Anomaly Detection: Aspects of Anomaly Detection

Expert
  • 11 videos | 54m 54s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 32 users Rating 4.5 of 32 users (32)
Network anomalies are behaviors or activities that deviate from the norm. It is important that security professionals learn to monitor these anomalies in network traffic because the traffic could be malicious. In this 11-video course, you will explore roles that network and security professionals play in detecting and addressing anomalies. Begin by looking at different types of anomalies or outliers, such as configuration faults or a malicious presence; then take a look at benefits of anomaly detection, such as early response and planning for the unexpected. Learners will also examine the limitations of traditional approaches to anomaly detection, such as chasing false positives; learn how to differentiate between manual and automated detection techniques; and view the importance of building a profile of what is normal, such as user activity, before looking at multimodel attributes and how they relate to anomaly detection. Furthermore, you will explore differences between least frequency of occurrence and baselining; view the benefits of machine learning; and finally, learn how to recognize benefits of auto-periodicity to aid in identifying anomalies.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Recognize different anomalies or outliers, such as configuration faults or a malicious presence
    Describe the benefits of anomaly detection, such as early response and planning for the unexpected
    Recognize limitations of traditional approaches to anomaly detection, such as chasing false positives
    Differentiate between manual and automated detection techniques
    Describe the importance to building a profile of what is normal, such as user activity
  • Describe multimodal attributes and how they relate to anomaly detection
    Differentiate between least frequency of occurrence and baselining
    Describe the benefits of machine learning
    Recognize the benefits of using auto-periodicity to aid in identifying anomalies
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 2s
  • 11m 4s
    Upon completion of this video, you will be able to recognize different anomalies or outliers, such as configuration faults or a malicious presence. FREE ACCESS
  • Locked
    3.  Benefits of Anomaly Detection
    6m 35s
    After completing this video, you will be able to describe the benefits of anomaly detection, such as early response and planning for the unexpected. FREE ACCESS
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    4.  Traditional Approaches
    6m 10s
    After completing this video, you will be able to recognize the limitations of traditional approaches to anomaly detection, such as chasing false positives. FREE ACCESS
  • Locked
    5.  Manual vs. Automated Detection
    4m 39s
    Learn how to differentiate between manual and automated detection techniques. FREE ACCESS
  • Locked
    6.  Baselining
    3m 44s
    Upon completion of this video, you will be able to describe the importance of building a profile of what is normal, such as user activity. FREE ACCESS
  • Locked
    7.  Multimodal Attributes
    4m 42s
    Upon completion of this video, you will be able to describe multimodal attributes and how they relate to anomaly detection. FREE ACCESS
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    8.  Least Frequency of Occurrence
    5m 26s
    Find out how to differentiate between the least frequency of occurrence and baselining. FREE ACCESS
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    9.  Machine Learning
    5m 52s
    After watching this video, you will be able to explain the advantages of using machine learning. FREE ACCESS
  • Locked
    10.  Auto-periodicity Detection
    3m 47s
    Upon completion of this video, you will be able to recognize the benefits of using auto-periodicity to aid in identifying anomalies. FREE ACCESS
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    11.  Course Summary
    53s

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