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By using machine learning and rule-based algorithms, network traffic analysis is the process of analyzing network traffic. It enables the network to be monitored regularly and handled appropriately when a suspicious activity or security threat is identified. A more serious malicious threat can be reported to the IT team for further treatment.
Many security tools are available today, such as firewalls and intrusion prevention systems (IPS). While these tools are capable of protecting the network from unauthorized traffic, they are still restricted. By contrast, NTA protects the network from both inside-parameter threats and outside threats originating from the cloud, virtual switches, and traditional TCP/IP packets.
The securityboulevard.com website points out that machine learning algorithms are used together with traditional NTA algorithms to analyze how a network behaves. Whenever the algorithms detect abnormal activity, the NTA tool alerts the network group. In this way, it is possible to provide analysis, monitor IoT devices that produce and transmit large amounts of data, solve a variety of security problems, and improve visibility in the cloud.
In addition to forecasting and analysis capabilities, the NTA offers high speed, encrypted network analysis and resource 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, meaning maintenance costs are higher.
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