Virtual Workshop AI-Powered Cyber Threat Intelligence: Dark Web Analysis & Proactive Defense

About This Course

In this 6-week course, participants dive into AI-powered Cyber Threat Intelligence (CTI), focusing on Dark Web analysis, proactive defense strategies, and using cutting-edge AI tools for threat detection. This course blends theory with hands-on labs, equipping you to anticipate, detect, and counteract emerging cyber threats by integrating AI-driven intelligence into security operations. From understanding threat actors to using AI models for real-time monitoring, you’ll gain practical skills applicable to real-world cyber defense.

Aim

To arm participants with the knowledge and skills to analyze Dark Web data, detect cyber threats using AI/ML techniques, and integrate Cyber Threat Intelligence (CTI) into operations for proactive defense, thus enhancing the security of digital infrastructures against adversaries.

Course Structure

Module 1 — Introduction to Cyber Threat Intelligence (CTI) and Threat Actors

• CTI basics: Lifecycle, types of intelligence, and its importance

• Who’s out there: understanding threat actors, attack vectors, and cybercrime tactics (phishing, malware, APTs)

• Open-Source Intelligence (OSINT) tools: WHOIS, Shodan, and Maltego—how to collect, analyze, and interpret data from public sources

Module 2 — Collection and Processing of Threat Data

• Types of data: open-source, closed-source, and internal data sources

• Data collection techniques: passive vs active, ethical and legal considerations

• Using tools like MISP and ThreatConnect for IOC analysis (Indicators of Compromise)

Module 3 — Advanced Threat Analysis and Attribution

• Techniques: anomaly detection, pattern recognition, and correlation of data

• Malware analysis: static vs dynamic analysis in the CTI process

• Threat attribution: understanding real-world case studies (APT29, Lazarus Group)

• Mapping threat actor behavior to predict future attacks

Module 4 — AI-Powered Threat Detection and Monitoring

• Introduction to AI/ML techniques in CTI for automated detection

• Building and training AI models for threat identification

• Hands-on Lab: Apply AI models for real-time threat monitoring

• Best practices for integrating AI-driven intelligence into daily operations

Module 5 — Operational Integration and Threat Intelligence Sharing

• Integrating CTI into SIEM and SOAR platforms for seamless security operations

• Automating the threat detection and response process

• Intelligence sharing standards: ISACs, STIX/TAXII—how to collaborate with the cybersecurity community for better defense

Module 6 — Capstone Project and Final Exercise

• Simulated Cyber Incident Scenario: Collect, analyze, and prioritize threat data

• Create actionable intelligence reports with recommendations

• Final Exercise: Present your findings and defend your proposed course of action to a simulated executive board

Who Should Enrol?

• Security Analysts, SOC Engineers, and Incident Responders seeking to elevate their CTI skills

• Network/Security Administrators transitioning to CTI roles

• Risk and Compliance Officers aiming for advanced threat intelligence knowledge

• AI/ML Engineers and Data Scientists exploring applications in cybersecurity

• Graduate Students or Professionals in Cybersecurity, AI, or Information Security
  Pre-requisites: Basic understanding of cybersecurity principles and an eagerness to learn AI techniques