What is network detection and response (NDR)?

Capabilities of network detection and response solutions include threat hunting, forensic analysis, malware detection, and automated incident response. Organizations can implement NDR solutions through cloud services, local hardware appliances, or a blend of techniques.

All types of NDR solutions enable defense-in-depth for high-value assets. They provide critical components of security operations centers (SOCs), safeguarding data and applications when robust security is vital.

How does NDR work?

NDR represents an evolution of traditional signature-based threat detection solutions. From the 2010s onward, companies started to encounter advanced threats that bypassed standard network security tools. NDR provides a solution to this problem by leveraging behavioral analytics to expose hidden threats.

Network traffic monitoring tools start by establishing baselines. These baselines represent the normal network behavior expected by authorized users or applications. Baselines identify deviations from the norm, evidence of malicious activity such as malware or insider threats.

Sensors located on network nodes monitor network traffic in real-time. These sensors capture network metadata passively. There should be minimal impact on devices or the user experience.

Centralized analytics tools monitor data flows and user requests against baselines. Meanwhile, machine learning and artificial intelligence allow NDR solutions to "learn" the difference between normal and malicious activity. The use of ML and AI sharpens the accuracy of threat detection and cuts the incidence of false positives.

Note: NDR solutions also leverage signature-based detection to identify known viruses or malware types. But it does more than match signatures and traffic.

Threat detection flows into incident response processes. NDR tools raise alerts when incidents cross risk thresholds. Users can calibrate automation functions to block threats (for example, terminating a suspicious user connection). Security incidents can also pass to the triage stage.

Data from network detection and response tools enables analysts to make informed security decisions. Integrations with endpoint detection and response (EDR), extended detection (XDR), and threat intelligence feeds enhance responses, providing comprehensive visibility for security professionals.

Why is NDR important?

Network detection and response matter because network traffic is a common conduit for malicious threats. Security teams can only detect these threats based on the imprint they leave when entering and navigating network resources. NDR solutions provide this capability, which older security tools lack.

NDR also fits into the workflows of time-poor and pressurized security workforces. Security professionals need to minimize false positives while ensuring the detection of genuine network threats. This is particularly challenging in contexts where the attack surface changes daily, as is the case in many modern business networks.

NDR identifies compromised devices instantly, detecting anomalous file transfers or the use of malicious IP addresses. Automated device isolation and traffic blocking contain the threat, facilitating triage and further mitigation actions.

NDR solutions also complement the work of efficient security professionals, enabling them to work more effectively. Analysts can use time saved through automation and the elimination of false positives to hunt threats and gather intelligence.

Benefits of network detection and response

Implementing network detection and response delivers many benefits (although there are challenges to consider, as discussed below). Common advantages of adding NDR solutions include:

• Improved threat visibility: NDR monitors network activity in granular detail, recording every action an attacker takes. Security teams can monitor and log every stage, from network scans and initial compromises, through to data access.
• Integrated responses: NDR helps orchestrate incident response procedures. Network monitoring combines with EDR/XDR and SIEM logging to track every aspect of network security. Detection and response target every relevant asset to block attacks at the source.
• Advanced threat detection: Artificial intelligence and machine learning analyze network activity, comparing suspicious actions to acceptable baselines. ML and AI adapt as networks expand, interpreting new user actions and refining threat detection techniques.
• High-quality alerts: Security teams can become overwhelmed by the volume of alerts. NDR side-steps this problem by discarding false positives, automating simple mitigation actions, and forwarding high-risk alerts to triage teams.
• Multi-dimensional threat analysis: NDR tools access a diverse range of information about network traffic, resulting in more accurate outputs. For instance, NDR solutions can monitor files accessed, credential requests, email contacts, device profiles, access times, and geo-locations. Security tools learn more about attackers and regular users, making it easier to identify threats.
• Rapid threat detection and response: NDR generally cuts the mean-time-to-respond (MTTR) by identifying threats before attacks occur. Dynamic behavioral analytics reduce dwell time and may even block persistent threats within minutes of a network compromise.
• Efficient performance via cloud delivery: Cloud-hosted network detection and response solutions reduce storage overheads by cutting reliance on locally-maintained log servers.
• In-depth threat hunting: NDR tools enable analysts to dive deep into attacker techniques and motivations. Security teams can combine local security data with external threat intelligence. This helps uncover active threat actors before they seed ransomware or launch DDoS attacks.

