Live Network Traffic Analysis: The Ripples in the Pond Before the Wave

This guest post was contributed by Subo Guha, Stellar Cyber's Senior Vice President of Cybersecurity Strategy and Solutions

The median dwell time for a hacker, once they’ve successfully penetrated a network, is ten days before they’re detected. Think about the amount of damage a sophisticated cybercriminal can do to a business’s systems in that amount of time. While this dwell time is down compared to years past, it’s still concerning. Companies are spending more than ever on cybersecurity tools, but they’re still missing many of the hallmark signs of a cyberattack.

Why?

Too many security solutions are still focused on collecting data from security event logs, better known as SIEMs. These tools are a staple in most enterprise security tech systems. The problem is, legacy SIEMs that collect data from logs reflect a moment in your organization’s past; a snapshot of your network’s historical data. This data is useful, but by the time your security team receives it and spends hours investigating, it may already be too late. 

To get ahead of attackers and reduce their dwell time in your systems, security teams need a holistic defense strategy that combines data from logs with real-time network traffic analysis. Security analysts need accurate alerts, delivered at near-real-time speed, so they don’t waste time investigating false positives while real threats slip through the cracks.

Monitoring and analyzing live network traffic, an approach also known as network detection and response, is more proactive in detecting the warning signs of an impending breach and reducing dwell times. Imagine a stone hitting the surface of a pond. The impact sends out ripples—small disturbances that, if observed closely, can warn of greater disruptions ahead. By recognizing these ripples early, security teams can act before the more severe waves of a cyberattack crash down and cause significant damage.

Why Network Traffic Analysis Shows the First Indicator of an Attack 

Continuously flowing network traffic offers a constant stream of data revealing interaction patterns between users, applications, and systems across diverse environments (on-premises, cloud, hybrid). This dynamic nature contrasts with SIEMs, endpoint detection, and signature-based tools. Security analysts gain an unfiltered view through live network traffic, making its observation, analysis, and timely response essential for early threat detection and damage prevention.

Security teams seeking to build more resilient systems should consider adding live network traffic monitoring capabilities to their arsenal of tools. Live network traffic monitoring assists with three important early indicators of possible compromise:

1. The First Ripple: A Stone Hits the Water (AKA Account Takeover)

   • Repeated login attempts from unusual locations or at odd hours often signal credential stuffing or brute-force attacks. Attackers will cycle through thousands of username-password combinations until they gain access. This initial disturbance in the network should be the first warning sign for security teams.

2. The Second Ripple: Movement Beneath the Surface (AKA Lateral Movement Attack)

   • Once attackers gain access to an account, they begin moving laterally within the environment, looking for higher-value targets. This east-west movement within the network is often detected by Network Detection and Response (NDR) tools. Like ripples expanding outward, an attacker’s presence starts affecting wider portions of the system.

3. The Third Ripple: The Data Breach Expands (AKA Critical Access)

   • If attackers reach an SQL server or another critical data repository, the organization is at serious risk. Sudden access to unfamiliar systems or large outbound data transfers are major red flags. This final ripple signals the potential for data exfiltration and serious consequences if the breach is not contained.

     
     

Analyzing network authentication logs for a surge in failed login attempts is crucial for detecting potential account takeovers early. This "uptick" serves as an initial warning sign. While security teams can identify these anomalies, advanced technology and automation are essential for faster analysis. AI-powered behavioral analytics applied to network traffic excels at identifying deviations from normal patterns, offering timely alerts. Modern AI further enhances this by providing more context around these irregularities, enabling analysts to prioritize critical threats over less significant alerts.

Recognizing the Ripples Before the Wave 

Cyberattacks often start subtly, with network signals that precede larger breaches. Early detection of these initial "ripples" is crucial for security teams.

Consider this example: a company observes a surge in failed login attempts from an international IP address, followed by a successful login from the same unusual location. The compromised account then accesses sensitive data at an abnormal time and initiates large data transfers, indicating exfiltration.

Traditional security measures focusing solely on endpoint alerts or SIEM might not detect this attack in time. However, continuous network traffic analysis empowers MSSPs and mid-market security operations teams to identify and neutralize such threats before data loss occurs. This proactive approach acts as an early warning system, catching the subtle indicators before they escalate into a full-blown breach.

By integrating AI-powered traffic analysis with automated response mechanisms, organizations can identify threats earlier, before an attacker gains control. They can also reduce manual investigations by correlating network traffic anomalies with other security signals. Automation means they can scale security without the cost of scaling teams.

Network detection and response acts as an early warning system, detecting subtle network activity that precedes a full-blown attack. This provides security teams with crucial time to manage potential incidents. By monitoring for these early "ripples," organizations can mitigate disasters before they become unmanageable "breach waves." Security teams should prioritize observing network activity for early signs of compromise.