Diploma in Anomaly and Attack Detection in Graphs
Sobre nuestro Diploma in Anomaly and Attack Detection in Graphs
The Diploma in Anomaly and Attack Detection in Graphs focuses on the analysis of complex data structured in graphs, applying machine learning and data mining techniques to identify anomalous patterns and detect cyberattacks. It explores the use of advanced algorithms for fraud detection, social media security, and cybersecurity in critical infrastructure. The program includes the analysis of social networks, transportation networks, and financial networks, with a focus on graph visualization and results interpretation. The diploma provides practical skills in using tools and frameworks such as Neo4j, NetworkX, and Python libraries for graph analysis, including graph machine learning and threat modeling. Students will acquire the ability to develop mitigation and response strategies for security incidents, preparing them for roles such as graph security analysts, graph data scientists, and cybersecurity consultants, strengthening the protection of critical data and systems.
Target keywords (natural in the text): graphs, anomaly detection, attacks, machine learning, cybersecurity, social media security, fraud detection, graph analysis.
Diploma in Anomaly and Attack Detection in Graphs
- Modalidad: Online
- Duración: 8 meses
- Horas: 900 H
- Idioma: ES / EN
- Créditos: 60 ECTS
- Fecha de matrÃcula: 19-06-2026
- Fecha de inicio: 30-07-2026
- Plazas disponibles: 3
1.580 $
Competencias y resultados
Qué aprenderás
1. Mastery of Anomaly and Attack Detection in Graphs
- Identify anomalous patterns and suspicious behavior in graph structures.
- Use graph-based anomaly detection techniques to identify fraud, security threats, and unusual behavior.
- Apply graph analysis algorithms for the detection of attacks on networks and systems.
- Understand the different categories of graph attacks and their impacts.
- Develop skills for incident mitigation and response based on graph analysis.
2. Unraveling Anomalies and Attacks in Graphs: A Comprehensive Diploma Course
Here is the requested content:
- Identify and classify anomalous patterns in complex graphs.
- Apply graph-based intrusion detection techniques for cybersecurity.
- Use graph analysis algorithms to uncover fraud and suspicious activity.
- Master specialized graph analysis tools and platforms.
- Develop graph-based predictive models to anticipate attacks and vulnerabilities.
- Implement risk mitigation and incident response strategies in graph environments.
- Understand the ethical and legal implications of graph analysis in security.
- Design and evaluate graph-based security solutions for different sectors.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. Master's Degree in Threat Identification and Prevention in Graph Networks
4. Master’s Degree in Threat Identification and Prevention in Graph Networks
- Understand the fundamentals of graph theory and its applications in cybersecurity.
- Identify and analyze anomalous behavior patterns in networks.
- Apply data mining and machine learning techniques for threat detection.
- Use specialized tools for the visualization and analysis of network graphs.
- Design and implement graph-based threat prevention strategies.
- Evaluate the effectiveness of graph-based security solutions.
- Manage and respond to security incidents using graph analysis.
- Develop skills in communicating findings and recommendations to technical and non-technical audiences.
5. In-depth Analysis of Anomalies and Attacks in Graphs: A Specialized Diploma
- Identify and classify anomalous patterns in complex graph structures.
- Apply advanced algorithms for detecting attacks in graph-based networks.
- Use graph visualization techniques for interpreting anomalous data.
- Develop predictive models for threat prevention in graphs.
- Evaluate the resilience of graph systems against sophisticated attacks.
- Implement incident mitigation and response strategies in graphs.
- Master specialized tools for graph analysis and cybersecurity.
- Understand the legal and ethical framework related to graph security.
- Apply knowledge to real-world case studies, including social and financial networks.
- Refine communication and presentation skills to report complex findings.
6. Postgraduate Diploma in the Detection, Analysis, and Mitigation of Graph Attacks
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
Para quien va dirigido nuestro:
Diploma in Anomaly and Attack Detection in Graphs
- Professionals and advanced students interested in maritime security and naval cybersecurity.
- Personnel from navies, coast guards, and maritime security organizations who wish to strengthen their cyber defense capabilities.
- Data analysts, data scientists, and IT professionals with experience in computer security who seek to specialize in threat detection in graph environments.
