How network traffic monitoring turns network traffic into clear, actionable visibility

Visibility is paramount in today's distributed enterprise environments where data prevails as a primary target for malicious actors. For security professionals, a blind spot for monitoring of the network is a critical missing defense compromising organizational resilience, speed, and defense against threats. This is why network traffic monitoring—the practice of collecting, analyzing, and interpreting traffic flowing across a network—is not only a recommended best practice, but a foundational requirement for both security and operational excellence.

This article will define network traffic monitoring, provide suggestions to help monitor network traffic, and explore the advanced security capabilities that drive modern defense strategies.

What is network traffic monitoring?

Network traffic monitoring is the process of continuously observing the data flowing through a network infrastructure to gain insights into its performance, health, and security posture. It involves capturing, aggregating, and analyzing raw traffic data to identify patterns, anomalies, and potential issues.

There are two main goals for network traffic monitoring:

How does network traffic monitoring work?

The process of monitoring network traffic is a continuous cycle of data collection, processing, analysis, and alerting. It is the engine that drives network visibility.

1. Data source collection

Effective network traffic monitoring relies on the following data sources:

• Flow data (NetFlow/IPFIX/sFlow): This is the most common method. Flow records summarize a conversation between two endpoints (Source IP, Destination IP, ports, protocol, timestamp, and byte/packet counts). They are highly scalable, consuming minimal storage, but lack the granular detail of the actual data payload.
• Log data: Logs provide event-driven records that offer context about network devices or application activities. They are timestamped records generated by operating systems, applications, and network devices (servers, firewalls, proxies) detailing events, errors, or status updates.
• Simple network management protocol (SNMP): This protocol queries devices (routers, switches, firewalls) for operational data, such as CPU load, interface status, and error counts. It provides health and performance metrics but is not suitable for deep traffic inspection.
• Packet capture (PCAP): Full packet capture involves recording every bit of data that traverses the network segment. This is the gold standard for monitoring network activity and forensic analysis, as it allows analysts to reconstruct full conversations and inspect malicious payloads. However, it is resource-intensive for storage and processing, making selective or metadata-generation strategies essential in large-scale enterprise network traffic monitoring.

2. Data aggregation and analysis

Once collected, the raw data must be processed:

• Baselining: The system establishes a baseline of "normal" traffic patterns. This baseline includes expected volumes, common protocols, and typical communications between hosts. This is crucial for detecting changes in network behavior.
• Real-time processing: Flow data is ingested and analyzed in near real-time network traffic monitoring to identify immediate deviations from the baseline, such as bandwidth utilization, latency, sudden, high-volume data transfers (exfiltration), or unexpected communication on non-standard ports.
• Deep Packet Inspection (DPI) & metadata generation: Modern security tools perform DPI on full packet streams to extract security-relevant metadata—like DNS requests, HTTP user-agents, and SSL/TLS certificate details—without retaining the entire, privacy-sensitive packet payload. This rich metadata (often called network evidence or logs) is the most important data source for NDR. It provides the context needed for behavioral AI to detect anomalies at scale.

3. Correlation and alerting

The monitoring system correlates traffic events with other data sources (logs, endpoint telemetry). When a threshold is crossed or an anomaly is detected, the system generates an alert, which can range from a low-priority notification about high link utilization to a critical alert about potential malware C2 activity.

How to monitor network traffic

Implementing an effective network traffic monitoring solution requires a structured approach, integrating both performance and security considerations. This instructional guide covers the necessary steps to check network activity and establish a mature monitoring program.

Tools for network traffic monitoring (security focus)

1. Network detection and response (NDR) platforms: These are the most effective modern tools for security-focused network traffic monitoring. They transform raw traffic into actionable security intelligence.
2. Zeek^® (formerly Bro): An open-source network security monitoring framework that generates rich, human-readable metadata logs (e.g., all HTTP requests, DNS queries, SSL certificates) from live traffic. It is a foundational component for many NDR solutions. The structured, comprehensive nature of this Zeek-generated data is critical because it provides the discrete data points (protocol, file hash, connection status, etc.) that machine learning models rely on to accurately build behavioral baselines and identify sophisticated, low-and-slow anomalies. It is a foundational component for many NDR solutions.
3. Intrusion Detection Systems (IDS): A security tool designed to monitor network or system activities for malicious activity or policy violations. IDS tools function by comparing observed activity against a database of known attack signatures (signature-based) or by identifying deviations from normal patterns (anomaly-based), generating an alert when a potential threat is detected.
4. Wireshark/tcpdump: Essential for forensic packet analysis when investigating a high-fidelity alert.

Key challenges in enterprise network traffic monitoring (security focus)

Despite its importance, effective enterprise network traffic monitoring presents several hurdles for security professionals:

• Encryption overload: The vast majority of internet traffic is now encrypted (HTTPS, TLS 1.3). This prevents standard deep packet inspection, leading to visibility gaps.

• Data volume and storage: In large environments, the sheer volume of traffic data (petabytes daily) makes full, long-term packet capture prohibitively expensive and complex.

• Alert fatigue: Poorly tuned systems can generate millions of alerts, forcing analysts to manually sift through noise, often leading to legitimate threats being missed. This challenge makes reducing false positives critical.

• Distributed environments: Monitoring traffic across hybrid and multi-cloud environments, remote workforces, and legacy on-premises systems fragments visibility and requires disparate tools.

Advanced detection with network detection and response (NDR)

To overcome the challenges of encryption and volume, modern network detection and response (NDR) platforms have become essential tools for security professionals. NDR offers security-relevant visibility where traditional methods fail.

The core advantage of NDR is its focus on protocol and metadata analysis rather than just raw packet content, especially for encrypted traffic.

• Protocol analysis without decryption: NDR tools can analyze the outer metadata of encrypted sessions. They look at TLS/SSL certificate details, server name indication (SNI), jitter, flow size, and connection frequency. For example, a host rapidly connecting to a series of newly registered domains with self-signed certificates is highly suspicious, even if the content remains encrypted.
• Behavioral AI and machine learning: NDR uses AI or machine learning models to analyze massive amounts of flow and metadata. These models excel at recognizing subtle deviations from normal behavior—a crucial step in reducing false positives in network traffic alerts. ML can detect the unique "fingerprints" of C2 communication or exfiltration without relying on static signatures.

NDR allows security teams to monitor encrypted traffic without decrypting everything, providing the necessary security insights while maintaining privacy and performance.

The NDR detection pipeline: from raw data to actionable alerts

At its core, NDR is a closed-loop system built on transforming raw network traffic into high-fidelity security events. This process formally follows a four-stage pipeline:

The evolution to network detection and response (NDR)

Network detection and response has evolved from network traffic analysis (NTA)— the process of monitoring and inspecting network traffic patterns to identify anomalies or suspicious behavior, serving both performance troubleshooting and preliminary threat detection—and NSM. NDR solutions utilize advanced machine learning (ML), behavioral analytics, and continuous active monitoring to identify known and unknown cyber threats. Unlike passive monitoring tools, NDR automates protective actions, such as terminating suspicious network connections to disrupt an attack, and integrates seamlessly with Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) systems for rapid incident response.

For security professionals, solely relying on traditional performance network traffic monitoring tools that lack security correlation and deep behavioral analytics can be inefficient. The industry trend favors unified platforms that natively fuse performance context (NTM data) with deep security analysis (NSM data). This unification eliminates the blind spots and the operational burden associated with correlating data across disparate tools—known as "swivel-chair" monitoring.