Network Detection and Response: Capabilities & Alternatives

What Is Network Detection and Response (NDR)?

Network detection and response (NDR) refers to cybersecurity solutions focused on detecting and responding to threats within network infrastructure. Unlike traditional methods, NDR systems analyze network traffic and metadata to identify unusual patterns indicative of potential security incidents. 

As cyber threats grow increasingly sophisticated, NDR serves as a layer of defense by offering visibility and context that endpoint and perimeter-level controls might miss. This approach allows organizations to manage threats that evade simpler detection mechanisms.

NDR solutions operate by continuously monitoring network traffic, utilizing artificial intelligence and machine learning to spot anomalies. These technologies enable rapid threat identification and provide valuable insights into attack strategies. By focusing on network flows and behavior, NDR complements other defensive technologies.

This is part of an extensive series of guides about information security.

The Importance and Benefits of Network Detection and Response 

Early Detection for Rapid Attack Mitigation

Early detection of threats through NDR is crucial for effective attack mitigation. By analyzing network activity in real-time, NDR solutions can identify potential threats before they evolve into significant breaches. This proactive detection is achieved through algorithms that discern irregularities in data flow and communication patterns, which might indicate malicious activity. Early identification allows security teams to intervene swiftly, reducing the potential damage and costs associated with cyber incidents.

Out-of-the-Box Detection Without Extensive Tuning

One of the significant advantages of NDR solutions is their efficacy right out-of-the-box, providing strong detection capabilities without extensive customization. Many traditional security measures require substantial tuning to adapt to the unique characteristics of a particular network, which can be resource-intensive. In contrast, NDR tools are often pre-configured with intelligent detection algorithms capable of identifying known and unknown threats with minimal initial setup.

Using Broad Analytical Data Inputs to Increase Accuracy

Broad analytical data inputs are a cornerstone of NDR systems, enhancing their threat detection accuracy. NDR solutions gather a wide range of network data, including traffic flows, connection logs, and even packet contents, to build behavioral models. The diversity and depth of data collected allow these systems to detect subtle anomalies that might escape notice with less extensive monitoring. This data-centric approach enables detection of sophisticated threats, including zero-day exploits.

Tips from the expert

Steve Moore is Vice President and Chief Security Strategist at Exabeam, helping drive solutions for threat detection and advising customers on security programs and breach response. He is the host of the “The New CISO Podcast,” a Forbes Tech Council member, and Co-founder of TEN18 at Exabeam.

In my experience, here are tips that can help you better leverage Network Detection and Response (NDR) for enhanced cybersecurity:

Leverage East-West traffic monitoring: While many solutions focus on North-South traffic, monitoring internal (East-West) network traffic is critical for detecting lateral movement by attackers once they bypass perimeter defenses. Make sure your NDR system has strong capabilities for East-West analysis.

Integrate NDR with threat hunting frameworks: NDRs are excellent for surfacing anomalies, but they shine when integrated with proactive threat hunting frameworks like MITRE ATT&CK. This allows analysts to proactively investigate the network for undetected threats and understand tactics and techniques used by attackers.

Use NDR to identify IoT and shadow IT devices: Many security tools struggle with visibility into IoT or rogue devices. NDR systems that monitor abnormal communication patterns can help you detect unknown or unmanaged devices communicating on your network.

Develop custom baselines: Rather than relying solely on out-of-the-box baselines, build custom profiles for your network traffic over time. Tailor baselines to your organization’s unique operational patterns to reduce false positives and catch more subtle deviations.

Automate threat response using playbooks: Integrate NDR alerts with security orchestration and response (SOAR) platforms to automate common response actions. For example, automatically isolate compromised devices or block malicious IPs based on predefined playbooks.

How Does NDR Work? 

1. Cyber Incident Detection

NDR systems detect cyber incidents through continuous monitoring and analysis of network activities. Utilizing machine learning models, these systems identify irregularities and deviations from normal behavior that may signal attack vectors. By leveraging both known threat signatures and anomaly detection algorithms, NDR uncovers threats such as malware, lateral movements, and data exfiltration attempts. The integration of contextual data reinforces its ability to discern genuine threats from benign anomalies.

Detection mechanisms in NDR solutions prioritize real-time assessment and reporting to security teams. This immediacy is critical in managing emerging threats, providing teams with the necessary intelligence to initiate a response. A well-calibrated NDR tool integrates with existing security architectures, forwarding alerts to security information and event management (SIEM) systems for further analysis.

2. Investigation

Once a potential threat is detected, a thorough investigation is critical to understand its scope and impact. NDR systems facilitate in-depth analysis by providing comprehensive logs and correlated event timelines. Security analysts can use this information to trace the threat’s origin, identify affected systems, and understand the attack methods. The tools often include visualization features that simplify the exploration of complex incidents, making it easier to identify vulnerabilities and entry points within the network.

Effective investigation allows organizations to learn from incidents, improving future identification and response capabilities. The data collected during investigations provide insights for strengthening defenses, such as applying patches, tightening security controls, or adjusting detection algorithms.