- Experts in threat intelligence, incident investigation, and cyber intelligence who wish to apply advanced techniques for anomaly and attack detection.
- Professionals in the naval industry, maritime logistics, and transportation.
Maritime professionals interested in protecting their systems and networks from cyberattacks.
Graduates in Computer Engineering, Computer Science, Telecommunications Engineering, Cybersecurity, or related fields.
Recommended requirements: Basic knowledge of networks, operating systems, computer security, and programming (preferably Python). Previous experience in data analysis and/or cybersecurity is a plus. Proficiency in Spanish (ES/EN) (advanced reading comprehension).
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Module 1 — Introduction to Graphs and Anomaly Analysis
1.1 Graph Fundamentals: Definition and Key Terminology
1.2 Types of Graphs: Structures and Properties
1.3 Graph Applications: Examples in Different Domains
1.4 Introduction to Anomaly Analysis: Concepts and Objectives
1.5 Graph Metrics and Statistics: Centrality, Density, etc.
1.6 Graph Visualization Techniques: Tools and Methods
1.7 Structure-Based Anomaly Detection: Unusual Patterns
1.8 Attribute-Based Anomaly Detection: Outliers
1.9 Introduction to Graph Analysis Tools
1.10 Case Study: Identifying Anomalies in a Graph Dataset
2.2 Introduction to Graph Theory and its Applications in Cybersecurity
2.2 Types of Graphs: Structure, Representation, and Properties
2.3 Key Concepts: Nodes, Edges, Degree, Centrality
2.4 Network Modeling: Creating Graphs to Represent Systems
2.5 Anomaly Detection: Introduction to Graph-Based Methods
2.6 Graph Attacks: Types of Attacks and Their Impacts
2.7 Anomaly Detection Algorithms: Clustering and Outlier Detection
2.8 Connectivity Analysis: Identifying Patterns and Vulnerabilities
2.9 Tools and Frameworks for Graph Analysis
2.20 Case Studies: Practical Applications of Threat Detection
3.3 Graph Structure-Based Detection Algorithms
3.2 Anomaly Detection Using Paths and Trajectories
3.3 Centrality Analysis and its Application in Detection
3.4 Clustering Techniques in Graphs for Anomaly Identification
3.5 Anomaly Detection in Temporal Graphs
3.6 Supervised Machine Learning Methods for Detection
3.7 Unsupervised and Semi-Supervised Machine Learning
3.8 Feature Integration and Analysis of Heterogeneous Networks
3.9 Evaluation and Comparison of Detection Methods
3.30 Case Studies: Application of Advanced Methods
3.4
4.4 Introduction to Graph Modeling and its Applications in Security
4.2 Fundamentals of Anomaly Detection in Graphs
4.3 Common Types of Attacks on Graph Networks
4.4 Structural Graph Analysis Techniques for Threat Detection
4.5 Advanced Algorithms for Anomaly Identification
4.6 Implementation of Attack Prevention and Mitigation Methods
4.7 Case Studies: Analysis of Attacks on Real-World Graphs
4.8 Artificial Intelligence and Machine Learning in Threat Detection in Graphs
4.9 Risk Assessment and Incident Response Strategies
4.40 Tools and Platforms for Graph Analysis in Security
4.5
5.5 Graph Fundamentals: Definitions and Essential Terminology
5.5 Types of Graphs: Directed, Undirected, Weighted, etc.
5.3 Graph Representation: Adjacency Matrices, Adjacency Lists
5.4 Data Structures for Graphs: Efficient Implementation
5.5 ​​Graph Traversal: BFS and DFS, Applications
5.6 Introduction to Graph Anomalies and Attacks
5.7 Importance of Anomaly and Attack Detection
5.8 Applications of Graphs in Network Security
5.9 Tools and Libraries for Graph Analysis
5.50 Introduction to the Graph Ecosystem and Its Potential in Security
6.6 Introduction to Graphs: Definitions and Key Concepts
6.2 Types of Graphs: Structures and Applications
6.3 Data Representation in Graphs: Nodes, Edges, and Attributes
6.4 Fundamental Graph Algorithms: Search and Traversal
6.5 Introduction to Threat Detection in Graphs: Basic Concepts
6.6 Anomaly Identification: Initial Methods and Techniques
6.7 Graph Visualization: Representation Tools and Techniques
6.8 Case Studies: Common Threats and Their Representation in Graphs
6.9 Tools and Libraries for Graph Analysis
6.60 Introduction to Network Security and the Importance of Anomaly Detection
7.7 Fundamentals of Graph Theory
7.2 Graph Representation and Data Structure
7.3 Types of Graphs and Their Properties
