Protecting data is key in the face of constant threats. You could be the guardian of that valuable information” 

Every second, thousands of pieces of data are generated, shared and stored in the digital environment. From making online payments and accessing educational services to coordinating business activities or protecting digital identities, technology has become an essential pillar that continually transforms the way we live and work. These interactions generate and transfer massive amounts of data at every instant, from personal information to sensitive files related to companies and institutions. This constant flow of data highlights the need for proper handling to ensure its security and privacy. 

Managing and protecting this data is no simple task, as it requires the combination of highly specialized expertise in areas such as cybersecurity and information management. These disciplines, although distinct, must be integrated to address the complex challenges of today's digital environment. In this context, the Advanced master’s degree in Secure Information Management represents a unique opportunity for engineers and IT professionals interested in acquiring a comprehensive vision that will enable them to master both areas and position themselves as leaders in a constantly growing sector. 

Many companies and institutions face the need to protect critical and highly sensitive data, but lack experts who can ensure effective management, preservation and surveillance of their digital information. To respond to this demand, TECH has designed a program that combines the best content with a teaching team of recognized professional experience. This approach ensures that students acquire the tools and knowledge necessary to stand out in the job market and access strategic positions in organizations seeking to strengthen their information security. 

Acquire the skills needed to secure and effectively manage data in a competitive digital environment”  

This Advanced master’s degree in  Secure Information Management contains the most complete and up-to-date educational program on the market. The most important features include:

• The development of practical cases presented by experts in Secure Information Management 
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice 
• Practical exercises where self-assessment can be used to improve learning 
• Special emphasis on innovative methodologies in Secure Information Management 
• 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 

Consolidate your theoretical knowledge with the numerous practical resources included in this Advanced master’s degree in Secure Information Management” 

The teaching staff includes professionals belonging to the field of Finance, who bring to this program the experience of their work, as well as recognized specialists from leading companies 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 an immersive learning experience designed to prepare for real-life situations. 

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

Discover the most innovative educational methodology designed by TECH to guarantee immersive and contextualized learning"

Access a 100% online program that allows you to study at your own pace, at any time and from anywhere in the world"

The teaching materials that make up this Advanced master’s degree in Secure Information Management have been developed by a team of experts in cybersecurity and data management. Therefore, the curriculum delves into the main digital threats and the most advanced methodologies for the protection and management of information. This will enable graduates to identify specific risks and develop effective solutions to ensure data security in various professional environments. The syllabus also addresses the most innovative tools in the sector, promoting strategies aimed at protecting the digital assets of organizations.

You will contribute to the protection of sensitive data and the creation of secure systems that guarantee the operational continuity of companies and institutions” 

Module 1. Data Analysis in a Business Organization

1.1. Business Analysis

1.1.1. Business Analysis
1.1.2. Data Structure
1.1.3. Phases and Elements 

1.2. Data Analysis in the Business

1.2.1. Departmental Scorecards and KPIs 
1.2.2. Operational, Tactical and Strategic Reports
1.2.3. Data Analytics Applied to Each Department

1.2.3.1. Marketing and Communication
1.2.3.2. Commercial
1.2.3.3. Customer Service
1.2.3.4. Purchasing 
1.2.3.5. Administration
1.2.3.6. HR
1.2.3.7. Production
1.2.3.8. IT

1.3. Marketing and Communication

1.3.1. KPIs for Measurement, Applications and Benefits
1.3.2. Marketing Systems and Data Warehouse
1.3.3. Implementation of a Data Analytics Framework in Marketing
1.3.4. Marketing and Communication Plan 
1.3.5. Strategies, Prediction and Campaign Management

1.4. Commerce and Sales 

1.4.1. Contributions of Data Analytics in the Commercial Area 
1.4.2. Sales Department Needs
1.4.3. Market Research 

1.5. Customer Service 

1.5.1. Loyalty 
1.5.2. Personal Coaching and Emotional Intelligence 
1.5.3. Customer Satisfaction

1.6. Purchasing 

1.6.1. Data Analysis for Market Research
1.6.2. Data Analysis for Competency Research
1.6.3. Other Applications

1.7. Administration 

1.7.1. Needs of the Administration Department
1.7.2. Data Warehouse and Financial Risk Analysis 
1.7.3. Data Warehouse and Credit Risk Analysis

1.8. Human Resources

1.8.1. HR and the Benefits of Data Analysis
1.8.2. Data Analytics Tools for the HR Department
1.8.3. Data Analytics Applications for the HR Department

1.9. Production

1.9.1. Data Analysis in a Production Department
1.9.2. Applications
1.9.3. Benefits 

1.10. IT 

1.10.1. IT Department
1.10.2. Data Analysis and Digital Transformation 
1.10.3. Innovation and Productivity

Module 2. Data Management, Data Manipulation and Information Management for Data Science 

2.1. Statistics. Variables, Indices and Ratios 

2.1.1. Statistics 
2.1.2. Statistical Dimensions 
2.1.3. Variables, Indices and Ratios 

2.2. Type of Data 

2.2.1. Qualitative 
2.2.2. Quantitative 
2.2.3. Characterization and Categories 

2.3. Data Knowledge from the Measurements 

2.3.1. Centralization Measurements 
2.3.2. Measures of Dispersion
2.3.3. Correlation 

2.4. Data Knowledge from the Graphs

2.4.1. Visualization According to Type of Data 
2.4.2. Interpretation of Graphic Information 
2.4.3. Customization of Graphics with  

