From the course: Data-Driven Network Security Essentials
Join today to access over 22,600 courses taught by industry experts or purchase this course individually.
Machine learning to detect a network anomaly
From the course: Data-Driven Network Security Essentials
Machine learning to detect a network anomaly
- [Instructor] Machine learning is a technique that allows a computer to make a decision on behalf of human operators. When given a data set, it uses statistics and pattern matching to arrive at a conclusion. In the context of detecting a network anomaly, such as an intrusion attempt, a machine learning algorithm can zip through numerous network events logged by various sources and identify an unusual activity that can lead to a security breach. To implement the machine learning solution, it is necessary to have a relevant data set, machine learning algorithm, and a computing platform. For the purpose of intrusion detection, it is sufficient to have network event logs capturing the details of packets that are either coming into or going out of the network of interest. Machine learning algorithm uses multiple features of the data set and builds a learning model that enables eventual decision making. In our example of intrusion detection, the features include when each packet was…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.