Protect Digital Landscapes: Harness AI-Enhanced Technologies

The AI+ Ethical Hacker certification delves into the intersection of cybersecurity and artificial intelligence, a pivotal juncture in our era of rapid technological progress. Tailored for budding ethical hackers and cybersecurity experts, it offers comprehensive insights into AI's transformative impact on digital offense and defense strategies. Unlike conventional ethical hacking courses, this program harnesses AI's power to enhance cybersecurity approaches.

Prerequisites

  • Programming Proficiency: Knowledge of Python, Java, C++,etc for automation and scripting.

 

  • Networking Fundamentals: Understanding of networking protocols, subnetting, firewalls, and routing.

 

  • Cybersecurity Basics: Familiarity with fundamental cybersecurity concepts, including encryption, authentication, access controls, and security protocols.

 

  • Operating Systems Knowledge: Proficiency in using Windows and Linux operating systems.

 

  • 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.

Exam Details

Modules

12

Examination

1

Minutes

90

Passing score

70%

Modules

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

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

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

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 & AI-Driven Target Profiling

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

6.1 Supervised Learning for Threat Detection

6.2 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

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

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

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)

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

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

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

What you will learn

AI-Integrated Cybersecurity Techniques

Learners will develop the ability to integrate AI tools and technologies into cybersecurity practices. This includes using AI for ethical hacking tasks such as reconnaissance, vulnerability assessments, penetration testing, and incident response.

Threat Analysis and Anomaly Detection

Students will gain skills in applying machine learning algorithms to detect unusual patterns and behaviors that indicate potential security threats. This skill is crucial for preemptively identifying and mitigating risks before.

AI for Identity and Access Management (IAM)

Learners will understand how to apply AI to enhance IAM systems, crucial for maintaining secure access to resources within an organization. This involves using AI to improve authentication processes and manage user permissions more dynamically and securely.

Automated Security Protocol Optimization

Students will be equipped to utilize AI to dynamically adjust and optimize security protocols based on real-time data analysis and threat assessment. Learners will explore how AI algorithms can predict and respond to potential security breaches by automatically tweaking firewall rules, security configurations, and other protective measures.

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