2.5. Probability 

2.5.1. Probability 
2.5.2. Function of Probability 
2.5.3. Distributions 

2.6. Data Collection 

2.6.1. Methodology of Data Collection 
2.6.2. Data Collection Tools 
2.6.3. Data Collection Channels 

2.7. Data Cleaning 

2.7.1. Phases of Data Cleansing 
2.7.2. Data Quality
2.7.3. Data Manipulation (with R) 

2.8. Data Analysis, Interpretation and Evaluation of Results

2.8.1. Statistical Measures 
2.8.2. Relationship Indexes 
2.8.3. Data Mining 

2.9. Datawarehouse 

2.9.1. Components  
2.9.2. Design 

2.10. Data Availability 

2.10.1. Access 
2.10.2. Uses 
2.10.3. Security

Module 3. Devices and IoT Platforms as a Base for Data Science

3.1. Internet of Things

3.1.1. Internet of the Future, Internet of Things
3.1.2. The Industrial Internet Consortium

3.2. Architecture of Reference 

3.2.1. The Architecture of Reference
3.2.2. Layers
3.2.3. Components

3.3. Sensors and IoT Devices 

3.3.1. Principal Components
3.3.2. Sensors and Actuators

3.4. Communications and Protocols

3.4.1. Protocols. OSI Model
3.4.2. Communication Technologies

3.5. Cloud Platforms for LoT and LIoT 

3.5.1. General Purpose Platforms 
3.5.2. Industrial Platforms
3.5.3. Open Code Platforms

3.6. Data Management on IoT Platforms

3.6.1. Data Management Mechanisms. Open Data
3.6.2. Data Exchange and Visualization

3.7. IoT Security

3.7.1. Requirements and Security Areas
3.7.2. Security Strategies in IIoT

3.8. Applications of IoT 

3.8.1. Intelligent Cities
3.8.2. Health and Fitness
3.8.3. Smart Home
3.8.4. Other Applications

3.9. Applications of IIoT

3.9.1. Fabrication
3.9.2. Transport
3.9.3. Energy
3.9.4. Agriculture and Livestock
3.9.5. Other Sectors

3.10. Industry 4.0. 

3.10.1. IoRT (Internet of Robotics Things)
3.10.2. 3D Additive Manufacturing
3.10.3. Big Data Analytics

Module 4. Graphical Representation of Data Analysis

4.1. Exploratory Analysis 

4.1.1. Representation for Information Analysis
4.1.2. The Value of Graphical Representation
4.1.3. New Paradigms of Graphical Representation

4.2. Optimization for Data Science 

4.2.1. Color Range and Design
4.2.2. Gestalt in Graphic Representation
4.2.3. Errors to Avoid and Advice 

4.3. Basic Data Sources

4.3.1. For Quality Representation
4.3.2. For Quantity Representation
4.3.3. For Time Representation

4.4. Complex Data Sources

4.4.1. Files, Lists and Databases 
4.4.2. Open Data
4.4.3. Continuous Data Generation

4.5. Types of Graphs 

4.5.1. Basic Representations
4.5.2. Block Representation 
4.5.3. Representation for Dispersion Analysis
4.5.4. Circular Representations
4.5.5. Bubble Representations
4.5.6. Geographical Representations 

4.6. Types of Visualization

4.6.1. Comparative and Relational
4.6.2. Distribution
4.6.3. Hierarchical

4.7. Report Design with Graphic Representation 

4.7.1. Application of Graphs in Marketing Reports
4.7.2. Application of Graphs in Scorecards and KPIs
4.7.3. Application of Graphs in Strategic Plans
4.7.4. Other Uses: Science, Health, Business 

4.8. Graphic Narration

4.8.1. Graphic Narration
4.8.2. Evolution 
4.8.3. Uses

4.9. Tools Oriented Towards Visualization 

4.9.1. Advanced Tools
4.9.2. Online Software
4.9.3. Open Source

4.10. New Technologies in Data Visualization 

4.10.1. Systems for Virtualization of Reality
4.10.2. Reality Enhancement and Improvement Systems
4.10.3. Intelligent Systems

Module 5. Data Science Tools

5.1. Data Science

5.1.1. Data Science
5.1.2. Advanced Tools for Data Scientists 

5.2. Data, Information and Knowledge

5.2.1. Data, Information and Knowledge 
5.2.2. Types of Data
5.2.3. Data Sources

5.3. From Data to Information 

5.3.1. Data Analysis
5.3.2. Types of Analysis
5.3.3. Extraction of Information from a Dataset 

5.4. Extraction of Information Through Visualization

5.4.1. Visualization as an Analysis Tool
5.4.2. Visualization Methods 
5.4.3. Visualization of a Data Set

5.5. Data Quality

5.5.1. Quality Data
5.5.2. Data Cleaning 
5.5.3. Basic Data Pre-Processing

5.6. Dataset

5.6.1. Dataset Enrichment
5.6.2. The Curse of Dimensionality
5.6.3. Modification of Our Data Set

5.7. Unbalance 

5.7.1. Classes of Unbalance
5.7.2. Unbalance Mitigation Techniques
5.7.3. Balancing a Dataset

5.8. Unsupervised Models 

5.8.1. Unsupervised Model
5.8.2. Methods
5.8.3. Classification with Unsupervised Models

5.9. Supervised Models

5.9.1. Supervised Model
5.9.2. Methods
5.9.3. Classification with Supervised Models

5.10. Tools and Good Practices

5.10.1. Good Practices for Data Scientists
5.10.2. The Best Model 
5.10.3. Useful Tools

Module 6. Data Mining: Selection, Preprocessing and Transformation

6.1. Statistical Inference

6.1.1. Descriptive Statistics vs. Statistical Inference
6.1.2. Parametric Procedures
6.1.3. Non-Parametric Procedures

6.2. Exploratory Analysis

6.2.1. Descriptive Analysis 
6.2.2. Visualization
6.2.3. Data Preparation

6.3. Data Preparation

6.3.1. Integration and Data Cleaning 
6.3.2. Normalization of Data
6.3.3. Transforming Attributes 

6.4. Missing Values

6.4.1. Treatment of Missing Values
6.4.2. Maximum Likelihood Imputation Methods
6.4.3. Missing Value Imputation Using Machine Learning

