Network detection and response: Difference between revisions

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{{short description|Threat monitoring technology}}

'''Network detection and response (NDR)''' refers to a category of [[network security]] products that detect abnormal system [[Behavior|behaviors]] by continuously analyzing network traffic. NDR solutions apply [[behavioral analytics]] to inspect raw [[Packet analyzer|network packets]] and [[metadata]] for both internal (east-west) and external (north-south) network communications.<ref name=":0">{{Cite web |last=Jonathan Nunez, Andrew Davies |date=20 July 2023 |title=Hype Cycle for Security Operations, 2023 |url=https://www.gartner.com/doc/reprints?id=1-2EIN4TVS&ct=230721&st=sb&__hstc=45788219.57a590308de95e51d2f62b49fac710ef.1691078838873.1691078838873.1691078838873.1&__hssc=45788219.1.1691078838873&__hsfp=3812163218&hsCtaTracking=51e2e4ef-078c-41c0-a8bf-673c4e38176a%7C2cb608e8-3bb2-41fb-96a8-b3d410ee1978 |access-date=2023-08-08 |website=www.gartner.com}}</ref>

'''Network detection and response (NDR)'''<ref>{{Cite web |title=What is network detection and response (NDR)? |url=https://www.sophos.com/en-us/cybersecurity-explained/network-detection-and-response |website=Sophos |access-date=2025-08-29}}</ref>

'''Network detection and response (NDR)''' refers to a category of [[network security]] products that detect abnormal system [[Behavior|behaviors]] by continuously analyzing [[network traffic]]. NDR solutions apply [[behavioral analytics]] to inspect raw [[Packet analyzer|network packets]] and [[metadata]] for both internal (east-west) and external (north-south) network communications.<ref name=":0">{{Cite web |last=Jonathan Nunez, Andrew Davies |date=20 July 2023 |title=HypeNetwork CycleDetection for Security Operations,and 2023Response |url=https://www.gartner.com/docreviews/reprints?id=1market/network-2EIN4TVS&ct=230721&st=sb&__hstc=45788219.57a590308de95e51d2f62b49fac710ef.1691078838873.1691078838873.1691078838873.1&__hssc=45788219.1.1691078838873&__hsfp=3812163218&hsCtaTracking=51e2e4efdetection-078cand-41c0-a8bf-673c4e38176a%7C2cb608e8-3bb2-41fb-96a8-b3d410ee1978response |website=Gartner |access-date=20232025-08-08 |website=www.gartner.com29}}</ref>

== Description ==

NDR is delivered through a combination of [[Computer hardware|hardware]] and [[software]] sensors, along with a software or [[Software as a service|SaaS]] management console. Organizations use NDR to detect and contain malicious post-breach activity, such as [[ransomware]], asor wellinsider as insidermalicious attacksactivity. NDR focuses on identifying abnormal behavior patterns and anomalies rather than relying solely on [[Signature based detection|signature-based threat detection]]. This allows NDR to spot weak signals and unknown threats from network traffic, like [[Network Lateral Movement|lateral movement]] or [[data exfiltration]].<ref name=":0" />

NDR provides visibility into network activities to identify anomalies using [[machine learning]] algorithms.<ref name=":3">{{Cite web |last=Maor |first=Etay |title=Council Post: EDR, XDR, MDR: Making Sense Of Threat Detection And Response Acronyms |url=https://www.forbes.com/sites/forbestechcouncil/2024/03/05/edr-xdr-mdr-making-sense-of-threat-detection-and-response-acronyms/ |access-date=2024-05-21 |website=Forbes |language=en}}</ref> The automated response capabilities can help reduce the workload for security teams. NDR also assists incident responders with threat hunting by supplying context and analysis.<ref name=":0" />

