AI+ Ethical Hacker
The AI+ Ethical Hacker course offers a comprehensive look at how Artificial Intelligence is revolutionizing ethical hacking practices. It covers the integration of AI into traditional hacking methodologies, including enhanced threat detection, automated vulnerability assessments, and advanced reconnaissance techniques. This category explores the fundamentals of AI technologies such as machine learning and deep learning and their applications in cybersecurity. It also highlights AI-driven tools for penetration testing, network security, and threat intelligence, showcasing how these innovations are reshaping the landscape of ethical hacking to create more sophisticated and effective security solutions.
All students receive:
- One-Year Subscription (with all updates)
- High-Quality E-Book
- Al Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Exam Study Guide
- Proctored Exam with one Free Retake
Check out our full list of training locations and learning formats. Please note that the location you choose may be an Established HD-ILT location with a virtual live instructor.
Train face-to-face with the live instructor.
Access to on-demand training content anytime, anywhere.
Attend the live class from the comfort of your home or office.
Interact with a live, remote instructor from a specialized, HD-equipped classroom near you. An SLI sales rep will confirm location availability prior to registration confirmation.
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Prerequisites:
Required:
- Knowledge of Python, Java, C++,etc for automation and scripting.
- Understanding of networking protocols, subnetting, firewalls, and routing.
- Familiarity with fundamental cybersecurity concepts, including encryption, authentication, access controls, and security protocols.
- Proficiency in using Windows and Linux operating systems.
- Understanding of machine learning concepts, algorithms, and basic implementation.
- Understanding of web technologies, including HTTP/HTTPS protocols, and web servers.
Recommended:
AI+ Executive or AI+ Everyone
Target Audience:
- Security Analyst
- IT Professional
Course Objectives:
- Establish foundational knowledge in Ethical Hacking, including methodology and legal aspects, as well as understanding hacker types, motivations, and information gathering techniques.
- Introduce AI's role in Ethical Hacking, covering fundamentals, technologies, and applications such as Machine Learning and Natural Language Processing.
- Explore AI tools and technologies for threat detection, penetration testing, and behavioral analysis in Ethical Hacking scenarios.
- Delve into AI-driven reconnaissance techniques, vulnerability assessment, and penetration testing, including automated scanning and fuzz testing.
- Examine the intersection of Machine Learning with threat analysis, behavioral analysis, incident response, identity management, system security, and ethical considerations in AI and Cybersecurity.
Course Outline:
Foundation of Ethical Hacking Using Artificial Intelligence (AI)
- Introduction to Ethical Hacking
- Ethical Hacking Methodology
- Legal and Regulatory Framework
- Hacker Types and Motivations
- Information Gathering Techniques
- Footprinting and Reconnaissance
- Scanning Networks
- Enumeration Techniques
Introduction to AI in Ethical Hacking
- AI in Ethical Hacking
- Fundamentals of AI
- AI Technologies Overview
- Machine Learning in Cybersecurity
- Natural Language Processing (NLP) for Cybersecurity
- Deep Learning for Threat Detection
- Adversarial Machine Learning in Cybersecurity
- AI-Driven Threat Intelligence Platforms
- Cybersecurity Automation with AI
AI Tools and Technologies in Ethical Hacking
- AI-Based Threat Detection Tools
- Machine Learning Frameworks for Ethical Hacking
- AI-Enhanced Penetration Testing Tools
- Behavioral Analysis Tools for Anomaly Detection
- AI-Driven Network Security Solutions
- Automated Vulnerability Scanners
- AI in Web Application
- AI for Malware Detection and Analysis
- Cognitive Security Tools
AI-Driven Reconnaissance Techniques
- Introduction to Reconnaissance in Ethical Hacking
- Traditional vs. AI-Driven Reconnaissance
- Automated OS Fingerprinting with AI
- AI-Enhanced Port Scanning Techniques
- Machine Learning for Network Mapping
- AI-Driven Social Engineering Reconnaissance
- Machine Learning in OSINT
- AI-Enhanced DNS Enumeration & AI-Driven Target Profiling
AI in Vulnerability Assessment and Penetration Testing
- Automated Vulnerability Scanning with AI
- AI-Enhanced Penetration Testing Tools
- Machine Learning for Exploitation Techniques
- Dynamic Application Security Testing (DAST) with AI
- AI-Driven Fuzz Testing
- Adversarial Machine Learning in Penetration Testing
- Automated Report Generation using AI
- AI-Based Threat Modeling
- Challenges and Ethical Considerations in AI-Driven Penetration Testing