6.5. Noise in the Data 

6.5.1. Noise Classes and Attributes
6.5.2. Noise Filtering 
6.5.3. The Effect of Noise

6.6. The Curse of Dimensionality

6.6.1. Oversampling
6.6.2. Undersampling
6.6.3. Multidimensional Data Reduction

6.7. From Continuous to Discrete Attributes

6.7.1. Continuous Data vs. Discreet Data
6.7.2. Discretization Process

6.8. The Data 

6.8.1. Data Selection 
6.8.2. Prospects and Selection Criteria
6.8.3. Selection Methods 

6.9. Instance Selection

6.9.1. Methods for Instance Selection
6.9.2. Prototype Selection
6.9.3. Advanced Methods for Instance Selection

6.10. Data Pre-Processing in Big Data Environments

6.10.1. Big Data
6.10.2. Classical Versus Massive Pre-processing
6.10.3. Smart Data 

Module 7. Predictability and Analysis of Stochastic Phenomena

7.1. Time Series

7.1.1. Time Series
7.1.2. Utility and Applicability
7.1.3. Related Case Studies

7.2. Time Series

7.2.1. Trend Seasonality of TS
7.2.2. Typical Variations
7.2.3. Waste Analysis

7.3. Typology

7.3.1. Stationary
7.3.2. Non-Stationary
7.3.3. Transformations and Settings

7.4. Time Series Schemes 

7.4.1. Additive Scheme (Model)
7.4.2. Multiplicative Scheme (Model)
7.4.3. Procedures to Determine the Type of Model

7.5. Basic Forecasting Methods

7.5.1. Media
7.5.2. Naïve
7.5.3. Seasonal Naivety
7.5.4. Method Comparison

7.6. Waste Analysis 

7.6.1. Autocorrelation
7.6.2. ACF of Waste
7.6.3. Correlation Test

7.7. Regression in the Context of Time Series 

7.7.1. ANOVA
7.7.2. Fundamentals
7.7.3. Practical Applications 

7.8. Predictive Methods of Time Series

7.8.1. ARIMA
7.8.2. Exponential Smoothing 

7.9. Manipulation and Analysis of Time Series with R

7.9.1. Data Preparation
7.9.2. Identification of Patterns
7.9.3. Model Analysis
7.9.4. Prediction

7.10. Combined Graphical Analysis with R 

7.10.1. Normal Situations
7.10.2. Practical Application for the Resolution of Simple Problems 
7.10.3. Practical Application for the Resolution of Advanced Problems 

Module 8. Design and Development of Intelligent Systems  

8.1. Data Pre-Processing 

8.1.1. Data Pre-Processing
8.1.2. Data Transformation 
8.1.3. Data Mining

8.2. Machine Learning

8.2.1. Supervised and Unsupervised Learning
8.2.2. Reinforcement Learning
8.2.3. Other Learning Paradigms

8.3. Classification Algorithms

8.3.1. Inductive Machine Learning
8.3.2. SVM and KNN
8.3.3. Metrics and Scores for Ranking

8.4. Regression Algorithms 

8.4.1. Lineal Regression, Logistical Regression and Non-Lineal Models 
8.4.2. Time Series 
8.4.3. Metrics and Scores for Regression 

8.5. Clustering Algorithms 

8.5.1. Hierarchical Clustering Techniques 
8.5.2. Partitional Clustering Techniques 
8.5.3. Metrics and Scores for Clustering 

8.6. Association Rules Techniques 

8.6.1. Methods for Rule Extraction 
8.6.2. Metrics and Scores for Association Rule Algorithms 

8.7. Advanced Classification Techniques. Multiclassifiers 

8.7.1. Bagging Algorithms 
8.7.2. Random Forests Sorter
8.7.3. Boosting for Decision Trees 

8.8. Probabilistic Graphical Models 

8.8.1. Probabilistic Models 
8.8.2. Bayesian Networks. Properties, Representation and Parameterization 
8.8.3. Other Probabilistic Graphical Models 

8.9. Neural Networks 

8.9.1. Machine Learning with Artificial Neural Networks 
8.9.2. Feedforward Networks 

8.10. Deep Learning 

8.10.1. Deep Feedforward Networks 
8.10.2. Convolutional Neural Networks and Sequence Models 
8.10.3. Tools for Implementing Deep Neural Networks

Module 9. Architecture and Systems for Intensive Use of Data

9.1. Non-Functional Requirements. Pillars of Big Data Applications 

9.1.1. Reliability 
9.1.2. Adaptation 
9.1.3. Maintainability 

9.2. Data Models 

9.2.1. Relational Model 
9.2.2. Document Model 
9.2.3. Graph Type Data Model 

9.3. Databases. Storage Management and Data Recovery 

9.3.1. H Indexes 
9.3.2. Structured Log Storage 
9.3.3. B Trees 

9.4. Data Coding Formats 

9.4.1. Language-Specific Formats 
9.4.2. Standardized Formats 
9.4.3. Binary Coding Formats 
9.4.4. Data Stream Between Processes 

