AI+ Ethical Hacker

Hours: 40 / Access Length: 12 Months / Delivery: Online, Self-Paced
Online Hours: 40
Retail Price: $495.00

Course Overview:

The AI+ Ethical Hacker course validates knowledge of the intersection of cybersecurity and artificial intelligence, a pivotal juncture in an era of rapid technological progress. Designed for cybersecurity professionals and ethical hacking practitioners, it assesses comprehensive knowledge of AI’s impact on digital offense and defense strategies. Unlike conventional ethical hacking certifications, this certification validates competency in applying AI techniques to enhance cybersecurity approaches. It is intended for professionals seeking to validate expertise in the integration of advanced AI methods with ethical hacking practices in a rapidly evolving digital landscape.

Recommended Prerequisites:
  • Programming Proficiency: Knowledge of Python, Java, C++, etc for automation and scripting.
  • Networking Fundamentals: Understanding of networking protocols, subnetting, firewalls, and routing.
  • Operating Systems Knowledge: Proficiency in using Windows and Linux operating systems.
  • Cybersecurity Basics: Familiarity with fundamental cybersecurity concepts, including encryption, authentication, access controls, and security protocols
  • Machine Learning Basics: Understanding of machine learning concepts, algorithms, and basic implementation.
  • Web Technologies: Understanding of web technologies, including HTTP/HTTPS protocols, and web servers.

Course Outline:

