Anomaly Detection in Cybersecurity
( 30 Modules )

Module #1
Introduction to Anomaly Detection
Overview of anomaly detection, its importance in cybersecurity, and types of anomalies

Module #2
Anomaly Detection vs. Traditional Rule-Based Systems
Comparison of anomaly detection with traditional rule-based systems, advantages and limitations

Module #3
Types of Anomalies
Point anomalies, contextual anomalies, and collective anomalies, examples and scenarios

Module #4
Anomaly Detection Techniques
Overview of statistical, machine learning, and machine learning-based techniques

Module #5
Statistical Anomaly Detection
Mean, median, and standard deviation-based methods, z-score and modified z-score

Module #6
Machine Learning Anomaly Detection
Supervised, unsupervised, and semi-supervised learning approaches, one-class SVM and LOF

Module #7
Deep Learning Anomaly Detection
Autoencoders, LSTM and GRU-based anomaly detection, anomaly detection using CNN

Module #8
Data Preparation for Anomaly Detection
Data preprocessing, feature engineering, and feature selection for anomaly detection

Module #9
Evaluation Metrics for Anomaly Detection
Precision, recall, F1-score, ROC-AUC, and precision-recall curve

Module #10
Handling Imbalanced Data
Techniques for handling class imbalance, oversampling, undersampling, and SMOTE

Module #11
Anomaly Detection in Network Traffic
Network traffic analysis, NetFlow, and IPFIX, anomaly detection in network traffic

Module #12
Anomaly Detection in System Calls
System call analysis, system call sequences, and anomaly detection in system calls

Module #13
Anomaly Detection in Logs
Log analysis, log parsing, and anomaly detection in logs

Module #14
Anomaly Detection in Endpoint Data
Endpoint data analysis, process monitoring, and anomaly detection in endpoint data

Module #15
Anomaly Detection in Cloud and IoT
Cloud and IoT security, anomaly detection in cloud and IoT environments

Module #16
Anomaly Detection Tools and Technologies
Open-source and commercial tools for anomaly detection, ELK Stack, Splunk, and OSSEC

Module #17
Case Studies in Anomaly Detection
Real-world case studies of anomaly detection in cybersecurity, lessons learned and best practices

Module #18
Challenges and Future Directions
Challenges in anomaly detection, future directions, and emerging trends

Module #19
Implementing Anomaly Detection in a Production Environment
Deploying and integrating anomaly detection models in a production environment

Module #20
Advanced Topics in Anomaly Detection
Advanced techniques, such as graph-based anomaly detection, and explanation methods

Module #21
Anomaly Detection in AI and ML Systems
Anomaly detection in AI and ML systems, AI-powered anomaly detection, and adversarial attacks

Module #22
Explainability and Interpretability in Anomaly Detection
Explainability and interpretability techniques for anomaly detection models

Module #23
Human-Machine Collaboration in Anomaly Detection
Human-machine collaboration, visualization, and decision support systems

Module #24
Anomaly Detection in Cyber-Physical Systems
Anomaly detection in cyber-physical systems, IoT, and SCADA systems

Module #25
Anomaly Detection in Identity and Access Management
Anomaly detection in identity and access management, authentication, and authorization

Module #26
Anomaly Detection in Incident Response
Anomaly detection in incident response, threat hunting, and digital forensics

Module #27
Anomaly Detection in Compliance and Governance
Anomaly detection in compliance and governance, regulatory requirements, and standards

Module #28
Anomaly Detection in Cloud Security
Anomaly detection in cloud security, cloud workload protection, and cloud security posture management

Module #29
Anomaly Detection in Endpoint Security
Anomaly detection in endpoint security, endpoint detection and response, and endpoint protection

Module #30
Course Wrap-Up & Conclusion
Planning next steps in Anomaly Detection in Cybersecurity career

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Anomaly Detection in Cybersecurity ( 30 Modules )

Anomaly Detection in Cybersecurity
( 30 Modules )