9.5. Replication 

9.5.1. Objectives of Replication 
9.5.2. Replication Models 
9.5.3. Problems with Replication 

9.6. Distributed Transactions 

9.6.1. Transaction 
9.6.2. Protocols for Distributed Transactions 
9.6.3. Serializable Transactions 

9.7. Partitions 

9.7.1. Forms of Partitioning 
9.7.2. Secondary Index Interaction and Partitioning 
9.7.3. Partition Rebalancing 

9.8. Offline Data Processing 

9.8.1. Batch Processing 
9.8.2. Distributed File Systems 
9.8.3. MapReduce 

9.9. Data Processing in Real Time 

9.9.1. Types of Message Brokers 
9.9.2. Representation of Databases as Data Streams 
9.9.3. Data Stream Processing 

9.10. Practical Applications in Business 

9.10.1. Consistency in Readings 
9.10.2. Holistic Focus of Data 
9.10.3. Scaling of a Distributed Service

Module 10. Practical Application of Data Science in Business Sectors 

10.1. Health Sector 

10.1.1. Implications of AI and Data Analysis in the Health Sector 
10.1.2. Opportunities and Challenges 

10.2. Risks and Trends in the Health Sector 

10.2.1. Use in the Health Sector 
10.2.2. Potential Risks Related to the Use of AI

10.3. Financial Services 

10.3.1. Implications of AI and Data Analysis in Financial Services Sector 
10.3.2. Use in the Financial Services 
10.3.3. Potential Risks Related to the Use of AI 

10.4. Retail 

10.4.1. Implications of AI and Data Analysis in the Retail Sector 
10.4.2. Use in Retail 
10.4.3. Potential Risks Related to the Use of AI 

10.5. Industry 4.0 

10.5.1. Implications of AI and Data Analysis in Industry 4.0 
10.5.2. Use in Industry 4.0  

10.6. Risks and Trends in Industry 4.0 

10.6.1. Potential Risks Related to the Use of AI 

10.7. Public Administration 

10.7.1. Implications of AI and Data Analysis in Public Administration 
10.7.2. Use in Public Administration 
10.7.3. Potential Risks Related to the Use of AI 

10.8. Educational 

10.8.1. Implications of AI and Data Analysis in Education 
10.8.2. Potential Risks Related to the Use of AI 

10.9. Forestry and Agriculture 

10.9.1. Implications of AI and Data Analysis in Forestry and Agriculture 
10.9.2. Use in Forestry and Agriculture 
10.9.3. Potential Risks Related to the Use of AI 

10.10. Human Resources 

10.10.1. Implications of AI and Data Analysis in Human Resources 
10.10.2. Practical Applications in the Business World
10.10.3. Potential Risks Related to the Use of AI

Module 11. Cyberintelligence and Cybersecurity

11.1. Cyberintelligence

11.1.1. Cyberintelligence

11.1.1.1. Intelligence

11.1.1.1.1. Intelligence Cycle

11.1.1.2. Cyberintelligence
11.1.1.3. Cyberintelligence and Cybersecurity

11.1.2. Intelligence Analyst

11.1.2.1. The Role of the Intelligence Analyst
11.1.2.2. The Intelligence Analyst's Biases in Evaluative Activity

11.2. Cybersecurity

11.2.1. Layers of Security
11.2.2. Identification of Cyber Threats

11.2.2.1. External Threats
11.2.2.2. Internal Threats

11.2.3. Adverse Actions

11.2.3.1. Social Engineering
11.2.3.2. Commonly Used Methods

11.3. Techniques and Tools of Intelligences

11.3.1. OSINT
11.3.2. SOCMINT
11.3.3. HUMIT
11.3.4. Linux Distributions and Tools
11.3.5. OWISAM
11.3.6. OWISAP
11.3.7. PTES
11.3.8. OSSTM

11.4. Evaluation Methodologies

11.4.1. Intelligence Analysis
11.4.2. Techniques for Organizing Acquired Information
11.4.3. Reliability and Credibility of Information Sources
11.4.4. Analysis Methodologies
11.4.5. Presentation of Intelligence Results

11.5. Audits and Documentation

11.5.1. IT Security Audit
11.5.2. Documentation and Permits for Auditing
11.5.3. Types of Audits
11.5.4. Deliverables

11.5.4.1. Technical Report
11.5.4.2. Executive Report

11.6. Anonymity in the Network

11.6.1. Use of Anonymity
11.6.2. Anonymity Techniques (Proxy, VPN)
11.6.3. TOR, Freenet and IP2 Networks

11.7. Threats and Types of Security

11.7.1. Types of Threats
11.7.2. Physical Security
11.7.3. Network Security
11.7.4. Logical Security
11.7.5. Web Application Security
11.7.6. Security on Mobile Devices

11.8. Regulations and Compliance

11.8.1. The GDPR
11.8.2. BORRAR
11.8.3. ISO 27000 Family
11.8.4. NIST Cybersecurity Framework
11.8.5. PIC
11.8.6. ISO 27032
11.8.7. Cloud Regulations
11.8.8. SOX
11.8.9. ICP

11.9. Risk Analysis and Metrics

11.9.1. Extent of Risk
11.9.2. The Assets
11.9.3. Threats
11.9.4. Vulnerabilities
11.9.5. Risk Evaluation
11.9.6. Risk Treatment