3. Threat Intelligence Management

Threat intelligence management is a vital component of NDR systems, serving to collect, store, and analyze threat intelligence. This capability enables organizations to maintain an updated knowledge base of known threats and attack strategies. NDR tools often integrate threat feeds from various sources, combining external intelligence with internal network data to optimize detection and response. The fusion of threat intelligence and network monitoring is crucial for understanding the threat landscape and anticipating future attacks.

The integration of threat intelligence management in NDR solutions supports predictive threat modeling and strategic decision-making. By analyzing historical data and current trends, organizations can prioritize risks and allocate resources effectively. Furthermore, real-time updates on emerging threats and vulnerabilities enable security teams to respond proactively, adjusting security measures as needed.

4. Feed Creation

Feed creation in NDR involves generating actionable threat data that can be integrated with broader security ecosystems. This process includes the synthesis of identified threats, anomalies, and network insights into structured data streams. These feeds inform other security tools, such as SIEMs or endpoint protection systems, helping create comprehensive defense strategies. By sharing threat intelligence, feed creation supports enhanced situational awareness across the security infrastructure.

NDR-generated feeds are essential for coordinated security operations. They allow for distributed threat intelligence, ensuring each layer of security can benefit from network-wide insights. By maintaining regular updates, organizations ensure that their defenses adapt quickly to the latest threat patterns.

5. Threat Prevention

Threat prevention through NDR relies on preemptive identification and neutralization of potential attacks before they cause harm. NDR systems are equipped with analytical capabilities to detect precursors to cyber threats, such as suspicious activities or network behaviors. By identifying these early warning signs, NDR facilitates timely intervention, enabling mitigation strategies like isolating affected systems or blocking malicious traffic to be enacted swiftly.

Preventive measures are enhanced through the automation of routine threat-handling tasks, allowing security personnel to concentrate on more strategic concerns. NDR’s ability to predict and prevent attacks reduces the risk of data breaches and system disruptions considerably.

Network Detection and Response vs. Similar Solutions 

NDR vs. EDR

Network detection and response (NDR) differs from endpoint detection and response (EDR) primarily in their focus areas. While NDR concentrates on network-level activities and traffic patterns to identify threats, EDR is dedicated to monitoring and responding to threats at the endpoint level, such as user devices and servers. NDR analyzes data across the entire network infrastructure, providing a broad perspective on potential threats, whereas EDR dives deep into individual endpoints for detailed threat detection.

The integration of NDR and EDR can significantly strengthen an organization’s defensive capabilities. By combining the holistic network visibility of NDR with the in-depth endpoint analysis of EDR, organizations can achieve comprehensive threat coverage.

NDR vs. MDR

Managed detection and response (MDR) services are distinguished from NDR by their operational model. MDR provides outsourced cybersecurity services, combining technology with expert analysis to detect threats across networks and endpoints. In contrast, NDR focuses strictly on network traffic and behaviors without the managed service component. MDR’s strength lies in its ability to offer companies access to specialized security expertise along with detection technologies, which can be advantageous for organizations lacking in-house cybersecurity resources.

Choosing between NDR and MDR depends on an organization’s specific requirements and existing capabilities. While NDR offers autonomy and network-focused threat detection, MDR provides a turnkey solution inclusive of threat assessment and response.

NDR vs. XDR

Extended detection and response (XDR) extends beyond NDR by integrating multiple security products into a unified platform, encompassing network, endpoint, and application data. XDR provides a holistic view of the security landscape, optimizing threat detection and response strategies across diverse IT environments. NDR, while robust in network traffic analysis, operates within its specified domain without integrating endpoint or application data unless explicitly configured to do so.

The decision to implement NDR or XDR should consider the organization’s scope and complexity of the cybersecurity environment. XDR suits organizations seeking an all-encompassing security solution that simplifies threat management by combining various data streams. In contrast, NDR is ideal for entities prioritizing network-specific insights and possessing existing solutions for endpoint and application security.

Potential Drawbacks of NDR 

Complexity and Cost

Implementing NDR solutions can entail significant complexity and cost, as these systems require setup and ongoing management. The nature of NDR technologies means that organizations must often invest in specialized tools and staff training. 

The initial cost outlay, combined with ongoing operational expenses, may pose challenges for smaller organizations with limited budgets or technical resources. Additionally, sophisticated systems can increase the workload on IT teams, potentially diverting attention from other critical IT tasks.

Scalability Issues

Scalability is a concern for organizations seeking to expand their NDR deployments alongside growing network infrastructures. As network traffic and complexity increase, the NDR solution must accommodate additional data volume without sacrificing performance or detection accuracy. The hardware and software resources necessary to support this scalability can become burdensome, requiring upgrades that incur further expenses and potential disruptions to existing security operations.

Addressing scalability issues involves selecting an NDR solution with flexible architecture capable of adapting to network growth. Solutions that leverage cloud technology or distributed processing can offer more scalable options, enabling seamless scaling without extensive infrastructure changes.

False Positives

False positives represent a persistent issue in many NDR systems, where benign network activities are incorrectly flagged as threats. This can lead to unnecessary alarm and resource allocation, with security teams spending valuable time investigating non-malicious events. Frequent false positives may desensitize response teams, potentially leading to delayed reactions to genuine threats.