Key components of NDR

Effective NDR solutions must include several core components. These critical elements include:

• Detecting advanced threats: Behavioral analytics and ML look for patterns of network behavior. Traditional signature-based scanning picks up malware and viruses based on the latest intelligence data.
• Automated responses: NDR solutions implement automated mitigation actions to secure compromised accounts and isolate infected devices. Responses occur in real-time, without a need for manual approval.
• All-round network monitoring: NDR tools cover all traffic flows within the network. Centralized command consoles provide comprehensive visibility, enhancing the organization's security posture.
• Adaptive coverage: NDR scales automatically as user communities and network architecture change. Monitoring tools track the evolving threat surface, ensuring there are no network security blind spots.
• Integrations: Crucially, NDR solutions should integrate with other security tools. Integration with SOAR, SIEM, XDR, and EDR tools multiplies the benefits of traffic analysis.

Network detection and response challenges

Despite its many benefits, implementing and using NDR solutions brings challenges. Companies should understand the most common bottlenecks and problems before choosing a network traffic-based security platform.

Handling network complexity

To be effective, network detection and response must apply to all network traffic. However, real-life networks are complex and liable to change.

Shadow IT, device sprawl, and remote working practices complicate matters. Security teams must configure NDR carefully. Auditing coverage regularly is essential.

Ensuring seamless integrations

Integrations make NDR more effective, but can also bring problems. Security solutions may be incompatible or use different data formats. Network users may employ chaotic scanning tools or firewall settings, resulting in patchy data delivery. Planning integrations is a vital step before adopting NDR solutions.

Over-reliance on machine learning

ML is a powerful tool that monitors and detects many hidden threats. However, security teams also need the expertise to analyze attack techniques and manage security incidents. The best solutions combine in-person triage and management with advanced AI/ML analysis.

Privacy issues

Data collection must be comprehensive, but it can also be invasive. Security teams must gain consent to collect data from network users, including employees and, potentially, customers. Compliance expertise should guide teams to balance privacy and network security.

NDR vs other security solutions

NDR is one of many cybersecurity solutions, and organizations rarely use it in isolation. Knowing how network detection fits into the cybersecurity landscape is essential. It may not be the best fit for your systems, and other tools may be needed to enhance NDR's effectiveness.

NDR vs endpoint detection and response (EDR)

EDR places monitoring agents on all network endpoints. Scanning tools monitor internet-facing workstations, routers, servers, and remote work devices such as laptops or tablets. Agents look for suspicious traffic patterns and behavior, adding another layer of protection at the network edge.

EDR detects threats at the network edge, such as brute force attacks, faulty processes, or configuration changes on endpoints. NDR solutions monitor internal network traffic such as data exfiltration or lateral movement within network assets.

NDR vs extended detection and response (XDR)

As the name suggests, XDR extends the capabilities of EDR and NDR. XDR monitors endpoint data, network traffic, and cloud deployments in real-time. It provides all-around coverage of network activity, applying advanced behavioral analytics and ML/AI to detect advanced threats.

XDR generally delivers greater visibility of potential threats. It consolidates the capabilities of EDR and NDR, streamlining triage and incident response processes.

NDR vs managed detection and response (MDR)

MDR solutions outsource network and endpoint monitoring tasks to external providers. Third parties "manage" the detection process, often via advanced XDR tools. Clients can outsource mitigation actions to save costs and time, or manage incident response internally. The choice depends on internal skill levels, budgets, and the need to maintain control over security incidents.

How to implement NDR on your network

When implementing network detection and response, organizations should keep the following best practices in mind:

• Understand your objectives: Setting clear objectives lets you plan implementation milestones and measure the performance of NDR solutions. Match NDR implementations with your security goals. For example, protecting health information or preventing password attacks on financial databases.
• Ensure NDR covers critical locations: Install network sensors at strategic locations. For instance, network traffic bottlenecks, interfaces with endpoints, or gateways to cloud services. Plan NDR architecture to capture relevant data about critical assets. You don't need to measure every activity.
• Establish accurate baselines: Behavioral analytics depend on accurate user and application baselines. Take care to calibrate NDR tools to capture benchmarks that reflect actual network functions.
• Manage integrations to enhance security: Integrations with SIEM, EDR, and, if possible, XDR enhance the power of network detection and response. Utilize layered tools to capture security data at the network edge and within the network itself.
• Use automation to focus on critical threats: Automation can handle simple security actions, including password resets or lockouts. Configure NDR to deliver alerts about high-risk threats.
• Update rules to keep pace with attackers: Cyberattack techniques evolve. Don't be caught out. Audit your NDR solution to ensure it leverages up-to-date threat intelligence. If necessary, test monitoring tools against contemporary threat styles to ensure robust protection.

Detect advanced threats with network detection and response

Network detection and response (NDR) uses behavioral analytics and agent-based monitoring to detect and block network threats. NDR captures complex evidence needed to detect advanced threats that evade traditional security measures.

NDR allows companies to strengthen their security posture by monitoring network traffic. However, NDR alone is not necessarily sufficient. To achieve multi-layered security, organizations may need to combine network behavior monitoring with endpoint detection tools and threat intelligence.