11.10. Important Cybersecurity Agencies

11.10.1. NIST
11.10.2. ENISA
11.10.3. BORRAR
11.10.4. OEA
11.10.5. UNASUR PROSUR

Module 12. Host Security

12.1. Backup Copies

12.1.1. Backup Strategies
12.1.2. Tools for Windows
12.1.3. Tools for Linux
12.1.4. Tools for MacOS

12.2. User Antivirus

12.2.1. Types of Antivirus
12.2.2. Antivirus for Windows
12.2.3. Antivirus for Linux
12.2.4. Antivirus for MacOS
12.2.5. Antivirus for Smartphones

12.3. Intrusion Detectors - HIDS

12.3.1. Intrusion Detection Methods
12.3.2. Sagan
12.3.3. Aide
12.3.4. Rkhunter

12.4. Local Firewall

12.4.1. Firewalls for Windows
12.4.2. Firewalls for Linux
12.4.3. Firewalls for MacOS

12.5. Password Managers

12.5.1. Password
12.5.2. LastPass
12.5.3. KeePass
12.5.4. StickyPassword
12.5.5. RoboForm

12.6. Detectors for Phishing

12.6.1. Manual Detection of Phishing
12.6.2. Antiphishing Tools

12.7. Spyware

12.7.1. Avoidance Mechanisms
12.7.2. Antispyware Tools

12.8. Trackers

12.8.1. Measures to Protect the System
12.8.2. Anti-Tracking Tools

12.9. EDR- End Point Detection and Response

12.9.1. EDR System Behavior
12.9.2. Differences between EDR and Antivirus
12.9.3. The Future of EDR Systems

12.10. Control Over Software Installation

12.10.1. Repositories and Software Stores
12.10.2. Lists of Permitted or Prohibited Software
12.10.3. Update Criteria
12.10.4. Software Installation Privileges

Module 13. Network Security (Perimeter)

13.1. Threat Detection and Prevention Systems

13.1.1. General Framework for Security Incidents
13.1.2. Current Defense Systems: Defense in Depth and SOC
13.1.3. Current Network Architectures
13.1.4. Types of Tools for Incident Detection and Prevention

13.1.4.1. Network-Based Systems
13.1.4.2. Host-Based Systems
13.1.4.3. Centralized Systems

13.1.5. Instance Hosts, Container and Serverless Communication and Detection

13.2. Firewall

13.2.1. Types of Firewalls
13.2.2. Attacks and Mitigation
13.2.3. Common Firewalls in Linux Kernel

13.2.3.1. UFW
13.2.3.2. Nftables and Iptables
13.2.3.3. Firewalls

13.2.4. Detection Systems Based on System Logs

13.2.4.1. TCP Wrappers
13.2.4.2. BlockHosts and DenyHosts
13.2.4.3. Fai2ban

13.3. Intrusion Detection and Prevention Systems (IDS/IPS)

13.3.1. Attacks on IDS/IPS
13.3.2. IDS/IPS Systems

13.3.2.1. Snort
13.3.2.2. Suricata

13.4. Next Generation Firewalls (NGFW)

13.4.1. Differences between NGFW and Traditional Firewall
13.4.2. Main Capabilities
13.4.3. Commercial Solutions
13.4.4. Firewalls for Cloud Services

13.4.4.1. Virtual Private Cloud (VPC) Architecture
13.4.4.2. Cloud ACLs
13.4.4.3. Security Group

13.5. Proxy

13.5.1. Types of Proxy
13.5.2. Use of Proxy Advantages and Disadvantages

13.6. Antivirus Engines

13.6.1. General Context of Malware and IOCs
13.6.2. Antivirus Engine Problems

13.7. Email Protection Systems

13.7.1. Antispam

13.7.1.1. Black and White Lists
13.7.1.2. Bayesian Filters

13.7.2. Mail Gateway (MGW)

13.8. SIEM

13.8.1. Components and Architecture
13.8.2. Correlation Rules and Use Cases
13.8.3. Current Challenges of SIEM Systems

13.9. SOAR

13.9.1. SOAR and SIEM: Enemies or Allies
13.9.2. The Future of SOAR Systems

13.10. Other Network-Based Systems

13.10.1. WAF
13.10.2. NAC
13.10.3. HoneyPots and HoneyNets
13.10.4. CASB

Module 14. Smartphone Security

14.1. The World of Mobile Devices

14.1.1. Types of Mobile Platforms
14.1.2. IOS Devices
14.1.3. Android Devices

14.2. Mobile Security Management

14.2.1. OWASP Mobile Security Project
14.2.1.1. Top 10 Vulnerabilities
14.2.2. Communications, Networks and Connection Modes

14.3. Mobile Devices in Business Environments

14.3.1. Risk
14.3.2. Security Policies
14.3.3. Device Monitoring
14.3.4. Mobile Device Management (MDM)

14.4. User Privacy and Data Security

14.4.1. Statements of Information
14.4.2. Data Protection and Confidentiality

14.4.2.1. Licenses
14.4.2.2. Encryption

14.4.3. Secure Data Storage

14.4.3.1. Secure Storage on iOS
14.4.3.2. Secure Storage on Android

14.4.4. Best Practices in Application Development

14.5. Vulnerabilities and Attack Vectors

14.5.1. Vulnerabilities
14.5.2. Attack Vectors

14.5.2.1. Malware
14.5.2.2. Data Exfiltration
14.5.2.3. Data Manipulation

14.6. Main Threats

14.6.1. Unforced User
14.6.2. Malware

14.6.2.1. Types of Malware

14.6.3. Social Engineering
14.6.4. Data Leakage
14.6.5. Information Theft
14.6.6. Unsecured Wi-Fi Networks
14.6.7. Outdated Software
14.6.8. Malicious Applications
14.6.9. Insecure Passwords
14.6.10 Weak or No Security Configuration
14.6.11. Physical Access
14.6.12. Loss or Theft of the Device
14.6.13. Identity Theft (Integrity)
14.6.14. Weak or Broken Cryptography
14.6.15. Denial of Service (DoS)

