Network Detection and Response What is network detection and response (NDR)?
What is network detection and response (NDR)?
Network Detection and Response (NDR) is technology that helps organizations monitor, detect, and respond to suspicious activity on their network. It works by analyzing traffic in real-time to identify potential threats, such as zero-day attacks, data exfiltration, and other unusual device or network activity. NDR plays a crucial role within a robust cybersecurity strategy by detecting threats that can evade other defenses.
- Network detection and response explained
- What are the benefits of NDR?
- What kinds of threats can NDR detect?
- What are the components of NDR?
- What to consider when evaluating NDR solutions?
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Network detection and response explained
Network detection and response (NDR) monitors network traffic to detect and respond to threats. NDR uses techniques like deep packet inspection, flow analysis, signature recognition, and behavioral analytics to identify threat patterns and anomalies. When a threat is detected, NDR can enable rapid response and alerting throughout the security ecosystem. AI and ML can enhance the accuracy of threat detection and the effectiveness of response.
What are the benefits of NDR?
NDR solutions protect the network and can provide risk mitigation and compliance benefits as part of an overall cybersecurity strategy.
- Reduced risk: With the help of machine learning, artificial intelligence, and behavioral analysis, NDR can detect unknown or emerging threats by identifying anomalies in traffic patterns or network behavior like unusual communication patterns and data transfers, complementing signature-based detection.
- Robust protection for complex environments: As organizations move to the cloud, NDR helps maintain visibility and security by monitoring traffic in cloud and hybrid infrastructures, which are often harder to secure using traditional tools alone.
- Stronger IoT and OT security: NDR is essential for securing IoT and operational technology (OT) environments, where traditional endpoint security may not be effective. These devices often lack built-in security controls, making network-based monitoring a key line of defense.
- Strategic automation: During investigation and decision cycles, manual processes can slow down response times. The best NDR solutions automate detection, data collection, and protective recommendations, so humans can make appropriate decisions while avoiding operational disruptions.
- Faster response times: NDR provides critical insights into network events, allowing security teams to investigate and respond more quickly to incidents. This can help prevent attackers from moving deeper into the network or accessing sensitive data.
- Enhanced compliance: Many regulations, such as GDPR, HIPAA, and PCI DSS, require organizations to monitor and secure their network traffic. NDR helps meet these requirements by providing detailed monitoring, logging, and reporting capabilities.
What kinds of threats can NDR detect?
Network detection and response (NDR) solutions can detect a wide range of cybersecurity threats using specialized techniques tailored to each threat. A few types of threats that NDR can help detect and protect against include:
- Malware infections: Signature detection and behavioral analysis can spot known malicious code and suspicious communications with command-and-control servers.
- Ransomware: Attacks can be detected via anomaly detection and behavioral analytics by identifying unusual patterns such as abnormal outbound traffic or unauthorized data transfers, or unusual file encryption activity.
- Phishing-based intrusions: NDR can use policy violation monitoring to flag unexpected connections or data flows post-compromise.
- DDoS attacks: NDR use traffic analysis and anomaly detection to reveal abnormal traffic spikes meant to overwhelm resources.
What are the components of NDR?
- Sensors and agents: Sensors are passive devices that monitor and capture traffic as it flows across the network, analyzing it for threats without directly interacting with endpoints. Agents are active components installed on individual devices or endpoints, providing deeper visibility into the activities and behavior of those specific devices. Sensors and agents are placed throughout the network to capture and analyze traffic data in real-time. By monitoring traffic patterns, sensors help detect suspicious activity that could indicate a possible threat.
- Network telemetry: Data that is collected and analyzed to monitor network performance, health, and behavior is called telemetry. Elements of network telemetry can include: Destination IP, Port, Protocol, application traffic, client information, SSID, session information, location, role, and UserID.
- Threat intelligence: NDR systems can detect known known threats more effectively by using external data such as malware signatures or IP addresses linked to cybercriminal activity.
- Behavioral analysis: Advanced NDR systems can identify threats using behavioral analysis, which doesn’t just look for known threats or attack signatures, but also monitors for abnormal patterns of behavior. For example, if a device suddenly starts communicating with an unfamiliar IP address or transfers large amounts of data, that could be flagged as suspicious.
- AI and ML: AI and machine learning can improve detection and response capabilities over time. The system learns from the traffic it monitors and the network environment, potentially becoming more accurate in identifying suspicious activity and recommending appropriate responses.
What to consider when evaluating NDR solutions?
Not all NDR solutions are created equal. Consider the following when evaluating an NDR solution.
- Does the NDR solution use AI and ML to enhance detection and response? Cybersecurity threats are always evolving and NDR techniques relying solely on known patterns, such as signature detection, can fall behind, leaving the organization vulnerable. AI-based approaches can help NDR solutions learn from both the organization’s environment as well as other environments, to aid in detecting novel and emerging threats. AI can also be used to assist security teams in defending against attacks more swiftly and effectively by providing response recommendations and more accurate impact assessments.
- Is the AI backed by robust data? AI is only as good as the data behind it. Ensure that the AI and ML techniques used by the NDR solution are backed by data with strong variety, volume, and velocity.
- Does the NDR solution integrate well with other solutions? Cybersecurity is best thought of as an ecosystem, not an isolated island. Threat information and alerts from the NDR solution that can be easily disseminated and consumed by other systems to take protective actions can help organizations achieve a strong security posture and comprehensive defense.
