Network behavior anomaly detection (NBAD) is a security technique that provides network security threat detection. It is a complementary technology to systems that detect security threats based on packet signatures.[1]
NBAD is the continuous monitoring of a network for unusual events or trends. NBAD is an integral part of network behavior analysis (NBA), which offers security in addition to that provided by traditional anti-threat applications such as firewalls, intrusion detection systems, antivirus software and spyware-detection software.
Most security monitoring systems utilize a signature-based approach to detect threats. They generally monitor packets on the network and look for patterns in the packets which match their database of signatures representing pre-identified known security threats. NBAD-based systems are particularly helpful in detecting security threat vectors in two instances where signature-based systems cannot: (i) new zero-day attacks, and (ii) when the threat traffic is encrypted such as the command and control channel for certain Botnets.
An NBAD program tracks critical network characteristics in real time and generates an alarm if a strange event or trend is detected that could indicate the presence of a threat. Large-scale examples of such characteristics include traffic volume, bandwidth use and protocol use.
NBAD solutions can also monitor the behavior of individual network subscribers. In order for NBAD to be optimally effective, a baseline of normal network or user behavior must be established over a period of time. Once certain parameters have been defined as normal, any departure from one or more of them is flagged as anomalous.
NBAD technology/techniques are applied in a number of network and security monitoring domains including: (i) Log analysis (ii) Packet inspection systems (iii) Flow monitoring systems and (iv) Route analytics.
NBAD has also been described as outlier detection, novelty detection, deviation detection and exception mining.[2]
^Hein, Daniel (2019-05-15). "Network Behavior Analysis and Anomaly Detection: The Basics". Best Network Monitoring Vendors, Software, Tools and Performance Solutions. Retrieved 2022-06-27.
^Ahmed, Mohiuddin (2016). "A survey of network anomaly detection techniques" (PDF). Journal of Network and Computer Applications. 60: 19–31. doi:10.1016/j.jnca.2015.11.016 – via Elsevier.
^"Palo Alto Networks Cortex XDR 3.0 automates threat detection and investigation across cloud environments". Help Net Security. 2021-08-24. Retrieved 2022-08-12.
^Daws, Ryan (2022-03-10). "Darktrace adds 70 ML models to its AI cybersecurity platform". AI News. Retrieved 2022-08-12.
^"DDoS Security & Protection Software: Secure Your Network".
^"How to block online threats and ransomware attacks with Cisco Stealthwatch". Business Review (in Romanian). 2019-01-23. Retrieved 2022-08-24.
^Heath, Thomas (2012-09-23). "Tenable enters partnership with In-Q-Tel". Washington Post. ISSN 0190-8286. Retrieved 2022-09-13.
^"ExtraHop Reveal(x) 360 for AWS detects malicious activity across workloads". Help Net Security. 2022-03-24. Retrieved 2022-08-18.
^"Flowmon ADS – Kyberbezpečnostní nástroj pro detekci nežádoucích anomálií".
^Whittaker, Zack (2020-06-04). "VMware acquires network security firm Lastline, said to lay off 40% of staff". TechCrunch. Retrieved 2022-10-11.
^Overly, Steven (2012-10-29). "Opnet Technologies to be bought for $1B". Washington Post. Retrieved 2022-08-18.
^Snyder, Joel (2008-01-21). "How we tested Sourcefire's 3D System". Network World. Retrieved 2022-09-13.
^Ot, Anina (2022-03-25). "How Endpoint Protection is Used by Finastra, Motortech, Bladex, Spicerhaart, and Connecticut Water: Case Studies". Enterprise Storage Forum. Retrieved 2022-10-06.
^Goled, Shraddha (2021-04-03). "Hackers Are Having A Field Day Post Pandemic: Praveen Jaiswal, Vehere". Analytics India Magazine. Retrieved 2021-05-17.