14.7. Main Attacks

14.7.1. Phishing Attacks
14.7.2. Attacks Related to Communication Modes
14.7.3. Smishing Attacks
14.7.4. Cryptojacking Attacks
14.7.5. Man in The Middle

14.8. Hacking

14.8.1. Rooting and Jailbreaking
14.8.2. Anatomy of a Mobile Attack

14.8.2.1. Threat Propagation
14.8.2.2. Malware Installation on the Device
14.8.2.3. Persistence
14.8.2.4. Payload Execution and Information Extraction

14.8.3. Hacking on iOS Devices: Mechanisms and Tools
14.8.4. Hacking Android Devices: Mechanisms and Tools

14.9. Penetration Testing

14.9.1.  iOS PenTesting
14.9.2. Android PenTesting
14.9.3. Tools

14.10. Safety and Security

14.10.1. Security Configuration

14.10.1.1. On iOS Devices
14.10.1.2. On Android Devices

14.10.2. Safety Measures
14.10.3. Protection Tools

Module 15. IoT Security

15.1. Devices

15.1.1. Types of Devices
15.1.2. Standardized Architectures

15.1.2.1. ONEM2M
15.1.2.2. IoTWF

15.1.3. Application Protocols
15.1.4. Connectivity Technologies

15.2. IoT Devices. Areas of Application

15.2.1. SmartHome
15.2.2. SmartCity
15.2.3. Transportation
15.2.4. Wearables
15.2.5. Health Sector
15.2.6. IioT

15.3. Communication Protocols

15.3.1. MQTT
15.3.2. LWM2M
15.3.3. OMA-DM
15.3.4. TR-069

15.4. SmartHome

15.4.1. Home Automation
15.4.2. Networks
15.4.3. Household Appliances
15.4.4. Surveillance and Security

15.5. SmartCity

15.5.1. Lighting
15.5.2. Meteorology
15.5.3. Security

15.6. Transportation

15.6.1. Localization
15.6.2. Making Payments and Obtaining Services
15.6.3. Connectivity

15.7. Wearables

15.7.1. Smart Clothing
15.7.2. Smart Jewelry
15.7.3. Smart Watches

15.8. Health Sector

15.8.1. Exercise/Heart Rate Monitoring
15.8.2. Monitoring of Patients and Elderly People
15.8.3. Implantable
15.8.4. Surgical Robots

15.9. Connectivity

15.9.1. Wi-Fi/Gateway
15.9.2. Bluetooth
15.9.3. Built-In Connectivity

15.10. Securitization

15.10.1. Dedicated Networks
15.10.2. Password Managers
15.10.3. Use of Encrypted Protocols
15.10.4. Tips for Use

Module 16. Ethical Hacking

16.1. Work Environment

16.1.1. Linux Distributions

16.1.1.1. Kali Linux - Offensive Security
16.1.1.2. Parrot OS
16.1.1.3. Ubuntu

16.1.2. Virtualization Systems

16.1.3. Sandbox
16.1.4. Deployment of Laboratories

16.2. Methods

16.2.1. OSSTM
16.2.2. OWASP
16.2.3. NIST
16.2.4. PTES
16.2.5. ISSAF

16.3. Footprinting

16.3.1. Open-Source Intelligence (OSINT)
16.3.2. Search for Data Breaches and Vulnerabilities
16.3.3. Use of Passive Tools

16.4. Network Scanning

16.4.1. Scanning Tools

16.4.1.1. Nmap
16.4.1.2. Hping3
16.4.1.3. Other Scanning Tools

16.4.2. Scanning Techniques
16.4.3. Firewall and IDS Avoidance Techniques
16.4.4. Banner Grabbing
16.4.5. Network Diagrams

16.5. Enumeration

16.5.1. SMTP Enumeration
16.5.2. DNS Enumeration
16.5.3. NetBIOS and Samba Enumeration
16.5.4. LDAP Enumeration
16.5.5. SNMP Enumeration
16.5.6. Other Enumeration Techniques

16.6. Vulnerability Analysis

16.6.1. Vulnerability Scanning Solutions

16.6.1.1. Qualys
16.6.1.2. Nessus
16.6.1.3. CFI LanGuard

16.6.2. Vulnerability Scoring Systems

16.6.2.1. CVSS
16.6.2.2. CVE
16.6.2.3. NVD

16.7. Attacks on Wireless Networks

16.7.1. Methodology of Hacking in Wireless Networks

16.7.1.1. Wi-Fi Discovery
16.7.1.2. Traffic Analysis
16.7.1.3. Aircrack Attacks

16.7.1.3.1. WEP Attacks
16.7.1.3.2. WPA/WPA2 Attacks

16.7.1.4. Evil Twin Attacks
16.7.1.5. Attacks on WPS
16.7.1.6. Jamming

16.7.2. Tools for Wireless Security

16.8. Hacking of Web Servers

16.8.1. Cross Site Scripting
16.8.2. CSRF
16.8.3. Session Hijacking
16.8.4. SQLinjection

16.9. Exploiting Vulnerabilities

16.9.1. Use of Known Exploits
16.9.2. Use of Metasploit
16.9.3. Use of Malware

16.9.3.1. Definition and Scope
16.9.3.2. Malware Generation
16.9.3.3. Bypass of Antivirus Solutions

16.10. Persistence

16.10.1. Rootkits Installation
16.10.2. Use of Ncat
16.10.3. Use of Programmed Tasks for Backdoors
16.10.4. User Creation
16.10.5. HIDS Detection

