Artificial Intelligence and ChatGPT for Cybersecurity Course 2024. Whether you are an AI enthusiast eager to explore the field of cybersecurity, a student looking to enhance your understanding of the security of the digital landscape, or an experienced developer building Python and artificial intelligence into cybersecurity tools, this is a training course designed for you! Our approach is practical and hands-on, designed to engage you in the dynamic integration of AI and cybersecurity. We believe in learning by doing and guiding you through real-world techniques and methods used by experts in the field. At the beginning of this course, we will get straight into it by showing you how to use ChatGPT for cybersecurity. You will learn practical ways to get the most out of ChatGPT, from understanding its basics to using it for data analysis and other advanced features. After that, we will discuss the following topics:
1. ChatGPT for Cybersecurity/Ethical Hacking – In this section, we dive into the dynamic world of ChatGPT for cybersecurity and ethical hacking and explore key topics ranging from addressing mistakes and inaccuracies in ChatGPT to understanding the complexities of Rapid Engineering. Including reference requests and output formatting. Through practical exercises, participants tackle few-shot motivations and thought-chain 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
- Introduction to Agile Engineering
- some scenes of excitement
- stimulate a chain of thought
- Custom made instructions
- Summarize the data
- Advanced ChatGPT functionality (data analysis, del, plugins)
- ChatGPT Alternatives (Bard, Cloud, Bing Chat)
- How do companies leak their data on ChatGPT?
2. The New Era of Social Engineering – In this section, we will highlight the concept of social engineering, expand on it, and equip participants with strategies to prevent potential threats. This module explores the implementation of artificial intelligence to detect new social engineering techniques including voice simulation and deepfaking.
- What is social engineering?
- Sound simulation with eleven labs
- Artificial intelligence voice generation with sound
- Creating Deepfakes with D-ID
- Using ChatGPT to write my style emails
- How to identify these types of scams?
3. Where is AI being used in cybersecurity today – In this section, we examine the forefront of cybersecurity advancements and explore the integration of AI into critical domains. Students will gain insight 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 practical journey where they use Python programming to implement AI algorithms to build 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 comparison with established systems like ChatGPT.
- 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 the ChatGPT API
- Comparison of our system with the 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 practical implementation, this module guides learners through the use of decision trees with Python programming, enabling them to build a robust phishing detection system.
- Introduction to Phishing
- How to Identify and Prevent Phishing Attacks
- Data set analysis
- Split the data
- Introduction to Decision Trees
- Training the random forest algorithm
- Precision and Recall
6. Artificial Intelligence in Network Security – In this section, students will learn the basics of network security and explore traditional practices along with practical implementation using Python. With the help of Logistic Regression, learners gain practical experience in building a system for network monitoring.
- 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 undertake an extensive exploration of malware types and prevention strategies before building a sophisticated malware detection system. This module guides learners through training on a number of algorithms learned during the course, enabling them to evaluate and implement the most accurate solution for a malware detection system.
- What is malware and how many types of malware are there?
- Traditional systems for malware detection
- Loading the malware dataset
- Malware Dataset Analysis and Preprocessing
- Teaching Machine Learning Algorithms
- Save the best malware detection model
8. AI 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 journey of cybersecurity that traces the evolution of cybersecurity and provides insight into tools, techniques, certifications, and best practices. This module serves as a compass and guides learners through the fundamentals of cybersecurity.
- The Evolution of Cyber Security
- Classification of cyber attacks
- Security Policies and Procedures
- Cybersecurity tools and technologies
- Getting Familiar With Cyber Security Certifications
- Cybersecurity Best Practices
10. Appendix B: Introduction to Artificial Intelligence – This is the second part of our appendix which covers the basics of artificial intelligence, a brief history, different categories of artificial intelligence such as normal and exceptional intelligence, and the difference between artificial intelligence, machine learning, and deep learning.
- A Brief History of Artificial Intelligence
- Types of artificial intelligence: narrow, general and super intelligence
- AI vs ML vs Deep Learning
- Areas affected by artificial intelligence
- Machine Learning Algorithms
- AI Ethics and Governance
We assure you that this AI in Cybersecurity Bootcamp is designed as the most comprehensive online course to master the integration of AI into cybersecurity practices!