Cybersecurity 4.0: Leveraging AI for Threat Detection and Defense
Duration
5 Days (8 hours per day)
Level
Basic to Intermediate Level
Design and Tailor this course
As per your team needs
This course is designed to provide a comprehensive understanding of how Artificial intelligence (AI) can be harnessed to strengthen cybersecurity practices. Participants will start with foundational Python programming tailored for cybersecurity applications, enabling them to build and automate security tools. As the course progresses, learners will delve into advanced machine learning techniques for detecting and mitigating various cyber threats, such as email phishing, malware, and network anomalies.
Participants will also explore cutting-edge AI applications, including AI-driven user authentication and the use of Generative Adversarial Networks (GANs) for simulating cyber threats. The course is highly practical, culminating in a Capstone Project where learners apply their acquired skills to solve real-world cybersecurity challenges, ensuring they are well-prepared to implement AI-enhanced security measures in their organizations.
Outcomes:
Participants will emerge with a strong foundation in AI-driven cybersecurity, equipped to detect and mitigate a wide range of cyber threats using advanced AI techniques. The course empowers learners to implement AI-enhanced security protocols, automate penetration testing, and utilize cutting-edge tools to safeguard digital assets effectively.
Upon completing this course, participants will:
- Gain expertise in using AI algorithms for detecting and mitigating cyber threats
- Acquire practical skills in applying ML to cybersecurity, including anomaly detection and proactive defense strategies
- Develop proficiency in AI-enhanced user authentication methods, including biometric and behavioral analysis
- Learn to automate penetration testing and other security protocols using advanced AI tools
- Experience hands-on application of GANs for simulating and detecting cyber threats, preparing them for real-world challenges
- Cybersecurity professionals seeking to integrate AI into their practices
- IT managers and network administrators aiming to enhance security protocols with AI
- Data scientists interested in applying their skills to cybersecurity challenges
- Anyone involved in cybersecurity who wants to understand and implement AI-driven solutions
- Kick-off with simulated cyber attacks (e.g., phishing, malware)
- Group analysis: Identify potential vulnerabilities and attack vectors
- Introduction to AI concepts and their role in mitigating threats
- Developing a Python-based tool to automate malware detection
- Hands-on Lab: Writing Python scripts to detect simple threats
- Python libraries relevant to cybersecurity (e.g., Scapy, Pandas)
- AI-driven incident response to a data breach
- AI in digital forensics: Leveraging AI for faster threat attribution
- Hands-on Lab: Using AI to analyze logs, detect intrusion patterns, and trace attack origins
- Integrating AI into existing incident response frameworks
- Detecting phishing attempts and network anomalies using AI
- Learning sessions on supervised and unsupervised learning techniques
- Hands-on Lab: Building and deploying a machine learning model for threat detection
- Simulating a cyber attack using AI to find vulnerabilities
- Hands-on Lab: Implementing AI-enhanced tools for penetration testing
- AI-based ethical hacking tools: Identifying and fixing security gaps
- Ethical considerations in AI-powered offensive security
- AI-driven user authentication breach simulation
- Hands-on lab: Implementing biometric and behavioral AI-based authentication methods
- Ethical considerations and privacy challenges
- Predicting cyber threats using AI-powered threat intelligence platforms
- Predictive analytics in cybersecurity
- Hands-on Lab: Building AI models to anticipate and mitigate emerging threats
- Role of AI in proactive threat intelligence and continually improving security posture
- Using GANs to simulate a sophisticated cyber attack
- Hands-on Lab: Developing a GAN to mimic a threat and deploying countermeasures
- Analyzing results and refining threat detection models
- Automating penetration testing to identify and fix vulnerabilities
- Hands-on Lab: Deploying AI tools for continuous monitoring and defense
- Group discussions: AI tool integration in real-time cybersecurity operations
- Participants select a real-world cybersecurity problem
- Team-based project to apply AI techniques
- Presentation of findings and proposed AI-driven solutions
- A basic understanding of computer science (no deep technical expertise required)
- An interest in machine learning, deep learning, and natural language processing
- A willingness to explore ethical and legal aspects of AI and data privacy in cybersecurity
