Description:
In this 14-video course, learners can explore best practices for anomaly detection for network forensics with topics such as network behavior anomaly detection (NBAD), frequency analysis, identifying beaconing activity, and recognizing signs of brute force attacks. Also discover protocol and population analysis, HTTPS and SSH (Secure Shell) attacks, as well as triage methods. Begin with a look at concepts and applications of NBAD, then discover how to implement frequency analysis. Learn how to identify beaconing activity, and how to recognize the signs of a brute force attack. Next, learners examine protocol analysis approaches and techniques, and learn about HTTPS attacks, deducing the activity of encrypted web traffic. Analyze SSH authentication behavior; take an overview of population analysis; explore techniques used to reveal hidden connections with behavioral analysis; and learn how to differentiate between different NBAD triage methods. In the final tutorials, discover methods and techniques for performing network anomaly analysis and the benefits of anomaly detection, and examine how network forensics can be used to protect mission critical areas of business.
Target Audience:
Duration: 01:12
Description:
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.
Target Audience:
Duration: 00:55