How does network analytics benefit from AI/ML techniques?

Network analytics uses a combination of local and cloud-based AI-driven analytics engines to make sense of all collected data. Using AI and ML, network analytics customizes the network baseline for alerts, reducing noise and false positives while enabling IT teams to identify issues, trends, anomalies, and root causes accurately. AI/ML techniques along with crowdsourced data are also used to reduce unknowns and improve the level of certainty in decision making ciso engineer.

Artificial intelligence (AI)

Artificial intelligence simulates intelligent decision making in computers. Many sources confuse artificial intelligence with machine learning (ML); machine learning is a subset of the many types of applications that result from the field of artificial intelligence.

Machine learning (ML)

Use of ML can improve analytics engines. With ML, the parameters in the decision tree can be improved based on experience (cognitive learning), peer comparison (prescriptive learning), or complex mathematical regressions (baselining).

ML offers large increases in the accuracy of insights and remediation, because with it the decision trees are modified to meet the specific conditions of a network's configuration, its installed hardware and software, and its services and applications.

In cases when an analytics engine may not have enough information to unequivocally identify endpoints, it may use ML to group together endpoints with similar characteristics. These clustering algorithms consider the distance between cluster members, density areas of the data space, and other factors when clustering objects, much like a human would. In many cases, the algorithms cluster more consistently and across many more dimensions than would be feasible for a human. Such clusters may be used by administrators to remove ambiguity and profile endpoints accurately.

ML is a subset of AI, since it gives analytics engines the ability to automatically learn and improve from experience without being explicitly programmed.

Machine reasoning (MR)

When analytics engines are programmed to reason through logical steps, MR is achieved. This capability can enable an analytics engine to navigate through a number of complex decisions to solve a problem or a complex query.

With MR, analytics can compare multiple possible outcomes and solve for an optimal result, using the same process that a human would. This is an important complement to ML.