7.4 Concepts of Centrality and Importance in Graphs
7.7 Introduction to Graph Anomalies and Attacks
7.6 Metrics and Challenges in Anomaly Detection
7.7 Introduction to Graph Visualization Techniques
7.8 Essential Tools and Libraries for Graph Analysis
7.9 Introductory Case Studies
7.70 Glossary of Key Terms
8.8 What are graphs and why are they important?
8.8 Types of graphs and their key structures
8.3 Introduction to graph theory and its essential terminology
8.4 Applications of graphs in various fields
8.5 Graphs in computer security and network analysis
8.6 Tools and libraries for graph analysis
8.7 Graph visualization and data representation
8.8 Introduction to graph algorithms and their usefulness
8.8 Practical examples of graphs in action
8.80 Challenges and opportunities in graph analysis
8.9
9.9 Fundamentals of Graph Theory
9.9 Types of Graphs and Their Properties
9.3 Graph Representation: Matrices and Lists
9.4 Graph Traversal: Depth-First and Breadth-First Search
9.5 Key Concepts: Path, Cycle, Connectivity
9.6 Initial Applications of Graph Theory
9.7 Tools and Libraries for Graph Analysis
9.8 Introduction to Anomaly Detection in Graphs
9.9 Practical Examples and Case Studies
9.90 Introduction to SEO Challenges for Graph Analysis Courses
9.90
1. Mastering Graph Anomaly and Attack Detection
1.1 Fundamentals of Graph Cybersecurity: Introduction to graphs and their application in networks.
1.2 Types of Graph Attacks: Identification and classification of common threats.
1.3 Structure-Based Anomaly Detection: Methods for identifying unusual patterns.
1.4 Behavior-Based Attack Detection: Analysis of malicious behavior patterns.
1.5 Tools and Techniques: Use of software and algorithms for early detection.
1.6 Case Studies: Analysis of real-world attacks and their detection.
1.7 Mitigation and Response: Strategies for containing and responding to threats.
1.8 Systems Integration: Implementation of detection solutions in real-world environments.
1.9 Ethics and Compliance: Legal and ethical considerations in graph cybersecurity. 1.10 Final Project: Implementation of a detection system in a simulated scenario.
2. Unraveling Anomalies and Attacks in Graphs: A Comprehensive Diploma Program
2.1 Introduction to Graph Theory and Cybersecurity: Essential concepts and applications.
2.2 Modeling Networks as Graphs: Representation of systems and data.
2.3 Structural Analysis Techniques: Topology-based anomaly detection.
2.4 Behavioral Analysis: Identification of malicious patterns.
2.5 Advanced Detection Algorithms: Machine learning applied to graphs.
2.6 Specific Threats: Data poisoning attacks and other vectors.
2.7 Incident Response: Containment and recovery strategies.
2.8 Practical Implementation: Development of a detection system. 2.9 Legal and Ethical Aspects: Privacy and Regulatory Compliance
2.10 Final Project: Development of a Defense System for a Graph Network
3. Advanced Exploration of Anomalies and Attacks in Graphs: A Diploma-Based Immersion
3.1 Advanced Graph Fundamentals: Modeling and Analysis of Complex Graphs
3.2 Artificial Intelligence in Graph Cybersecurity: AI and Machine Learning Applications
3.3 Anomaly Detection Techniques: Advanced Detection Methods
3.4 Targeted Attack Detection: Identification of Advanced Threats
3.5 Social Network and Graph Analysis: Detection of Disinformation and Malicious Campaigns
3.6 Data Security in Graphs: Protection of Privacy and Confidentiality
3.7 Advanced Incident Mitigation and Response: Strategies for Responding to Complex Attacks
3.8 Implementation of Detection Systems: Integration in high-security environments.
3.9 Regulatory Aspects and Compliance: Compliance with rules and standards.
3.10 Final Project: Implementation of a complete graph-based cybersecurity solution.
4. Master’s Degree in Threat Identification and Prevention in Graph Networks
4.1 Graph Architecture and Cybersecurity: Design of secure graph-based systems.
4.2 Graph Analysis Methods: Advanced techniques for analyzing complex networks.
4.3 Graph-Based Threat Detection: Application of graphs for threat identification.
4.4 Graph Threat Intelligence: Gathering and analyzing threat information.
4.5 Graph Attack Prevention: Strategies for preventing attacks and vulnerabilities. 4.6 Data Security in Graphs: Techniques for protecting data integrity and confidentiality.
