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*Modules of our curriculum are subject to change. We update our curriculum based on the new releases of the libraries, frameworks, Software, etc. Students will be informed about the final curriculum in the course induction class.
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Cyber Security Analytics merges with the dynamic intersection of cloud computing and cybersecurity in LSET’s course. Gain expertise in safeguarding cloud infrastructures against evolving threats using cutting-edge AI technologies.
Cloud Computing: Learn about the infrastructure and services provided over the Internet, which enable scalable and flexible access to computing resources for Cyber Security Analytics.
Artificial Intelligence (AI): Explore the simulation of human intelligence processes by machines, including learning, reasoning, and problem-solving, essential for Cyber Security Analytics.
Machine Learning (ML): Dive into the subset of AI focusing on enabling computers to learn from data and improve performance over time without explicit programming, crucial for Cyber Security Analytics.
Threat Intelligence Platforms: Discover tools that collect, analyze, and disseminate information about cyber threats to support proactive cybersecurity measures, which is integral for Cybersecurity Analytics.
Data Visualization Tools (e.g., Tableau, Power BI): Master software applications that transform complex data sets into visual representations, aiding in understanding and decision-making in Cyber Security Analytics.
Big Data Analytics (e.g., Hadoop, Spark): Explore frameworks and tools for processing and analyzing large volumes of data, unlocking insights and trends crucial for informed decision-making in Cyber Security Analytics.
Security Information and Event Management (SIEM) Solutions: Learn about platforms that provide real-time analysis of security alerts generated by network hardware and applications, facilitating threat detection and response in Cyber Security Analytics.
Identity and Access Management (IAM) Tools: Understand solutions for managing user identities and access permissions across cloud and on-premises environments, ensuring secure and compliant access to resources in Cyber Security Analytics.
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Join the LSET Cyber Security Analytics and Threat Intelligence Certificate course to prepare yourself. LSET follows a project-based, hands-on approach to develop real-world applications using the latest industry standards.
>> Overview of cyber security analytics: Role in detecting and mitigating cyber threats | >> Types of cyber security data: Logs, network traffic, endpoint telemetry, and threat intelligence feeds | >> Challenges and opportunities in cyber security analytics |
>> Principles of data visualisation: Design principles, chart types, and visualisation tools | >> Visualising cyber security data: Log analysis, network traffic analysis, and incident timelines | >> Communicating insights through visualisations: Dashboards, reports, and presentations |
>> Introduction to machine learning for cyber security: Supervised, unsupervised, and semi-supervised learning techniques | >> Machine learning models for threat detection: Anomaly detection, classification, and clustering | >> Feature engineering and model selection for cyber security applications |
>> Overview of AI-driven threat intelligence platforms: Features, capabilities, and benefits | >> Leveraging AI for threat intelligence analysis: Natural language processing (NLP), entity extraction, and sentiment analysis | >> Integrating threat intelligence into security operations: Indicators of compromise (IOCs), threat feeds, and threat hunting |
>> Challenges of analysing big data in cyber security: Volume, velocity, variety, and veracity | >> Technologies for big data analytics: Hadoop, Spark, and distributed computing frameworks | >> Analysing large-scale cyber security datasets: Log aggregation, correlation, and pattern recognition |
>> Introduction to threat hunting: Proactive identification of cyber threats | >> Leveraging data analytics for threat hunting: Behavioural analytics, anomaly detection, and correlation analysis | >> Incident response analytics: Investigative techniques, root cause analysis, and post-incident review |
>> Privacy considerations in cyber security analytics: Personally identifiable information (PII), data anonymisation, and privacy-preserving techniques | >> Ethical considerations in cyber security data analytics: Transparency, fairness, and accountability | >> Compliance with regulations and standards: GDPR, HIPAA, and industry-specific requirements |
>> Importance of real-time analytics in cyber security: Detecting and responding to threats in real-time | >> Technologies for real-time data processing: Stream processing, complex event processing (CEP), and in-memory databases | >> Implementing real-time cyber security analytics: Use cases and best practices |
>> Analysing cyber threat intelligence data: Identifying threat actors, tactics, techniques, and procedures (TTPs) | >> Threat intelligence sharing and collaboration: Information sharing platforms, ISACs, and ISAOs | >> Applying threat intelligence to security operations: Threat hunting, incident response, and vulnerability management |
>> Emerging trends in cyber security analytics and threat intelligence | >> Future challenges and opportunities in analysing and interpreting cyber security data | >> Ethical considerations in cyber security analytics: Privacy, transparency, and accountability |
*Modules of our curriculum are subject to change. We update our curriculum based on the new releases of the libraries, frameworks, Software, etc. Students will be informed about the final curriculum in the course induction class.
We love to answer questions, empower students, and motivate professionals. Feel free to fill out the form and clear up your doubts related to our Cyber Security Analytics and Threat Intelligence Course.
Start Your Journey to becoming a Professional Cyber Security Analytics and Threat Intelligence
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