Certified Specialist Programme in Security Information and Event Management with Machine Learning
-- viewing nowSecurity Information and Event Management with Machine Learning The Security Information and Event Management (SIEM) with Machine Learning programme is designed for IT professionals and security experts who want to enhance their skills in threat detection and incident response. With the increasing use of machine learning in security, this programme focuses on teaching learners how to integrate machine learning algorithms into SIEM systems to improve threat detection and incident response.
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About this course
Learn how to use machine learning to analyze security data, identify patterns, and predict potential threats.
Develop the skills to implement machine learning-based solutions in your organization and stay ahead of emerging threats.
Explore the programme and discover how you can enhance your security skills and protect your organization from cyber threats.
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Course details
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Machine Learning Fundamentals for Security Information and Event Management
This unit covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. It also introduces the concept of anomaly detection and its application in security information and event management. •
Security Information and Event Management (SIEM) Systems
This unit focuses on the architecture, components, and functionality of SIEM systems, including data collection, processing, and correlation. It also covers the different types of SIEM systems, such as cloud-based and on-premise solutions. •
Threat Intelligence and Incident Response
This unit explores the concept of threat intelligence, including threat hunting, threat analysis, and threat sharing. It also covers incident response strategies, including containment, eradication, recovery, and post-incident activities. •
Network Traffic Analysis and Anomaly Detection
This unit delves into the analysis of network traffic, including protocol analysis, packet capture, and network behavior analysis. It also covers anomaly detection techniques, including machine learning-based approaches. •
User Entity Behavior Analytics (UEBA)
This unit focuses on UEBA, a subset of machine learning-based security analytics that focuses on user behavior analysis. It covers the different types of UEBA, including insider threat detection and advanced persistent threat (APT) detection. •
Cloud Security and Compliance
This unit explores the security and compliance challenges of cloud computing, including data encryption, access control, and cloud security architecture. It also covers cloud security standards and regulations, such as HIPAA and PCI-DSS. •
Artificial Intelligence and Machine Learning in Security
This unit covers the application of artificial intelligence and machine learning in security, including predictive analytics, automated threat detection, and incident response. It also explores the different types of AI and ML models used in security. •
Security Orchestration, Automation, and Response (SOAR)
This unit focuses on SOAR, a security automation platform that integrates security tools and incident response processes. It covers the different components of SOAR, including incident response, threat hunting, and security analytics. •
Cybersecurity Information Sharing and Collaboration
This unit explores the importance of information sharing and collaboration in cybersecurity, including threat intelligence sharing, incident response collaboration, and security information exchange (SIE). •
Machine Learning for Security Information and Event Management
This unit covers the application of machine learning in SIEM, including anomaly detection, threat detection, and incident response. It also explores the different types of machine learning models used in SIEM, including supervised and unsupervised learning.
Machine Learning Fundamentals for Security Information and Event Management
This unit covers the basics of machine learning, including supervised and unsupervised learning, regression, classification, clustering, and neural networks. It also introduces the concept of anomaly detection and its application in security information and event management. •
Security Information and Event Management (SIEM) Systems
This unit focuses on the architecture, components, and functionality of SIEM systems, including data collection, processing, and correlation. It also covers the different types of SIEM systems, such as cloud-based and on-premise solutions. •
Threat Intelligence and Incident Response
This unit explores the concept of threat intelligence, including threat hunting, threat analysis, and threat sharing. It also covers incident response strategies, including containment, eradication, recovery, and post-incident activities. •
Network Traffic Analysis and Anomaly Detection
This unit delves into the analysis of network traffic, including protocol analysis, packet capture, and network behavior analysis. It also covers anomaly detection techniques, including machine learning-based approaches. •
User Entity Behavior Analytics (UEBA)
This unit focuses on UEBA, a subset of machine learning-based security analytics that focuses on user behavior analysis. It covers the different types of UEBA, including insider threat detection and advanced persistent threat (APT) detection. •
Cloud Security and Compliance
This unit explores the security and compliance challenges of cloud computing, including data encryption, access control, and cloud security architecture. It also covers cloud security standards and regulations, such as HIPAA and PCI-DSS. •
Artificial Intelligence and Machine Learning in Security
This unit covers the application of artificial intelligence and machine learning in security, including predictive analytics, automated threat detection, and incident response. It also explores the different types of AI and ML models used in security. •
Security Orchestration, Automation, and Response (SOAR)
This unit focuses on SOAR, a security automation platform that integrates security tools and incident response processes. It covers the different components of SOAR, including incident response, threat hunting, and security analytics. •
Cybersecurity Information Sharing and Collaboration
This unit explores the importance of information sharing and collaboration in cybersecurity, including threat intelligence sharing, incident response collaboration, and security information exchange (SIE). •
Machine Learning for Security Information and Event Management
This unit covers the application of machine learning in SIEM, including anomaly detection, threat detection, and incident response. It also explores the different types of machine learning models used in SIEM, including supervised and unsupervised learning.
Career path
Certified Specialist Programme in Security Information and Event Management with Machine Learning
Job Roles and Career Paths:
1. Security Information and Event Management (SIEM) Specialist
Conduct threat detection, incident response, and security monitoring using SIEM systems. Analyze logs and network traffic to identify security threats and vulnerabilities. Collaborate with security teams to implement security policies and procedures.
2. Machine Learning (ML) Engineer
Design and develop predictive models to detect security threats and anomalies using machine learning algorithms. Implement ML solutions to improve incident response and security monitoring. Collaborate with data scientists to develop and train ML models.
3. Data Analytics Specialist
Analyze security data to identify trends, patterns, and anomalies. Develop data visualizations and reports to present findings to security teams. Collaborate with data scientists to develop and implement data analytics solutions.
4. Cyber Security Consultant
Conduct security risk assessments and vulnerability analysis to identify security threats and weaknesses. Develop and implement security policies and procedures to mitigate security risks. Collaborate with security teams to implement security solutions.
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Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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CERTIFIED SPECIALIST PROGRAMME IN SECURITY INFORMATION AND EVENT MANAGEMENT WITH MACHINE LEARNING
is awarded to
Learner Name
who has completed a programme at
London School of Planning and Management (LSPM)
Awarded on
05 May 2025
Blockchain Id: s-1-a-2-m-3-p-4-l-5-e
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