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Whether you are an aspiring AI enthusiast eager to delve into the realm of Cyber Security, a student aiming to fortify your understanding of securing digital landscapes, or a seasoned programmer who is looking to implement Python and Artificial Intelligence into Cyber Security Tools, this course is tailored for you!Our approach is hands-on and practical, designed to engage you in the dynamic fusion of Artificial Intelligence and Cyber Security. We believe in learning by doing, guiding you through real-world techniques and methods utilised by experts in the field. At the start of this course, we will dive right in by showing you how to use ChatGPT for Cyber Security. You will learn practical ways to make the most of ChatGPT, from understand its basics to using it for data analysis and other advanced features. After that we will dive into topics like:1. ChatGPT For Cyber Security/Ethical Hacking - In this section, we delve into the dynamic world of ChatGPT for Cyber Security and Ethical Hacking, exploring key topics that range from addressing mistakes and inaccuracies in ChatGPT to understanding the intricacies of prompt engineering, including context prompting and output formatting. Through hands-on exercises, participants will tackle Few-Shot prompting and Chain of thought prompting, building a solid foundation in applying ChatGPT effectively. Additionally we'll navigate through advanced functionalities like Data Analysis, DALL E integration, and plugin utilisation, providing practical insights into preventing data leakage and exploring alternatives to ChatGPT.Mistakes and Inaccuracies in ChatGPTIntroduction to prompt engineeringFew-shot promptingChain of thought promptingBuilding Custom InstructionsSummarising DataAdvanced ChatGPT functionality (Data Analysis, Dalle, Plugins)Alternatives to ChatGPT (Bard, Claude, Bing Chat)How Companies leak their data to ChatGPT2. New Age Of Social Engineering - In this section we unravel the concept of social engineering, delving into its nuances and equipping participants with strategies to prevent potential threats. The module further explores Implementing Artificial Intelligence to explore new social engineering techniques which include voice cloning and creation of deepfakes.What is social engineering ?Voice Cloning with ElevenLabsAI Voice Generating with ResembleCreating deepfakes with D-IDUsing ChatGPT to write Emails in my styleHow to recognise these type of scams3. Where Is AI Used In Cyber Security Today - In this section we explore the forefront of cybersecurity advancements, delving into the integration of AI across critical domains. Students will gain insights into how traditional Cybersecurity tools like Firewalls, SIEM systems, IDS/IPS, Email Filtering and Identity and Access Management work when Artificial Intelligence is applied to them.AI Based SIEM SystemsFirewalls With AIEmail Filtering With AIAI In IAMIDS/IPS with AI4. Building an Email Filtering System With AI - In this section students encounter a hands-on journey, utilising Python programming to implement Artificial Intelligence algorithms for crafting effective email filtering system. This module not only introduces the fundamentals of email filtering and security but also provides a comprehensive understanding of spam filters, guiding learners through dataset analysis, algorithm implementation and practical comparisons with established systems like ChatGPT.Introduction To Email Security and FilteringWhat are Spam filters and how do they work ?Dataset analysisTraining and testing our AI systemImplementing Spam detection using ChatGPT APIComparing our system vs ChatGPT system5. Building a Phishing Detection System With AI - In this section, students will gain essential knowledge about phishing and acquiring skills to recognise phishing attacks. Through practical implementation, this module guides learners in utilising decision trees with Python programming, enabling them to construct a robust phishing detection system.Introduction To PhishingHow to Recognise and Prevent Phishing AttacksDataset AnalysisSplitting The DataIntroduction To Decision TreesTraining Random Forest AlgorithmPrecision and Recall6. AI In Network Security - In this section, students get into the foundations of network security, exploring traditional measures alongside practical implementations using Python. With the help of Logistic Regression, learners gain hands-on experience in building a system for network monitoring.Introduction To Network SecurityDataset AnalysisData Pre-ProcessingData PreparationLogistic RegressionTraining Logistic Regression For Network MonitoringHyperparameter Optimisation7. AI For Malware Detection - In this section students get on a comprehensive exploration of malware types and prevention strategies before delving into the creation of a sophisticated malware detection system. This module guides learners through the training of multiple algorithms learned throughout the course, empowering them to evaluate and implement the most accurate solution for malware detection system.What Is Malware & Different Types of MalwareTraditional Systems for Malware DetectionLoading Malware DatasetMalware Dataset Analysis and Pre-ProcessingTraining Machine Learning AlgorithmsSaving The Best Malware Detection Model8. AI Security Risks - In this section we explore critical Artificial Intelligence security risks such as data poisoning, data bias, model vulnerabilities and ethical concerns. This module dives into deep understanding of potential risks and ethical considerations of Artificial Intelligence Implementation.Data PoisoningData BiasModel VulnerabilitiesEthical Concerns9. Appendix A: Introduction To Cyber Security - This is our first Appendix section which is a cybersecurity foundational journey, tracing the evolution of cybersecurity and gaining insights into essential tools, techniques, certificates and best practices. This module serves as a compass, guiding learners through the core principles of cybersecurity.Evolution Of Cyber SecurityCategories of Cyber AttacksSecurity Policies and ProceduresCyber Security Tools and TechnologiesUnderstanding Cyber Security CertificationsCyber Security Best Practices10. Appendix B: Introduction to Artificial Intelligence - This is our second Appendix section which is Artificial Intelligence fundamentals, covering brief history, diverse categories such as Narrow, General and Super intelligence and the distinctions between AI, machine learning and deep learning.Brief History of AITypes of AI: Narrow, General and SuperintelligenceAI vs ML vs Deep LearningFields influenced by AIMachine Learning AlgorithmsAI Ethics and GovernanceWe assure you that this bootcamp on Artificial Intelligence in Cyber Security is designed to be the most comprehensive online course for mastering integration of AI in cybersecurity practices!

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