January 01, 1970
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This article needs additional citations for verification Please help improve this article by adding citations to reliable sources Unsourced material may be challenged and removed Find sources Network behavior anomaly detection news newspapers books scholar JSTOR August 2013 Learn how and when to remove this message Network behavior anomaly detection NBAD is a security technique that provides network security threat detection It is a complementary technology to systems that detect security threats based on packet signatures 1 NBAD is the continuous monitoring of a network for unusual events or trends NBAD is an integral part of network behavior analysis NBA which offers security in addition to that provided by traditional anti threat applications such as firewalls intrusion detection systems antivirus software and spyware detection software Contents 1 Description 2 Popular threat detections within NBAD 3 Commercial products 4 See also 5 ReferencesDescription editMost security monitoring systems utilize a signature based approach to detect threats They generally monitor packets on the network and look for patterns in the packets which match their database of signatures representing pre identified known security threats NBAD based systems are particularly helpful in detecting security threat vectors in two instances where signature based systems cannot i new zero day attacks and ii when the threat traffic is encrypted such as the command and control channel for certain Botnets An NBAD program tracks critical network characteristics in real time and generates an alarm if a strange event or trend is detected that could indicate the presence of a threat Large scale examples of such characteristics include traffic volume bandwidth use and protocol use NBAD solutions can also monitor the behavior of individual network subscribers In order for NBAD to be optimally effective a baseline of normal network or user behavior must be established over a period of time Once certain parameters have been defined as normal any departure from one or more of them is flagged as anomalous NBAD technology techniques are applied in a number of network and security monitoring domains including i Log analysis ii Packet inspection systems iii Flow monitoring systems and iv Route analytics NBAD has also been described as outlier detection novelty detection deviation detection and exception mining 2 Popular threat detections within NBAD editPayload Anomaly Detection Protocol Anomaly MAC Spoofing Protocol Anomaly IP Spoofing Protocol Anomaly TCP UDP Fanout Protocol Anomaly IP Fanout Protocol Anomaly Duplicate IP Protocol Anomaly Duplicate MAC Virus Detection Bandwidth Anomaly Detection Connection Rate DetectionCommercial products editPalo Alto Networks Cortex XDR 3 Darktrace 4 AI Enterprise Immune System Antigena Autonomous Response Allot Communications 5 Allot Communications DDoS Protection Arbor Networks NSI 6 Arbor Network Security Intelligence Cisco Stealthwatch 7 formerly Lancope StealthWatch IBM QRadar since 2003 Enterasys Networks Enterasys Dragon 8 Exinda Inbuilt Application Performance Score APS Application Performance Metric APM SLA and Adaptive Response ExtraHop Networks Reveal x 9 Flowmon Networks 10 Flowmon ADS FlowNBA NetFlow Juniper Networks STRM Fidelis Cybersecurity Network Security Lastline 11 McAfee McAfee Network Threat Behavior Analysis HP ProCurve Network Immunity Manager Riverbed Technology Riverbed Cascade 12 Sourcefire Sourcefire 3D 13 Symantec Symantec Advanced Threat Protection 14 GREYCORTEX Mendel 15 formerly TrustPort Threat Intelligence Vectra AI 16 ZOHO Corporation ManageEngine NetFlow Analyzer s Advanced Security Analytics Module 17 Microsoft Corp Windows Defender ATP and Advanced Threat Analytics Vehere PacketWorker Network Detection and Response 18 See also editUser behavior analyticsReferences edit Hein Daniel 2019 05 15 Network Behavior Analysis and Anomaly Detection The Basics Best Network Monitoring Vendors Software Tools and Performance Solutions Retrieved 2022 06 27 Ahmed Mohiuddin 2016 A survey of network anomaly detection techniques PDF Journal of Network and Computer Applications 60 19 31 doi 10 1016 j jnca 2015 11 016 via Elsevier Palo Alto Networks Cortex XDR 3 0 automates threat detection and investigation across cloud environments Help Net Security 2021 08 24 Retrieved 2022 08 12 Daws Ryan 2022 03 10 Darktrace adds 70 ML models to its AI cybersecurity platform AI News Retrieved 2022 08 12 DDoS Security amp Protection Software Secure Your Network Arbor DDoS Solutions NETSCOUT NETSCOUT How to block online threats and ransomware attacks with Cisco Stealthwatch Business Review in Romanian 2019 01 23 Retrieved 2022 08 24 Heath Thomas 2012 09 23 Tenable enters partnership with In Q Tel Washington Post ISSN 0190 8286 Retrieved 2022 09 13 ExtraHop Reveal x 360 for AWS detects malicious activity across workloads Help Net Security 2022 03 24 Retrieved 2022 08 18 Flowmon ADS Kyberbezpecnostni nastroj pro detekci nezadoucich anomalii Whittaker Zack 2020 06 04 VMware acquires network security firm Lastline said to lay off 40 of staff TechCrunch Retrieved 2022 10 11 Overly Steven 2012 10 29 Opnet Technologies to be bought for 1B Washington Post Retrieved 2022 08 18 Snyder Joel 2008 01 21 How we tested Sourcefire s 3D System Network World Retrieved 2022 09 13 Ot Anina 2022 03 25 How Endpoint Protection is Used by Finastra Motortech Bladex Spicerhaart and Connecticut Water Case Studies Enterprise Storage Forum Retrieved 2022 10 06 GreyCortex Advanced Network Traffic Analysis www greycortex com Retrieved 2016 06 29 Hageman Mitchell 2022 09 05 Vectra AI attributes significant growth to expansion and new innovations IT Brief Australia Retrieved 2022 09 20 NetFlow Traffic Analyzer Real Time NetFlow Analysis ManageEngine NetFlow Analyzer www manageengine com Retrieved 2022 09 20 Goled Shraddha 2021 04 03 Hackers Are Having A Field Day Post Pandemic Praveen Jaiswal Vehere Analytics India Magazine Retrieved 2021 05 17 Retrieved from https en wikipedia org w index php title Network behavior anomaly detection amp oldid 1116502576, wikipedia, wiki, book, books, library,
