What is network traffic analysis?

It involves the close examination of network activity to gain insight into how the systems and devices connected over a network are functioning.

Networks are fundamental to most modern enterprises, enabling employees to communicate and collaborate freely and powering critical applications (apps) and business operations.

NTA helps organizations optimize network performance, mitigate network security threats, and troubleshoot any problems before they can spread.

Networking, or computer networking, is the connection of multiple computing devices, such as desktops, mobile devices and routers, so that they can transmit and receive information and resources.

Devices on a network rely on various types of connections for functions, including Ethernet, wireless (wifi) and cellular. They must also adhere to certain protocols that govern how they communicate with one another and the kinds of information they exchange.

The most widespread and well-known network is the internet itself, which powers how people communicate, work and entertain themselves. But as the internet has spread, so have the frequency and cost of cyberthreats, which are attempts to gain unauthorized access to a network.

Last year, the global average cost of a network breach was USD 4.4 million according to the IBM Cost of a Data Breach 2025 Report. While still large, that number is 9% less than the previous year, indicating organizations are taking NTA and threat detection and response (TDR) more seriously than in the past.  

Network security is a field of cybersecurity that focuses on securing the networks and communication systems organizations rely upon from cyberattacks. As businesses embrace new technologies like cloud computing, artificial intelligence (AI) and the Internet of Things (IoT), they expand their digital capabilities. However, doing so also increases the size of their attack surface, a measure of how vulnerable their systems and networks are to cyberattacks.

Every year cyberattacks involving malware and ransomware cost companies millions, leading to increased demand for network security solutions. In 2024, the global network security market was valued at USD 24 billion, with projections indicating a compound annual growth rate (CAGR) of 14% over the next 7 years.¹

Network traffic analysis’s core processes are typically broken down into four steps:

1. Data collection
2. Processing
3. Analysis
4. Visualization

Here’s a closer look at each step and the tools and techniques associated with it.

Before you can analyze network traffic, you need to collect it. Organizations rely on various sources for data collection, including simple devices like routers and switches and more complex network monitoring tools that can collect and analyze data in real-time.

Data capture, a subset of data collection, focuses on data that is flowing across a network and still in its most raw state. Data capture collects unstructured data, often directly from a source, relying on specialized tools like network analyzers, packet sniffers and intrusion detection systems (IDS).

Once collected, data must be filtered through specific criteria to determine whether it contains relevant information or not—a technique known as data processing. Typical information assessed during the processing stage involves IP addresses, ports and common protocols like Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP) and domain name server (DNS).

The purpose of data processing is to transform raw data into valuable, actionable data that can be more easily analyzed. The processing step is crucial in identifying potential threats to a network, optimizing performance and troubleshooting any issues.

After network data has been collected and processed, it is ready to be analyzed. There are five common types of data analysis that NTA relies upon: behavioral, protocol, statistical, payload and flow.

• Behavioral analysis: Behavioral analysis focuses on patterns in network traffic patterns, relying on baseline models to identify suspicious activities. By comparing current, real-time flow data with baseline models, behavioral analysis can determine whether an ebb or spike is evidence of a cyberattack or something else. Advanced behavioral analysis tools use AI and machine learning (ML) to identify abnormal behavior and potential threats.

• Protocol analysis: Network protocols (rules that govern how data is sent and received over a network) provide important clues into overall network health and traffic flows. By analyzing the protocols that devices and systems on a network are following, protocol analysis can determine whether the type of communication occurring represents a threat or not.

• Statistical analysis: Statistical analysis looks at network traffic volume and traffic patterns to try to identify trends or anomalies. By using mathematical formulas on network metrics like volume, packet size and protocol distribution, NTA tools estimate the likelihood that an anomaly signals a cyberthreat or another network issue.

• Payload analysis: Payload analysis looks closely at the contents of network packets (small units of data transmitted over a network) and tries to interpret their meaning. By examining application-layer information, such as web addresses and email subjects, payload analysis helps security teams gain insight into the kinds of communication occurring on a network and spot security threats.

• Flow analysis: Flow analysis examines the flow of network traffic between devices and systems connected over a network. It looks for evidence of troubling patterns that can indicate performance issues or security threats. When conducted effectively, flow analysis helps resolve security incidents and spot potential bottlenecks where network traffic is slowing down in a single location.

Finally, after network traffic data has been collected, processed and analyzed, it must be displayed in a way that it can be reported across an organization, a step known as data visualization. This final step in NTA usually involves dashboards, graphs, charts and other visualization methods that help teams and administrators understand the insights and develop a strategy for dealing with them.

As networks become more complex, organizations are increasingly relying on network traffic analysis (NTA) to monitor network traffic and identify potential threats to IT infrastructure.

From on-premises to cloud, hybrid and even multicloud environments, network administrators are finding endpoint solutions like firewalls and antivirus software insufficient for their needs. As a result, they are relying more heavily on NTA. Network Traffic Analysis (NTA) offers several key benefits for enterprises.

NTA helps administrators uncover insights into the kinds of traffic that is flowing across their networks and the routes it is taking. By uncovering traffic patterns, NTA helps optimize network performance and identify potential bottlenecks where traffic experiences avoidable delays.

Modern NTA solutions rely on AI and ML to automate issue identification and resolution. AI-powered tools enhance operational visibility and help businesses increase network performance and cost efficiencies. According to a survey by The IBM Institute for Business Value (IBV), 51% of executives are already automating certain aspects of IT networking. This figure is projected to grow to 82% over the next 3 years.

NTA can reveal how much of a network is being used in real-time. This insight enables administrators to distribute workloads—defined as the time and computing resources a specific task requires—more strategically and ensure that their networks operate at peak capacity.

By relying on granular measurements from AI and ML monitoring tools, NTA helps administrators spot sudden changes in network conditions and traffic patterns and take appropriate action. Some advanced solutions even generative AI (gen AI) to speed the process of traffic classification and incident tracking.

By constantly measuring network traffic against baseline metrics, NTA allows administrators to identify applications that use more bandwidth than others and allot network resources accordingly.

Strong NTA helps security teams diversify the types of data they monitor on their network, so they aren’t just relying on a single data source for insights. For example, modern network management systems combine flow data, packet capture and log data to provide a comprehensive look at how a network is performing.

Advanced NTA solutions are easily integrated into other network management systems so that they don’t exist in a silo. For example, many modern enterprises rely on security incident and event management (SIEM) tools that can be easily combined with NTA solutions.

As modern enterprises ramp up their digital transformation efforts to keep up with the pace of innovation, the need to closely monitor and analyze network traffic is more critical than ever.

Here are five of the most popular use cases.