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Cybersecurity risks can manifest in forms ranging from phishing attempts to advanced ransomware attacks. Therefore it is crucial to have solutions, like Network Detection and Response (NDR) and Extended Detection and Response (XDR). While these terms are occasionally used interchangeably they fulfill different roles.

This article will delve into the intricacies of NDR and XDR highlighting their disparities, practical applications, and their innovative approaches, to detecting and responding to threats.

About NDR

Network Detection and Response (NDR) tools are designed to monitor raw network traffic, detect and respond to attacks, and provide visibility into all network activities, including north/south and east/west (lateral) movement, traffic from remote users, and cloud, hybrid, and multi-cloud environments. NDR platforms gather and record data about network protocol activity, creating log files that are typically ingested by a Security Information and Event Management (SIEM) system for analysis and review.

Ideally, NDR should combine alerts (both signature-based and anomaly-based), network data, and behavioral analytics to create a synergistic detection system. Many sophisticated NDR platforms utilize machine learning and automation to detect and respond to network-based attack techniques such as command and control (C2), data exfiltration, and unauthorized access. These systems generate baseline models of normal network behavior and identify suspicious traffic patterns, triggering alerts when anomalies are detected.

One challenge with NDR is the expense of storing packet capture (PCAP) data. However, advanced NDR platforms can extend look-back windows by setting capture rules based on triggers such as alerts, protocol type, or encryption status. This selective packet capturing helps security teams uncover more evidence of cyber breaches and compile richer evidence for criminal investigations or regulatory compliance. Security teams use NDR to gain early detection of network security issues, improve visibility, enhance analytics, reduce mean time to detection (MTTD) and mean time to resolution (MTTR), and ensure regulatory compliance.

About XDR

Extended Detection and Response (XDR) solutions aim to connect and correlate Endpoint Detection and Response (EDR) data with various security data types, such as network, email, and cloud workloads. XDR adds analytics and automation to enhance threat detection and response, providing a more comprehensive approach to incident management. XDR can extend to cloud security and identity and access management, integrating data from multiple security tools to offer a broad view of the security landscape.

XDR shows a complete, connected view of an attack’s impact, helping security teams respond quickly and effectively. When an attack occurs, XDR platforms provide information on the initial infection point, impact scope, historical data, and more, enhanced by cross-domain detections and integrated response capabilities. Despite its potential, there is ongoing debate regarding XDR's definition and capabilities. Some vendors market it as a catch-all solution, which may not always deliver the promised performance and can lead to vendor lock-in and added complexity.

Security teams should evaluate whether a potential XDR solution can integrate with their existing security stack before making a selection. This ensures that the solution enhances rather than hinders their threat detection and response capabilities.

Is XDR the Same as NDR?

Although XDR and NDR share similar use cases, they are not the same. NDR platforms focus solely on network-level detection, using signature-based detection, deep packet inspection, advanced analytics, machine learning, and behavioral analysis techniques to provide full visibility into network traffic. In contrast, XDR integrates data sources across networks, endpoints, and cloud environments, expanding the capabilities of SIEM and SOAR systems with additional correlations and analytics.

The NDR platform like Stellar Cyber provides full network visibility, which is vital for detecting and responding to threats effectively. So, understanding the differences between these two systems requires a closer look at what XDR is and how it is used.

How XDR Works

For many vendors, XDR consists of two or more vendor-specific log sources, often EDR and firewall, with some Active Directory log integration for additional context and enrichment. In some cases, machine learning engines built on top of these data sets provide anomaly or user behavior analytics, which requires data normalization. If the data from EDR, firewall, and other sources aren't in similar formats, they need to be processed and normalized before analysis, adding complexity and delaying responses.

Once these log sources are aggregated, the XDR platform helps support security operations by correlating alerts into attack campaigns, providing a single interface for investigating and responding to security alerts. In essence, XDR acts as a vendor-specific security orchestration, automation, and response platform with customized cross-product playbooks and vendor-specific machine-learning engines.

NDR and XDR Comparison

Both NDR and XDR are used to help customers detect and respond to threats. However, their fundamental differences lie in the data source, analytic approach, and deployment requirements.

Category NDR XDR
Data Source Utilizes network taps, traffic mirrors, or AWS flow logs across on-premises, virtual, hybrid, or public cloud environments. Combines endpoint agents analyzing host process behavior, next-generation firewall (NGFW) appliances analyzing network traffic, and potentially other data sources.
Deployment Location No agents required; operates out-of-band in the cloud, data center, and remote sites. Requires agents on each endpoint and NGFW appliances for greater visibility.
Deployment Model Low deployment friction, minimal performance impact. High deployment friction, potential performance impact when monitoring east-west traffic.
Fundamental Approach Purpose-built for passive monitoring of L2-L7 network data, leveraging machine learning and threat intelligence, avoiding vendor lock-in. Vendor-specific, often integrating with EDR or NGFW, which can limit third-party integrations and add complexity.

Drawbacks of XDR

While XDR offers benefits, it also has its drawbacks. Many vendors specialize in specific security tools, such as EDR or NGFW, and when they build additional capabilities outside their core competencies, the result is often a flawed toolkit. This can lack important feature functionality and detection capabilities.

Security analysts must be aware of these limitations when considering XDR solutions. Although it has huge benefits, XDR can also introduce unnecessary complexity and delays, hindering threat detection and response.

Conclusion

Choosing between NDR and XDR depends on an organization's specific needs and existing security infrastructure. NDR offers deep visibility into network traffic, using machine learning and behavioral analysis for effective threat detection. While XDR on the other hand, provides a broader view, integrating multiple data sources for comprehensive incident response. Both solutions have their strengths and weaknesses, and organizations must carefully evaluate their options to select the best fit.


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