How to Detect Security Problems Before They Impact Your Network and Users

What’s the Key to Detecting Security Problems Earlier? 

Maintaining a stable network and preventing security issues are challenging without full visibility into what is going on within that network. This might sound obvious, yet most network teams out there lack exactly this full level of visibility to do their jobs well.  When gaps exist, threats can infiltrate your network unnoticed, creating problems before you can detect them. Unfortunately, these blind spots are common, with organizations worldwide monitoring only 66% of their IT environments.

Understanding how to improve network observability helps close these gaps, allowing for earlier threat detection, proactive issue resolution, and preventing configuration issues that impact users and compromise security.

Solving Visibility Blindspots

If your network team wants to improve visibility, one of the fastest ways to achieve it is by leveraging NetFlow data. However, you might be wondering, "NetFlow? Isn’t that just for Top-N dashboards?" While it’s true that NetFlow supports tasks like showing which applications consume the most bandwidth, that is only the tip of the iceberg of what it can do. With the right network observability platform, NetFlow can also support:

Spotting network anomalies. Are there unusual traffic patterns? DDoS attacks? Internal threats? NetFlow data helps you identify these issues early, so you can take action before threats escalate.

Differentiating benign actors from real threats. NetFlow helps you distinguish between false alerts and genuine threats, saving time and allowing you to focus on actual security issues.

Improving security posture. Beyond detecting traffic spikes, NetFlow offers the granularity needed to spot insider threats, data exfiltration, or rogue devices on your network.

When combined with machine learning, NetFlow can learn what’s "normal" for your network, establish baselines, and more easily detect deviations from the norm.

Finding Hidden Threats by Using Unsampled Data and Machine Learning 

Unsampled flow data allows your team to detect subtle traffic abnormalities that may signal reconnaissance efforts. Using machine learning, you can uncover unusual activity, such as slow, steady information leaks that a traditional sampled approach might overlook.

Think of it like a traffic camera. If the police are searching for a "bad actor’s" license plate, relying on a camera that takes one picture every five minutes instead of a continuous video is likely to fail and miss the bad actor most times. This is exactly what sampling Netflow traffic does to the network security stance of any company using sampled data. The picture might capture a scenario where 10,000 trucks are blocking all lanes (a DDoS attack), but catching other bad actors is pure luck. With access to all network traffic and an observability platform to quickly sift through the data, identifying bad actors becomes much easier.

Machine learning can also highlight internal traffic taking unusual paths that need attention. For instance, it can detect devices from the engineering org accessing accounting software—behavior that might point to information leakage or be in conflict with your micro-segmentation strategy.

Here are several ways machine learning can use unsampled data to protect your organization:

DDoS detection. Identifies sudden spikes in traffic volume that could indicate distributed denial-of-service attacks.

Port scanning detection. Analyzes connection attempts across multiple ports to detect potential reconnaissance activities.

Unusual protocol usage. Identifies abnormal usage patterns of network protocols that might signify malicious activity.

Data exfiltration detection. Monitors for unusual outbound data transfers that could suggest unauthorized data exfiltration.

Service degradation analysis. Detects unusual increases in response times or error rates for critical services.

Link saturation monitoring. Identifies network links approaching or exceeding capacity limits.

Routing anomaly detection. Detects unexpected changes in network routing patterns.

Device health monitoring. Analyzes device metrics for unusual patterns that might indicate impending failures.

Traffic pattern analysis. Identifies unusual shifts in overall network traffic patterns.

With a platform like ElastiFlow, machine learning jobs are designed to work with unsampled IPFIX (Internet Protocol Flow Information Export) flow metadata, ensuring that 100% of network data is analyzed for the most accurate and comprehensive anomaly detection.

How ElastiFlow Can Help 

ElastiFlow helps you to process unsampled data with support for over 7,500 vendor-specific fields, allowing real-time identification of security issues or device misconfigurations before they cause damage. The ElastiFlow NetObserv platform offers several benefits:

Real-time analysis and early detection of network vulnerabilities. ElastiFlow’s ability to handle unsampled data, including vendor-specific fields, allows for immediate identification of potential security threats. This level of detail helps your team quickly spot issues, such as potential DDoS attacks or unusual internal traffic, before they grow. Advanced network security detection allows for proactive action, minimizing the risk of security breaches and downtime.

Comprehensive visibility across all egress points. ElastiFlow collects flow data from every egress point on your network—not just the main connections. All exit points, whether major links or secondary connections, are monitored for potential threats. With this full visibility, your team can see all traffic entering and leaving the network, reducing the chances of undetected malicious activity.

Integrating flow data from multiple sources. ElastiFlow’s flexibility in accepting flow data from various sources, like AWS VPC flow logs and transit gateway logs, creates a unified view for enhanced visibility and more effective monitoring.

A proactive approach to network visibility, combined with unsampled flow data and machine learning-powered analytics—such as those offered by ElastiFlow—can help network teams prevent security issues before they impact users.

Want to learn more about how to detect security problems before they impact users? Check out our full guide to discover how unsampled flow data and machine learning can help you improve network security through proactive threat detection and early intervention.