Download Udemy – Artificial Intelligence & ChatGPT for Cyber ​​Security 2024 2024-1

1. ChatGPT for Cyber ​​Security / Ethical Hacking – In this section, we delve into the dynamic world of ChatGPT for Cyber ​​Security and Ethical Hacking and explore key topics ranging from addressing mistakes and inaccuracies in ChatGPT to understanding the intricacies of rapid engineering. includes Context request and output formatting. Through hands-on exercises, participants tackle Few-Shot motivations and chain-of-thought motivations, building a solid foundation in the effective use of ChatGPT. In addition, we will provide practical insights to prevent data leakage and explore alternatives to ChatGPT through advanced functions such as data analysis, DALL E integration, and plugin usage.

• Errors and inaccuracies in ChatGPT
• An introduction to agile engineering
• A few shots of stimulation
• Stimulating a chain of thought
• Custom made instructions
• Summarize the data
• Advanced ChatGPT functionality (data analysis, Dalle, plugins)
• ChatGPT alternatives (Bard, Claude, Bing Chat)
• How companies leak their data to ChatGPT

2. The New Age of Social Engineering – In this section, we will uncover the concept of social engineering, detail it, and equip participants with strategies to prevent potential threats. This module explores the implementation of artificial intelligence to explore new social engineering techniques, including sound simulation and deepfaking.

• What is social engineering?
• Sound simulation with Eleven Labs
• Artificial intelligence voice generation with Resemble
• Creating deepfakes with D-ID
• Using ChatGPT to write my style emails
• How to recognize these types of scams

3. Where AI is being used in cybersecurity today – In this section, we examine the forefront of cybersecurity advancements and the integration of AI into critical domains. Students will gain insights into how traditional cybersecurity tools such as firewalls, SIEM systems, IDS/IPS, email filtering, and identity and access management work when AI is applied to them.

• SIEM systems based on artificial intelligence
• Firewall with artificial intelligence
• Email filtering with artificial intelligence
• Artificial intelligence in IAM
• IDS/IPS with artificial intelligence

4. Building an Email Filtering System with Artificial Intelligence – In this section, students will experience a hands-on journey where they use Python programming to implement AI algorithms to create an effective email filtering system. This module not only introduces the basics of email filtering and security, but also provides a comprehensive understanding of spam filters, guiding learners through data analysis, algorithm implementation, and practical comparisons with established systems such as ChatGPT. to give

• An introduction to email security and filtering
• What are spam filters and how do they work?
• Data set analysis
• Training and testing our artificial intelligence system
• Implement spam detection using ChatGPT API
• Comparison of our system with ChatGPT system

5. Building a Phishing Detection System with Artificial Intelligence – In this section, students will gain basic knowledge about phishing and acquire skills to detect phishing attacks. Through hands-on implementation, this module guides learners through the use of decision trees with Python programming, enabling them to build a robust phishing detection system.

• An introduction to phishing
• How to identify and prevent phishing attacks
• Data set analysis
• Split data
• An introduction to decision trees
• Training of random forest algorithm
• Accuracy and recall

6. Artificial Intelligence in Network Security – In this section, students learn the basics of network security and explore traditional practices alongside practical implementations using Python. With the help of logistic regression, learners gain practical experience in building a system for network monitoring.

• An introduction to network security
• Data set analysis
• Data preprocessing
• Data preparation
• logistic regression
• Training logistic regression for network monitoring
• Hyperparameter optimization

7. Artificial Intelligence for Malware Detection – In this section, students conduct a comprehensive exploration of malware types and prevention strategies before building a sophisticated malware detection system. This module guides learners through the training of several algorithms learned during the course, enabling them to evaluate and implement the most accurate solution for a malware detection system.

• What is malware and different types of malware
• Traditional systems for malware detection
• Loading malware dataset
• Malware dataset analysis and preprocessing
• Teaching machine learning algorithms
• Save the best malware detection model

8. Artificial Intelligence Security Risks – In this section, we examine critical AI security risks such as data poisoning, data bias, model vulnerabilities, and ethical concerns. This module provides an in-depth understanding of the potential risks and ethical considerations of implementing artificial intelligence.

• Data poisoning
• Biased data
• Model vulnerabilities
• Ethical concerns

9. Appendix A: Introduction to Cybersecurity – This is the first part of our appendix, a fundamental cybersecurity journey that traces the evolution of cybersecurity and provides insights into tools, techniques, certifications, and best practices. obtains This module acts as a compass and guides learners through the basic principles of cyber security.

• The evolution of cyber security
• Classification of cyber attacks
• Security policies and procedures
• Cyber ​​security tools and technologies
• Familiarity with cyber security certificates
• Cyber ​​security best practices

10. Appendix B: Introduction to Artificial Intelligence – This is the second part of our appendix which is the basics of artificial intelligence, a brief history, various categories such as artificial intelligence, general and extraordinary intelligence and the distinctions between artificial intelligence, machine learning and deep learning. cover.

• A brief history of artificial intelligence
• Types of artificial intelligence: narrow, general and super intelligence
• AI vs. ML vs. Deep Learning
• Fields affected by artificial intelligence
• Machine learning algorithms
• AI ethics and governance

We assure you that this AI in Cybersecurity Bootcamp is designed to be the most comprehensive online course to master the integration of AI into cybersecurity practices!

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