Module 17. Reverse Engineering

17.1. Compilers

17.1.1. Types of Codes
17.1.2. Phases of a Compiler
17.1.3. Table of Symbols
17.1.4. Error Manager
17.1.5. GCC Compiler

17.2. Types of Analysis in Compilers

17.2.1. Lexical Analysis

17.2.1.1. Terminology
17.2.1.2. Lexical Components
17.2.1.3. LEX Lexical Analyzer

17.2.2. Parsing

17.2.2.1. Context-Free Grammars
17.2.2.2. Types of Parsing

17.2.2.2.1. Top-Down Analysis
17.2.2.2.2. Bottom-Up Analysis

17.2.2.3. Syntactic Trees and Derivations
17.2.2.4. Types of Parsers

17.2.2.4.1. LR (Left To Right) Analyzers
17.2.2.4.2. LALR Analyzers

17.2.3. Semantic Analysis

17.2.3.1. Attribute Grammars
17.2.3.2. S-Attributed
17.2.3.3. L-Attributed

17.3. Data Structures in Assembler

17.3.1. Variables
17.3.2. Arrays
17.3.3. Pointers
17.3.4. Structures
17.3.5. Objects

17.4. Assembler Code Structures

17.4.1. Selection Structures

17.4.1.1. If, Else If, Else
17.4.1.2. Switch

17.4.2. Iteration Structures

17.4.2.1. For
17.4.2.2. While
17.4.2.3. Use of Break

17.4.3. Functions

17.5. X86 Architecture Hardware

17.5.1. x86 Processor Architecture
17.5.2. x86 Data Structures
17.5.3. x86 Code Structures

17.6. ARM Architecture Hardware

17.6.1. ARM Processor Architecture
17.6.2. ARM Data Structures
17.6.3. ARM Code Structures

17.7. Static Code Analysis

17.7.1. Disassemblers
17.7.2. IDA
17.7.3. Code Rebuilders

17.8. Dynamic Code Analysis

17.8.1. Behavioral Analysis

17.8.1.1. Communications
17.8.1.2. Monitoring

17.8.2. Linux Code Debuggers
17.8.3. Windows Code Debuggers

17.9. Sandbox

17.9.1. Sandbox Architecture
17.9.2. Sandbox Evasion
17.9.3. Detection Techniques
17.9.4. Avoidance Techniques
17.9.5. Countermeasures
17.9.6. Sandbox in Linux
17.9.7. Sandbox in Windows
17.9.8. Sandbox in MacOS
17.9.9. Sandbox in Android 

17.10. Malware Analysis

17.10.1. Malware Analysis Methods
17.10.2. Malware Obfuscation Techniques

17.10.2.1. Executable Obfuscation
17.10.2.2. Restriction of Execution Environments

17.10.3. Malware Analysis Tools

Module 18. Secure Development

18.1. Secure Development

18.1.1. Quality, Functionality and Safety
18.1.2. Confidentiality, Integrity and Availability
18.1.3. Software Development Life Cycle

18.2. Requirements Phase

18.2.1. Authentication Control
18.2.2. Role and Privilege Control
18.2.3. Risk-Oriented Requirements
18.2.4. Privilege Approval

18.3. Analysis and Design Phases

18.3.1. Component Access and System Administration
18.3.2. Audit Trails
18.3.3. Session Management
18.3.4. Historical Data
18.3.5. Proper Error Handling
18.3.6. Separation of Functions

18.4. Implementation and Coding Phase

18.4.1. Ensuring the Development Environment
18.4.2. Preparation of Technical Documentation
18.4.3. Secure Codification
18.4.4. Communications Security

18.5. Good Secure Coding Practices

18.5.1. Input Data Validation
18.5.2. Coding of Output Data
18.5.3. Programming Style
18.5.4. Change Log Management
18.5.5. Cryptographic Practices
18.5.6. Error and Log Management
18.5.7. File Management
18.5.8. Memory Management
18.5.9. Standardization and Reuse of Security Functions

18.6. Server Preparation and Hardening

18.6.1. Management of Users, Groups and Roles on the Server
18.6.2. Software Installation
18.6.3. Server Hardening
18.6.4. Robust Configuration of the Application Environment

18.7. Preparing Databases and Hardening

18.7.1. DB Engine Optimization
18.7.2. Create Your Own User for the Application
18.7.3. Assigning the Required Privileges to the User
18.7.4. Hardening of the Databases

18.8. Testing Phase

18.8.1. Quality Control in Security Controls
18.8.2. Phased Code Inspection
18.8.3. Checking Configuration Management
18.8.4. Black Box Testing

18.9. Preparing the Transition to Production

18.9.1. Perform Change Control
18.9.2. Carry out Production Changeover Procedure
18.9.3. Perform Rollback Procedure
18.9.4. Pre-Production Testing