Efforts to reduce false positives include refining detection algorithms and integrating machine learning models that improve accuracy over time. Regularly updating threat intelligence databases and implementing continuous learning processes are also effective strategies.

What to Look for in a Network Detection and Response Solution 

Contextual Visibility

Contextual visibility is a crucial feature of effective NDR solutions, providing insights into network activity and threat contexts. Solutions that offer deep visibility into network traffic patterns help security teams understand the broader environment associated with detected threats. This includes identifying the source, target, and potential impact of malicious activities, thereby facilitating more informed decision-making and response strategies.

In addition to offering insights into specific threats, contextual visibility supports proactive threat hunting by enabling teams to discern emerging patterns and vulnerabilities. This broader understanding aids in identifying security gaps and fine-tuning defense mechanisms.

Non-Signature-Based Threat Detection

Non-signature-based threat detection is critical for identifying new, unknown threats that traditional signature-based methods might miss. NDR solutions employing anomaly detection and behavioral analytics can recognize deviations in normal network activity, offering a proactive approach to threat detection. These methods are crucial in defending against zero-day attacks, where threat signatures have not yet been identified or documented in threat intelligence databases.

By focusing on behavior rather than signatures, NDR tools adapt to evolving threat landscapes, maintaining efficacy as attackers develop new techniques.

Effective Threat Identification and Alerting

Effective threat identification and alerting are central to the success of NDR solutions. Systems should provide clear, actionable alerts that detail the nature and severity of identified threats, enabling quick prioritization and response. Clear alerts allow security teams to accurately assess threat levels, thereby allocating resources to critical incidents while reducing time spent on irrelevant alerts.

Automated alerting also helps ensure timely responses to threats, often integrating with existing IT and security workflows to streamline incident management. An ideal NDR solution encapsulates this functionality while allowing for customization of alert thresholds and conditions, aligning with an organization’s unique risk profile and operational needs.

Robust Threat Detection Engines

The core of NDR’s threat detection capabilities lies within its engines, which employ various methodologies to identify threats. Detection engines utilize machine learning, pattern matching, and anomaly detection to uncover threats within network traffic. The effectiveness and efficiency of these engines significantly influence an organization’s ability to detect and respond to both known and unknown threats. Therefore, consideration of the technological robustness of these engines is vital when selecting an NDR tool.

Integrated threat detection engines should support continuous learning and adaptation, allowing them to refine their accuracy over time. This refinement is essential for minimizing false positives and negatives, ensuring security teams receive reliable threat intelligence.

Easy Management and Reporting

Ease of management and robust reporting are essential characteristics of NDR solutions that impact their operational effectiveness. Solutions that offer intuitive interfaces and simplified management features enable security teams to focus on strategic activities rather than operational challenges. User-friendly dashboards and automation tools help streamline daily operations, while reducing the complexity associated with ongoing system maintenance and updates.

Comprehensive reporting capabilities are equally important, providing insights necessary for ongoing security assessments and compliance audits. Detailed reports on detected threats, system performance, and incident responses allow organizations to evaluate their security status over time.

Exabeam Platform Capabilities: SIEM, UEBA, SOAR, Insider Threats, Compliance, TDIR

The Exabeam Security Operations Platform applies AI and automation to security operations workflows for a holistic approach to combating cyberthreats, delivering the most effective threat detection, investigation, and response (TDIR): 

• AI-driven detections pinpoint high-risk threats by learning normal behavior of users and entities, and prioritizing threats with context-aware risk scoring. 
• Automated investigations simplify security operations, correlating disparate data to create threat timelines. 
• Playbooks document workflows and standardize activity to speed investigation and response. 
• Visualizations map coverage against the most strategic outcomes and frameworks to close data and detection gaps. 

With these capabilities, Exabeam empowers security operations teams to achieve faster, more accurate, and consistent TDIR.

See Additional Guides on Key Information Security Topics

Together with our content partners, we have authored in-depth guides on several other topics that can also be useful as you explore the world of information security.

Insider Threat

Authored by Exabeam

• [Guide] What is an Insider Threat? 4 Defensive Strategies 
• [Guide] Insider Threat Examples: 3 Famous Cases and 4 Preventive Measures 
• [Whitepaper] 2024 State of the Security Team Research 
• [Product] Exabeam | AI-Driven Security Operations

Mitre ATT&CK-EX

Authored by Exabeam

• [Guide] What is MITRE ATT&CK^®: An Explainer
• [Guide] MITRE ATT&CK Navigator: Use Cases, Layers, and How to Get Started
• [Blog] Cybersecurity Threats: Everything you Need to Know 
• [Product] Exabeam | AI-Driven Security Operations

DDoS Protection

Authored by Radware

• [Guide] Anti-DDoS: 6 Techniques, Solution Types & 8 Key Considerations 
• [Guide] DDoS Protection: Techniques, Types & 7 Solutions to Know in 2024 
• [Product] Radware DDoS Attack Mitigation and Defense