Lesson 1: Foundation of Ethical Hacking Using Artificial Intelligence (AI)
  • 1.1        Introduction to Ethical Hacking
  • 1.2        Ethical Hacking Methodology
  • 1.3        Legal and Regulatory Framework
  • 1.4        Hacker Types and Motivations
  • 1.5        Information Gathering Techniques
  • 1.6        Footprinting and Reconnaissance
  • 1.7        Scanning Networks
  • 1.8        Enumeration Techniques
Lesson 2: Introduction to AI in Ethical Hacking
  • 2.1        AI in Ethical Hacking
  • 2.2        Fundamentals of AI
  • 2.3        AI Technologies Overview    
  • 2.4        Machine Learning in Cybersecurity
  • 2.5        Natural Language Processing (NLP) for Cybersecurity
  • 2.6        Deep Learning for Threat Detection
  • 2.7        Adversarial Machine Learning in Cybersecurity
  • 2.8        AI-Driven Threat Intelligence Platforms
  • 2.9        Cybersecurity Automation with AI
Lesson 3: AI Tools and Technologies in Ethical Hacking
  • 3.1        AI-based Threat Detection Tools
  • 3.2        Machine Learning Frameworks for Ethical Hacking
  • 3.3        AI-Enhanced Penetration Testing Tools
  • 3.4        Behavioral Analysis Tools for Anomaly Detection
  • 3.5        AI-Driven Network Security Solutions
  • 3.6        Automated Vulnerability Scanners
  • 3.7        AI in Web Application
  • 3.8        AI for Malware Detection and Analysis
  • 3.9        Cognitive Security Tools
Lesson 4: AI-Driven Reconnaissance Techniques
  • 4.1        Introduction to Reconnaissance in Ethical Hacking
  • 4.2        Traditional vs. AI-Driven Reconnaissance
  • 4.3        Automated OS Fingerprinting with AI
  • 4.4        AI-Enhanced Port Scanning Techniques
  • 4.5        Machine Learning for Network Mapping
  • 4.6        AI-Driven Social Engineering Reconnaissance
  • 4.7        Machine Learning in OSINT
  • 4.8        AI-Enhanced DNS Enumeration and AI-Driven Target Profiling
Lesson 5: AI in Vulnerability Assessment and Penetration Testing
  • 5.1        Automated Vulnerability Scanning with AI
  • 5.2        AI-Enhanced Penetration Testing Tools
  • 5.3        Machine Learning for Exploitation Techniques
  • 5.4        Dynamic Application Security Testing (DAST) with AI
  • 5.5        AI-Driven Fuzz Testing
  • 5.6        Adversarial Machine Learning in Penetration Testing
  • 5.7        Automated Report Generation using AI
  • 5.8        AI-Based Threat Modeling
  • 5.9        Challenges and Ethical Considerations in AI-Driven Penetration Testing
Lesson 6: Machine Learning for Threat Analysis
  • 6.1        Supervised Learning for Threat Detection
  • 6.1        Unsupervised Learning for Anomaly Detection
  • 6.3        Reinforcement Learning for Adaptive Security Measures
  • 6.4        Natural Language Processing (NLP) for Threat Intelligence
  • 6.5        Behavioral Analysis using Machine Learning
  • 6.6        Ensemble Learning for Improved Threat Prediction
  • 6.7        Feature Engineering in Threat Analysis
  • 6.8        Machine Learning in Endpoint Security
  • 6.9        Explainable AI in Threat Analysis
Lesson 7: Behavioral Analysis and Anomaly Detection for System Hacking
  • 7.1        Behavioral Biometrics for User Authentication
  • 7.2        Machine Learning Models for User Behavior Analysis
  • 7.3        Network Traffic Behavioral Analysis
  • 7.4        Endpoint Behavioral Monitoring
  •  7.5       Time Series Analysis for Anomaly Detection
  • 7.6        Heuristic Approaches to Anomaly Detection
  • 7.7        AI-Driven Threat Hunting
  • 7.8        User and Entity Behavior Analytics (UEBA)
  • 7.9        Challenges and Considerations in Behavioral Analysis
Lesson 8: AI Enabled Incident Response Systems
  • 8.1        Automated Threat Triage using AI
  • 8.2        Machine Learning for Threat Classification
  • 8.3        Real-time Threat Intelligence Integration
  • 8.4        Predictive Analytics in Incident Response
  • 8.5        AI-Driven Incident Forensics
  • 8.6        Automated Containment and Eradication Strategies
  • 8.7        Behavioral Analysis in Incident Response
  • 8.8        Continuous Improvement through Machine Learning Feedback
  • 8.9        Human-AI Collaboration in Incident Handling
Lesson 9: AI for Identity and Access Management (IAM)
  • 9.1        AI-Driven User Authentication Techniques
  • 9.2        Behavioral Biometrics for Access Control
  • 9.3        AI-Based Anomaly Detection in IAM
  • 9.4        Dynamic Access Policies with Machine Learning
  • 9.5        AI-Enhanced Privileged Access Management (PAM)
  • 9.6        Continuous Authentication using Machine Learning
  • 9.7        Automated User Provisioning and De-provisioning
  • 9.8        Risk-Based Authentication with AI
  • 9.9        AI in Identity Governance and Administration (IGA)
Lesson 10: Securing AI Systems
  • 10.1     Adversarial Attacks on AI Models
  • 10.2     Secure Model Training Practices
  • 10.3     Data Privacy in AI Systems
  • 10.4     Secure Deployment of AI Applications
  • 10.5     AI Model Explainability and Interpretability
  • 10.6     Robustness and Resilience in AI
  • 10.7     Secure Transfer and Sharing of AI Models
  • 10.8     Continuous Monitoring and Threat Detection for AI
Lesson 11: Ethics in AI and Cybersecurity
  • 11.1     Ethical Decision-Making in Cybersecurity
  • 11.2     Bias and Fairness in AI Algorithms
  • 11.3     Transparency and Explainability in AI Systems
  • 11.4     Privacy Concerns in AI-Driven Cybersecurity
  • 11.5     Accountability and Responsibility in AI Security
  • 11.6     Ethics of Threat Intelligence Sharing
  • 11.7     Human Rights and AI in Cybersecurity
  • 11.8     Regulatory Compliance and Ethical Standards
  • 11.9     Ethical Hacking and Responsible Disclosure
Lesson 12: Capstone Project
  • 12.1     Case Study 1: AI-Enhanced Threat Detection and Response
  • 12.2     Case Study 2: Ethical Hacking with AI Integration
  • 12.3     Case Study 3: AI in Identity and Access Management (IAM)
  • 12.4     Case Study 4: Secure Deployment of AI Systems

All necessary course materials are included.


System Requirements:

Internet Connectivity Requirements:

  • Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.

NOTE: While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system.  This is due to the potential high download and upload latency of cellular connections.   Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.

Hardware Requirements:

  • CPU: 1 GHz or higher
  • RAM: 4 GB or higher
  • Resolution: 1280 x 720 or higher.  1920x1080 resolution is recommended for the best experience.
  • Speakers / Headphones
  • Microphone for Webinar or Live Online sessions.

Operating System Requirements:

  • Windows 7 or higher.
  • Mac OSX 10 or higher.
  • Latest Chrome OS
  • Latest Linux Distributions

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

Web Browser Requirements:

  • Latest Google Chrome is recommended for the best experience.
  • Latest Mozilla FireFox
  • Latest Microsoft Edge
  • Latest Apple Safari

Basic Software Requirements (These are recommendations of software to use):

  • Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
  • PDF reader program (Adobe Reader, FoxIt)
  • Courses may require other software that is described in the above course outline.


** The course outlines displayed on this website are subject to change at any time without prior notice. **