Thanks to this 100% online university program, you will acquire key skills in Security Data Analysis, using advanced algorithms to detect anomalous patterns and prevent cyber attacks”

Proactive Cybersecurity Defense focuses on identifying and mitigating vulnerabilities before they can be exploited, anticipating malicious actions. This is achieved through the use of advanced technologies such as Artificial Intelligence, which allows analyzing patterns, predicting behaviors and reinforcing protection measures. Moreover, Digital Forensic Analysis is concerned with investigating security incidents to identify their causes, perpetrators and consequences. In this context, tools based on intelligent systems have transformed the ability to collect, analyze and preserve digital evidence efficiently and accurately.

However, with the steady increase in targeted cyber-attacks such as ransomware and advanced phishing, the need for experts who can anticipate these threats and, in the event of an incident, conduct thorough investigations to minimize the impact and prevent future threats has become apparent. Likewise, the proliferation of connected devices and digital transformation have exponentially increased the attack surface, making specialized preparation in this field essential.

It is in this context that this TECH Postgraduate diploma arises, a comprehensive program that provides computer scientists with advanced skills in Cyber Defense and Digital Forensics, using tools based on Artificial Intelligence to protect digital environments. In this way, they will delve into the proactive identification and mitigation of vulnerabilities, master the techniques of collection and analysis of digital evidence, and be able to design predictive models that anticipate emerging threats.

In this sense, TECH has designed this 100% online university program that guarantees maximum flexibility to professionals, who will only need an electronic device with an Internet connection to access the contents. At the same time, they will be able to benefit from the Relearning methodology, an innovative learning system based on the strategic reiteration of key concepts, which facilitates a progressive and natural assimilation of knowledge, optimizing learning and enhancing results.

You will analyze practical cases of Cybersecurity guided by specialists with experience in the management of cybercrime and the use of automated response systems”

This Postgraduate diploma in Proactive Defense and Digital Forensics with Artificial Intelligence contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in cybersecurity and Digital Forensics, with extensive mastery of advanced Artificial Intelligence tools applied to proactive defense and incident investigation
• The graphic, schematic and eminently practical content of the book provides scientific and practical information on those disciplines that are essential for professional practice
• Practical exercises where the process of self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will delve into advanced Cyber Defense and Forensic Analysis techniques, using intelligent systems to anticipate threats and manage incidents effectively”

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned experts.

You will apply predictive models based on Neural Networks and Reinforcement Learning to design innovative protection strategies in digital environments”

You will access simulated environments that recreate real scenarios, allowing you to develop practical skills and prepare you to lead Cyber Defense projects”

Throughout the curriculum of this Postgraduate diploma, professionals will explore the fundamental concepts of Modern Cryptography and Forensic Analysis to the design of Predictive Models for the anticipation of cyber threats. In this way, through a practical approach, and the use of advanced Artificial Intelligence tools such as ChatGPT, this program prepares computer scientists to lead digital protection strategies in increasingly complex environments.

You will delve into the most innovative tools for cryptographic key management and detection of anomalous patterns in encrypted systems”

Module 1. Modern Cryptography with ChatGPT Support for Data Protection

1.1. Basic Principles of Cryptography with Artificial Intelligence Applications

1.1.1. Fundamental Concepts of Cryptography: Confidentiality and Authenticity
1.1.2. Main Cryptographic Algorithms and Their Current Relevance
1.1.3. Role of Artificial Intelligence in the Modernization of Cryptography

1.2. ChatGPT in the Teaching and Practice of Symmetric and Asymmetric Cryptography

1.2.1. Introduction to Symmetric and Asymmetric Cryptography
1.2.2. Comparison between Symmetric and Asymmetric Encryption
1.2.3. Use of ChatGPT in Learning Cryptographic Methods

1.3. Advanced Encryption (AES, RSA) and AI-Generated Recommendations

1.3.1. Fundamentals of AES and RSA Algorithms in Data Encryption
1.3.2. Strengths and Weaknesses of These Algorithms in the Current Context
1.3.3. Generation of Security Recommendations in Advanced Cryptography with Artificial Intelligence