18.10. Maintenance Phase

18.10.1. Risk-Based Assurance
18.10.2. White Box Security Maintenance Testing
18.10.3. Black Box Safety Maintenance Tests

Module 19. Forensic Analysis

19.1. Data Acquisition and Duplication

19.1.1. Volatile Data Acquisition

19.1.1.1. System Information
19.1.1.2. Network Information
19.1.1.3. Volatility Order

19.1.2. Static Data Acquisition

19.1.2.1. Creating a Duplicate Image
19.1.2.2. Preparation of a Chain of Custody Document

19.1.3. Methods for Validation of Acquired Data

19.1.3.1. Methods for Linux
19.1.3.2. Methods for Windows

19.2. Evaluation and Defeat of Antiforensic Techniques

19.2.1. Objectives of Antiforensic Techniques
19.2.2. Data Deletion

19.2.2.1. Deletion of Data and Files
19.2.2.2. File Recovery
19.2.2.3. Recovery of Deleted Partitions

19.2.3. Password Protection
19.2.4. Steganography
19.2.5. Secure Device Wiping
19.2.6. Encryption

19.3. Forensic Analysis of the Operating System

19.3.1. Windows Forensics
19.3.2. Linux Forensics
19.3.3. Mac Forensics

19.4. Network Forensic Analysis

19.4.1. Logs Analysis
19.4.2. Data Correlation
19.4.3. Network Research
19.4.4. Steps to Follow in Network Forensic Analysis

19.5. Web Forensics

19.5.1. Investigation of Web Attacks
19.5.2. Attack Detection
19.5.3. IP Address Location

19.6. Forensic Database Analysis

19.6.1. Forensic Analysis in MSSQL
19.6.2. MySQL Forensic Analysis
19.6.3. PostgreSQL Forensic Analysis
19.6.4. Forensic Analysis in MongoDB

19.7. Cloud Forensics

19.7.1. Types of Crimes in the Cloud

19.7.1.1. Cloud as a Subject
19.7.1.2. Cloud as an Object
19.7.1.3. Cloud as a Tool

19.7.2. Challenges of Cloud Forensics
19.7.3. Research on Cloud Storage Services
19.7.4. Cloud Forensic Analysis Tools

19.8. Investigation of Email Crimes

19.8.1. Mailing Systems

19.8.1.1. Mail Clients
19.8.1.2. Mail Server
19.8.1.3. SMTP Server
19.8.1.4. POP3 Server
19.8.1.5. IMAP4 Server

19.8.2. Mailing Crimes
19.8.3. Mail Message

19.8.3.1. Standard Headers
19.8.3.2. Extended Headers

19.8.4. Steps for the Investigation of These Crimes
19.8.5. E-Mail Forensic Tools

19.9. Mobile Forensic Analysis

19.9.1. Cellular Networks

19.9.1.1. Types of Networks
19.9.1.2. CDR Contents

19.9.2. Subscriber Identity Module (SIM)
19.9.3. Logical Acquisition
19.9.4. Physical Acquisition
19.9.5. File System Acquisition

19.10. Forensic Report Writing and Presentation

19.10.1. Important Features of a Forensic Report
19.10.2. Classification and Types of Reports
19.10.3. Guide to Writing a Report
19.10.4. Presentation of the Report

19.10.4.1. Prior Preparation for Testifying
19.10.4.2. Deposition
19.10.4.3. Dealing with the Media

Module 20. Current and Future Challenges in Information Security

20.1. Blockchain Technology

20.1.1. Scope of Application
20.1.2. Confidentiality Guarantee
20.1.3. Non-Repudiation Guarantee

20.2. Digital Money

20.2.1. Bitcoins
20.2.2. Cryptocurrencies
20.2.3. Cryptocurrency Mining
20.2.4. Pyramid Schemes
20.2.5. Other Potential Crimes and Problems

20.3. Deepfake

20.3.1. Media Impact
20.3.2. Dangers to Society
20.3.3. Detection Mechanisms

20.4. The Future of Artificial Intelligence

20.4.1. Artificial Intelligence and Cognitive Computing
20.4.2. Uses to Simplify Customer Service

20.5. Digital Privacy

20.5.1. Value of Data in the Network
20.5.2. Use of Data in the Network
20.5.3. Privacy and Digital Identity Management

20.6. Cyberconflicts, Cybercriminals and Cyberattacks

20.6.1. The Impact of Cybersecurity on International Conflicts
20.6.2. Consequences of Cyber-attacks on the General Population.
20.6.3. Types of Cybercriminals. Protective Measures

20.7. Telework

20.7.1. Remote Work Revolution during and post COVID-19
20.7.2. Access Bottlenecks
20.7.3. Variation of the Attacking Surface
20.7.4. Workers' Needs

20.8. Emerging Wireless Technologies

20.8.1. WPA3
20.8.2. 5G
20.8.3. Millimeter Waves
20.8.4. Trend in Get Smart instead of Get More

20.9. Future Addressing in Networks

20.9.1. Current Problems with IP Addressing
20.9.2. IPv6
20.9.3. IPv4+
20.9.4. Advantages of IPv4+ Over IPv4
20.9.5. Advantages of IPv6 Over IPv4

20.10. The Challenge of Raising Awareness of Early and Continuing Education in the Population

20.10.1. Current Government Strategies
20.10.2. Resistance of the Population to Learning
20.10.3. Training Plans to be Adopted by Companies

You will learn through real-world cases designed in simulated learning environments that reflect current challenges in data management and cybersecurity”