to AI, machine learning, and deep learning in cybersecurity
  • Benefits of AI-powered detection vs traditional methods
  • Real-time threat detection and predictive capabilities
  • Types of cybersecurity data: network logs, endpoint logs, threat intelligence feeds
  • Data cleaning, normalization, and feature extraction
  • Handling missing or inconsistent data
  • Setting up Python/AI environment, libraries, and cybersecurity datasets
  • Introduction to platforms like Jupyter Notebook, Google Colab, and Security AI platforms
  • Account creation, API keys, and environment configuration
  • Import and clean network and endpoint datasets
  • Explore initial patterns and anomalies
  • Indicators of compromise (IoCs) and behavioral patterns
  • Network traffic analysis fundamentals
  • User and entity behavior analytics (UEBA)
  • Supervised learning for malware and phishing detection
  • Unsupervised learning for anomaly detection
  • Feature selection and model evaluation metrics
  • Stream data analysis for live threat detection
  • Automated alert generation
  • Integration with SIEM (Security Information and Event Management) tools
  • Collecting and integrating external threat intelligence
  • Correlating intelligence with internal logs for better detection
  • Implement anomaly detection model for network traffic logs
  • Generate real-time alerts for suspicious activity
  • Forecasting attack likelihood and potential impact
  • Risk scoring and prioritization
  • Time-series analysis for threat prediction
  • Simulating attacks and response scenarios
  • Evaluating potential business impact and mitigation strategies
  • Automating response actions for detected threats
  • Integration with security orchestration, automation, and response (SOAR) tools
  • Creating AI-powered playbooks for common incidents
  • Deep learning for malware and ransomware detection
  • Graph-based analysis for detecting lateral movement in networks
  • Natural language processing for phishing and social engineering detection
  • Build predictive model for phishing or malware attacks
  • Design and test automated response workflow using AI
  • Define a real-world threat scenario or dataset
  • Build full AI solution: data ingestion -> anomaly detection -> predictive analytics -> automated alerts
  • Visualizing threats and predictions in dashboards
  • Reporting incidents to management using AI-generated insights
  • Model performance monitoring and retraining
  • Continuous improvement of AI threat detection models
  • Handling false positives and tuning detection thresholds
  • Cybersecurity compliance standards
  • Ethical use of AI in security
  • Risk assessment and mitigation best practices
  • Complete and present capstone project
  • Peer review and facilitator feedback
  • Develop roadmap for implementing AI-powered cybersecurity in participants' organizations
  • Review and discussion of tools and concepts covered.
  • Q&A session to address any questions.
Enrol Course Now Contact Us
This course includes:

English

2 Days

Physical Class

Certificate of Completion

HRDC Claimable

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