Deployment options include physical or virtual sensors. Sensors are typically out-of-band, positioned to monitor network flows without impacting performance. Cloud-based NDR options integrate with IaaS providers to gain visibility across hybrid environments. Ongoing tuning helps reduce false positives. NDR competes against broader platforms like [[SIEM]] and [[Extended detection and response|XDR]] for security budgets.<ref name=":0" /> NDR can be used to complement EDR's blind spot.<ref name=":3" /><ref>{{Cite web |title=Change Is Coming to the Network Detection and Response (NDR) Market |url=https://www.darkreading.com/cyber-risk/change-is-coming-to-the-network-detection-and-response-ndr-market |access-date=2024-05-21 |website=www.darkreading.com |language=en}}</ref>

Key capabilities offered by NDR solutions include: Realreal-time threat detection through continuous monitoring, Rapidrapid incident response workflows to minimize damage, Reducedreduced complexity versus managing multiple point solutions, Improvedimproved visibility for compliance and risk management, Automatedautomated detection and response, Endpointendpoint and user behavior analytics, Integrationand integration with [[SIEM]] for centralized monitoring.<ref name=":2" />

==History==

The origins of NDR trace back to [[network traffic analysis]] (NTA) solutions that emerged around 2019. NTA provided greater visibility into network activities to quickly identify and respond to potential threats.<ref name=":2">{{Cite web |last=Wiens |first=Christian |date=2023-02-02 |title=A Comprehensive Guide to Network Detection & Response (NDR) — What CIOs & Security Analysts Should Know |url=https://securityboulevard.com/2023/02/a-comprehensive-guide-to-network-detection-response-ndr-what-cios-security-analysts-should-know/ |access-date=2023-08-15 |website=Security Boulevard |language=en-US}}</ref>

By 2020, NTA adoption was growing for real-time threat detection. That year, a study found that 87% of organizations used NTA, with 43% considering it a "first line of defense.". The NTA market was valued at US$2.9 billion in 2022, and expected to reach US$8.5 billion by 2032. NTA evolved into NDR as a distinct product category. NDR combined detection capabilities with incident response workflows. This enabled detecting and reacting to threats across networks in real- time.<ref name=":2" />

Major attacks like [[WannaCry]] in 2017 and the [[SolarWinds]] breach in 2020 highlighted the need for solutions like NDR. Traditional perimeter defenses and signature-based tools proved insufficient against modern threats.<ref name=":2" />

== AIArtificial Intelligence applications ==

The use of [[artificial intelligence]] in NDR tools is growing, as security teams explore AI's potential to enhance NDR capabilities. Key AI use cases for NDR include:<ref name=":1">{{Cite web |last=Grady |first=John |title=How AI benefits network detection and response |url=https://www.techtarget.com/searchsecurity/opinion/How-AI-benefits-network-detection-and-response |access-date=2023-08-15 |website=TechTarget |language=en}}</ref>

* Improved threat detection : AI can analyze large volumes of data on vulnerabilities, threats, and attack tactics to identify anomalous network activities. This allows NDR to detect emerging attack patterns with greater accuracy and fewer [[False positives and false negatives|false positives]].<ref name=":1" />

* Alert prioritization : AI models can evaluate the criticality of NDR alerts based on factors like affected assets, exploitability, and potential impact. This enables security teams to triage alerts effectively despite staff shortages.<ref name=":1" />

* Analyst workflow optimization : AI assistants can provide guidance toguide analysts during incident response, suggesting relevant investigation steps based on details of the threat. This amplifies analyst efficiency, especially for junior staff lacking specialized expertise.<ref name=":1" />

* Automated response : Although not yet widely adopted, AI could enable NDR platforms to autonomously execute containment measures like quarantining endpoints. AI would identify and recommend response actions for analyst approval.<ref name=":1" />

* Security team communications : NDR vendors are exploring integrations with natural language AI to generate incident reports and metrics digestible for business leaders, not just technical security staff.<ref name=":1" />

== NDR Vendors ==

According to [[Gartner]], NDR vendors include [[Cisco]], Corelight, [[Darktrace]], [https://www.linkshadow.com/ LinkShadow] [[ExtraHop Networks|ExtraHop]], [[Fortinet]], IronNet, MixMode, Plixer, [[Trend Micro]], Trellix, [[Vectra AI]].<ref name=":0" />

== References ==