4.7 Incident Response: Strategies for responding to graph security incidents.
4.8 Implementation of Detection and Prevention Systems: Development of security solutions.
4.9 Governance and Compliance: Risk management and regulatory compliance.
4.10 Final Project: Development of a comprehensive graph-based security plan.
5. In-Depth Analysis of Anomalies and Attacks in Graphs: A Specialized Diploma
5.1 Introduction to Advanced Graph Analysis: Specialized techniques and tools.
5.2 Graph Data Modeling for Cybersecurity: Creating accurate and efficient models.
5.3 Graph Anomaly Detection: Advanced methods for detecting unusual patterns. 5.4 Graph Attack Analysis: Identifying and understanding complex attack vectors.
5.5 Graph Forensics: Applying forensic techniques in graph environments.
5.6 Graph-Based Social Media Security: Detecting disinformation and manipulation.
5.7 Risk Mitigation and Incident Response: Advanced mitigation strategies.
5.8 Development of Detection Tools: Creating custom tools.
5.9 Legal and Ethical Framework: Regulations and ethical considerations in graph analysis.
5.10 Final Project: Analyzing and responding to a simulated security incident.
6. Diploma in Graph Attack Detection, Analysis, and Mitigation
6.1 Graph Cybersecurity Fundamentals: Reviewing key concepts and applications.
6.2 Graph Network Architecture: Designing and configuring secure networks.
6.3 Graph Attack Detection: Advanced techniques for detecting and classifying threats.
6.4 Malicious Graph Behavior Analysis: Identifying patterns and trends.
6.5 Graph Risk Mitigation: Strategies to prevent and reduce the impact of attacks.
6.6 Incident Response and Recovery: Planning and executing effective responses.
6.7 Tools and Technologies: Using specialized software and analysis tools.
6.8 Practical Implementation: Developing a detection and response system.
6.9 Regulatory Framework and Compliance: Adherence to security standards and regulations.
6.10 Final Project: Implementing a complete security solution in a real-world environment.
7. Diploma in Expert Detection of Graph Attacks and Anomalies
7.1 Introduction to Graph Threat Detection: Key concepts and principles.
7.2 Graph Data and Network Modeling: Representation of complex systems and data.
7.3 Anomaly Detection Techniques: Advanced methods and algorithms.
7.4 Graph Attack Analysis: Identification and understanding of attack vectors.
7.5 Graph Threat Intelligence: Gathering and analyzing threat information.
7.6 Incident Response: Containment and recovery strategies.
7.7 Implementation of Detection Systems: Development and deployment of solutions.
7.8 Graph Forensics: Application of forensic techniques.
7.9 Legal and Ethical Aspects: Regulatory compliance and ethical considerations.
7.10 Final Project: Design and implementation of a threat detection system.
8. Diploma in Graph Analysis: Threat and Anomaly Detection
8.1 Fundamentals of Graph Analysis: Introduction to concepts and techniques.
8.2 Network and System Modeling in Graphs: Data and relationship representation.
8.3 Anomaly Detection in Graphs: Structure- and behavior-based methods.
8.4 Attack Analysis: Threat identification and classification.
8.5 Tools and Technologies: Use of analysis software and platforms.
8.6 Threat Intelligence: Data collection and analysis on threats.
8.7 Mitigation and Response: Strategies for containing and responding to attacks.
8.8 Practical Implementation: Development of an analysis and detection system.
8.9 Legal and Ethical Aspects: Regulatory compliance and ethical considerations.
8.10 Final Project: Analysis of a real-world case study.
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Proyectos tipo capstones
Admisiones, tasas y becas
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
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