How does network traffic analysis work?

This position is also available in:
עברית (Hebrew)

Using machine learning and rule-based algorithms, network traffic analysis is the process of analyzing network traffic. It helps to regularly monitor the network and manage it appropriately when suspicious activity or a security threat is identified. A more serious malware threat may be reported to the IT team for further processing.

Many security tools are available today, such as firewalls and intrusion prevention systems (IPS). Although these tools are capable of protecting the network from unauthorized traffic, they are still limited. In contrast, NTA protects the network from both internal parameter threats and external threats from the cloud, virtual switches, and traditional TCP/IP packets.

The site points out that machine learning algorithms are used alongside traditional NTA algorithms to analyze the behavior of a network. Whenever the algorithms detect abnormal activity, the NTA tool alerts the network group. In this way, it is possible to provide analytics, monitor IoT devices that produce and transmit large amounts of data, address various security issues, and improve visibility in the cloud.

In addition to forecasting and analysis capabilities, the NTA offers high-throughput encrypted network analysis and asset monitoring. Despite its strengths, the system has some weaknesses: Data storage – existing data is needed to train machine learning; Most NTA tools do not store old data but rely on new data; and Cost and complexity – NTA solutions require the use of additional tools and storage devices, which means higher maintenance costs.

Charles J. Kaplan