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.
Anomaly Detection: Aspects of Anomaly Detection