1.4. Artificial Intelligence in Key Management and Authentication

1.4.1. Principles of Cryptographic Key Management
1.4.2. Importance of Secure Key Authentication
1.4.3. Application of Artificial Intelligence to Optimize Key Management and Authentication Processes

1.5. Hashing Algorithms and ChatGPT in Integrity Assessment

1.5.1. Basic Concepts and Applications of Hashing Algorithms
1.5.2. Hashing Functions in Data Integrity Verification
1.5.3. Data Integrity Analysis and Verification with the Help of ChatGPT

1.6. ChatGPT in the Detection of Anomalous Encryption Patterns

1.6.1. Introduction to Anomalous Pattern Detection in Cryptography
1.6.2. ChatGPT's Ability to Identify Irregularities in Cryptographic Data
1.6.3. Limitations of Language Models in Anomalous Cipher Detection

1.7. Introduction to Post-Quantum Cryptography with Artificial Intelligence Simulations

1.7.1. Fundamentals of Post-Quantum Cryptography and Its Importance
1.7.2. Main Post-Quantum Algorithms in Research
1.7.3. Use of Artificial Intelligence in Simulations for the Study of Post-Quantum Cryptography

1.8. Blockchain and ChatGPT in the Verification of Secure Transactions

1.8.1. Basic Concepts of Blockchain and Its Security Structure
1.8.2. Role of Cryptography in Blockchain Integrity
1.8.3. Application of ChatGPT to Explain and Analyze Secure Transactions

1.9. Privacy Protection and Federated Learning

1.9.1. Definition and Principles of Federated Learning
1.9.2. Importance of Privacy in Decentralized Learning
1.9.3. Benefits and Challenges of Federated Learning for Data Security

1.10. Development of a Generative Artificial Intelligence Based Encryption System

1.10.1. Basic Principles in the Creation of Encryption Systems
1.10.2. Advantages of Generative Artificial Intelligence in the Design of Encryption Systems
1.10.3. Components and Requirements of an AI-Assisted Encryption System

Module 2. Digital Forensics and Artificial Intelligence-Assisted Incident Response

2.1. ChatGPT Forensic Processes for the Identification of Evidence

2.1.1. Basic Concepts of Forensic Analysis in Digital Environments
2.1.2. Stages of Evidence Identification and Collection
2.1.3. Role of ChatGPT in the Support of Forensic Identification

2.2. Gemini and ChatGPT in Data Identification and Data Mining

2.2.1. Fundamentals of Data Extraction for Forensic Analysis
2.2.2. Relevant Data Identification Techniques
2.2.3. Contribution of Artificial Intelligence to the Automation of the Extraction Process

2.3. Log Analysis and Event Correlation with Artificial Intelligence

2.3.1. Importance of Logs in Incident Analysis
2.3.2. Event Correlation Techniques for Incident Reconstruction
2.3.3. Use of Artificial Intelligence to Identify Patterns in Log Correlation

2.4. Data Recovery and Restoration of Systems Using Artificial Intelligence

2.4.1. Data Recovery Principles and Their Importance in Digital Forensics
2.4.2. Restoration Techniques of Compromised Systems
2.4.3. Application of Artificial Intelligence to Improve Recovery and Restoration Processes

2.5. Machine Learning for Incident Detection and Reconstruction

2.5.1. Introduction to Machine Learning in Incident Detection
2.5.2. Incident Reconstruction Techniques with Artificial Intelligence Models
2.5.3. Ethical and Practical Considerations in Event Detection

2.6. Incident Reconstruction and Simulation with ChatGPT

2.6.1. Fundamentals of Incident Reconstruction in Forensic Analysis
2.6.2. ChatGPT's Ability to Create Incident Simulations
2.6.3. Limitations and Challenges in Complex Incident Simulation

2.7. Detection of Malicious Activity on Mobile Devices

2.7.1. Characteristics and Challenges in Forensic Analysis of Mobile Devices
2.7.2. Major Malicious Activities in Mobile Environments
2.7.3. Application of Artificial Intelligence to Identify Threats in Mobile Devices

