Network behavior anomaly detection

From HandWiki
Short description: Approach to network security

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.

Description

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]

Popular threat detections within NBAD

  • Payload 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 Detection

Commercial products

See also

References

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  2. Ahmed, Mohiuddin (2016). "A survey of network anomaly detection techniques". Journal of Network and Computer Applications 60: 19–31. doi:10.1016/j.jnca.2015.11.016. https://daneshyari.com/article/preview/457163.pdf. 
  3. "Palo Alto Networks Cortex XDR 3.0 automates threat detection and investigation across cloud environments" (in en-US). 2021-08-24. https://www.helpnetsecurity.com/2021/08/24/palo-alto-networks-cortex-xdr-3-0/. 
  4. Daws, Ryan (2022-03-10). "Darktrace adds 70 ML models to its AI cybersecurity platform" (in en-GB). https://www.artificialintelligence-news.com/2022/03/10/darktrace-adds-70-ml-models-ai-cybersecurity-platform/. 
  5. "DDoS Security & Protection Software: Secure Your Network". https://www.allot.com/products/security/serviceprotector/. 
  6. "Arbor DDoS Solutions – NETSCOUT". http://www.arbornetworks.com/products/pravail/nsi. 
  7. "How to block online threats and ransomware attacks with Cisco Stealthwatch" (in ro). 2019-01-23. https://business-review.eu/partner-content/how-to-block-online-threats-and-ransomware-attacks-with-cisco-stealthwatch-195307. 
  8. Heath, Thomas (2012-09-23). "Tenable enters partnership with In-Q-Tel" (in en-US). Washington Post. ISSN 0190-8286. https://www.washingtonpost.com/business/capitalbusiness/tenable-enters-partnership-with-in-q-tel/2012/09/23/50e82e64-01a9-11e2-b257-e1c2b3548a4a_story.html. 
  9. "ExtraHop Reveal(x) 360 for AWS detects malicious activity across workloads" (in en-US). 2022-03-24. https://www.helpnetsecurity.com/2022/03/24/extrahop-revealx-360-aws/. 
  10. "Flowmon ADS – Kyberbezpečnostní nástroj pro detekci nežádoucích anomálií". https://www.flowmon.com/en/products/flowmon/anomaly-detection-system. 
  11. Whittaker, Zack (2020-06-04). "VMware acquires network security firm Lastline, said to lay off 40% of staff" (in en-US). https://techcrunch.com/2020/06/04/vmware-lastline-staff-cuts/. 
  12. Overly, Steven (2012-10-29). "Opnet Technologies to be bought for $1B" (in en-US). Washington Post. https://www.washingtonpost.com/blogs/capital-business/post/opnet-technologies-to-be-bought-for-1b/2012/10/29/0c6a3ef0-21d7-11e2-8448-81b1ce7d6978_blog.html. 
  13. Snyder, Joel (2008-01-21). "How we tested Sourcefire's 3D System" (in en). https://www.networkworld.com/article/2282089/how-we-tested-sourcefire-s-3d-system.html. 
  14. Ot, Anina (2022-03-25). "How Endpoint Protection is Used by Finastra, Motortech, Bladex, Spicerhaart, and Connecticut Water: Case Studies" (in en-US). https://www.enterprisestorageforum.com/software/endpoint-protection-use-cases/. 
  15. "GreyCortex | Advanced Network Traffic Analysis". http://www.greycortex.com/. 
  16. Hageman, Mitchell (2022-09-05). "Vectra AI attributes significant growth to expansion and new innovations" (in en). https://itbrief.com.au/story/vectra-ai-attributes-significant-growth-to-expansion-and-new-innovations. 
  17. "NetFlow Traffic Analyzer | Real-Time NetFlow Analysis - ManageEngine NetFlow Analyzer". https://www.manageengine.com/products/netflow/. 
  18. Goled, Shraddha (2021-04-03). "Hackers Are Having A Field Day Post Pandemic: Praveen Jaiswal, Vehere" (in en-US). https://analyticsindiamag.com/hackers-are-having-a-field-day-post-pandemic-praveen-jaiswal-vehere/.