2.8. Automated Incident Response with Artificial Intelligence Workflows

2.8.1. Principles of Incident Response in Cybersecurity
2.8.2. Importance of Automation in Rapid Incident Response
2.8.3. Benefits of Artificial Intelligence-Assisted Workflows in Mitigation

2.9. Ethics and Transparency in Forensic Analysis with Generative AI

2.9.1. Ethical Principles in the Use of Artificial Intelligence in Forensic Analysis
2.9.2. Transparency and Explainability of Generative Models in Forensics
2.9.3. Privacy and Accountability Considerations in Analysis

2.10. Forensic Analysis and Incident Recreation Lab with ChatGPT and Gemini

2.10.1. Structure and Objectives of a Forensic Analysis Laboratory
2.10.2. Benefits of Controlled Environments for Forensics Practice
2.10.3. Key Components for Setting Up a Simulation Laboratory

Module 3. Predictive Models for Proactive Defense in Cybersecurity Using ChatGPT

3.1. Predictive Analytics in Cybersecurity: Techniques and Applications with Artificial Intelligence

3.1.1. Basic Concepts of Predictive Analytics in Security
3.1.2. Predictive Techniques in the Field of Cybersecurity
3.1.3. Application of Artificial Intelligence in the Anticipation of Cyber Threats

3.2. Regression and Classification Models with ChatGPT Support

3.2.1. Principles of Regression and Classification in Threat Prediction
3.2.2. Types of Classification Models in Cybersecurity
3.2.3. ChatGPT Assistance in the Interpretation of Predictive Models

3.3. Identifying Emerging Threats with ChatGPT Predictions

3.3.1. Emerging Threat Detection Concepts
3.3.2. Techniques for Identifying New Attack Patterns
3.3.3. Limitations and Precautions in the Prediction of New Threats

3.4. Neural Networks for Anticipation of Cyberattacks

3.4.1. Fundamentals of Neural Networks Applied in Cybersecurity
3.4.2. Common Architectures for Detection and Prediction of Attacks
3.4.3. Challenges in Implementing Neural Networks in Cyber Defense

3.5. Use of ChatGPT for Threat Scenario Simulations

3.5.1. Basic Concepts of Threat Simulation in Cybersecurity
3.5.2. ChatGPT Capabilities for Developing Predictive Simulations
3.5.3. Factors to Consider in the Design of Simulated Scenarios

3.6. Reinforcement Learning Algorithms for Optimization of Defenses

3.6.1. Introduction to Reinforcement Learning in Cybersecurity
3.6.2. Reinforcement Algorithms Applied to Defense Strategies
3.6.3. Benefits and Challenges of Reinforcement Learning in Cybersecurity Environments

3.7. Threat Simulation and Response with ChatGPT

3.7.1. Threat Simulation Principles and Their Relevance in Cyber Defense
3.7.2. Automated and Optimized Responses to Simulated Attacks
3.7.3. Benefits of Simulation for Improving Cyber Preparedness

3.8. Accuracy and Effectiveness Assessment in Predictive Artificial Intelligence Models

3.8.1. Key Indicators for the Evaluation of Predictive Models
3.8.2. Accuracy Assessment Methodologies in Cybersecurity Models
3.8.3. Critical Factors in the Effectiveness of Artificial Intelligence Models in Cybersecurity

3.9. Artificial Intelligence in Incident Management and Automated Response

3.9.1. Fundamentals of Incident Management in Cybersecurity
3.9.2. Role of Artificial Intelligence in Real-Time Decision Making
3.9.3. Challenges and Opportunities in Response Automation

3.10. Creation of a Predictive Defense System with ChatGPT Support

3.10.1. Proactive Defense System Design Principles
3.10.2. Integration of Predictive Models in Cybersecurity Environments
3.10.3. Key Components for an AI-Based Predictive Defense System

You will implement modern encryption algorithms, including post-quantum solutions, to ensure data integrity and privacy in real-world scenarios”