SlideShare ist ein Scribd-Unternehmen logo
1 von 21
Large enterprise SIEM:
get ready for oversize
Svetlana/Mona Arkhipova
Qiwi
OWASP Meetup, Moscow, 28 Feb 2015
What are we talking about?
• Log collecting != Security Information and
Event Management
• Systems monitoring is not enough
• Logs as a ‘Big Data’
•
WTF is qRadar?
Hello IBM!
• Log management
• Network activity/anomaly detection
• SIEM
• Nice API
WTF is qRadar?
Administrator’s nightmare:
• Frontend: Java+Tomcat
• Backend: Java daemons
• DB: Ariel for collected+
indexed data, PostgreSQL for ‘static’ data
• Painful performance metrics and load
balancing
Architecture
To log or not to log
Guides/best practices
• https://www.owasp.org/index.php/Logging_Cheat_
Sheet
• http://www.syslog.org/logged/logging-and-syslog-
best-practices/
• http://sniperforensicstoolkit.squarespace.com/stora
ge/logging/Windows%20Logging%20Cheat%20Shee
t%20v1.1.pdf
• https://zeltser.com/media/docs/security-incident-
log-review-checklist.pdf
• …
To log or not to log
Huston, we got a problem:
• Standard syslog message size (RFC 5424)
• Windows security logs permissions on
W7/2008+
• Database audit – what to log?
• Log files on FS (IIS and so on)
• In-house developed apps
To log or not to log
Standard sources: Windows
• Event collectors vs. agents
• Extended system audit
• Non-English logs:
To log or not to log
Standard sources: *nix, network devices
• Syslog as a standard
• TCP syslog+network issues=pain
(google: “TCP is not reliable”)
• UDP syslog message size
• Auditd – what to log?
To log or not to log
Standard sources: Databases
• Is login history enough?
• Syslog vs DB connection
To log or not to log
Non-Standard sources:
• Exotic network devices
• In-house developed apps
• 1C (OMG…) and other specific apps
• Integration with other security systems (NGFW,
DBFW, AV, Security scanners…)
To log or not to log
When syslog is powerless:
WAF CEF log file
Normalizing/indexing
Event at a glance
• Standard properties: timestamp, src IP, dst IP, log
source identifier and so on
• Custom event properties – KISS principle
• No search – no property.
Indexing
• Standard properties – index, index, index!
• Custom event properties indexing: with great
power comes great responsibility…
• BTW, watch your index size.
Over(sizing)
Current Qiwi SIEM metrics:
• 1800 log sources
• 10 000 - 24 000 RAW events per second (EPS)
• ~11 600 network flows per second (FPS),
~700 000 flows per minute(FPM)
SIEM system: 39 virtual servers, 2 hardware servers
with Napatech 2x10G cards, 1 archive server
Over(sizing)
Expectations (sizing) Reality
vCPU 140 160
vRAM 272 Gb 521 Gb
vHDD 15 TB 61 TB
Once upon a time in a far far galaxy we decided to
build our own SIEM…
Online/offline storage
Daily stats:
• 67-145 Gb raw event logs per day
• 37-53 Gb network communication events per
day
• Online storage – fast access (realtime + some
previoius data)
• Offline – archive storage
What if...
…EPS or FPM x2 ?
Internal security scanners
“Normal paranormal” activity inside and outside.
• Butthurt :(
• Log or drop events?
• Custom rules set for nodes
• Keep an eye on credentials!
• Balancers
NAT/SNAThttps://f5.com/resources/white-
papers/load-balancing-101-nuts-and-bolts
Autopilot: ON
• Simple rules
• Chained rules:
Autopilot: ON
Questions?
Svetlana/Mona Arkhipova
Lead information security expert
QIWI infrastructure security team
mona@qiwi.com
mona.sax m0na_sax

Weitere ähnliche Inhalte

Was ist angesagt?

Secure Your Apps with NGINX Plus and the ModSecurity WAF
Secure Your Apps with NGINX Plus and the ModSecurity WAFSecure Your Apps with NGINX Plus and the ModSecurity WAF
Secure Your Apps with NGINX Plus and the ModSecurity WAFNGINX, Inc.
 
Try {stuff} Catch {hopefully not} - Evading Detection & Covering Tracks
Try {stuff} Catch {hopefully not} - Evading Detection & Covering TracksTry {stuff} Catch {hopefully not} - Evading Detection & Covering Tracks
Try {stuff} Catch {hopefully not} - Evading Detection & Covering TracksYossi Sassi
 
conf2015_TLaGatta_CHarris_Splunk_BusinessAnalytics_DeliveringHighLevelAnalytics
conf2015_TLaGatta_CHarris_Splunk_BusinessAnalytics_DeliveringHighLevelAnalyticsconf2015_TLaGatta_CHarris_Splunk_BusinessAnalytics_DeliveringHighLevelAnalytics
conf2015_TLaGatta_CHarris_Splunk_BusinessAnalytics_DeliveringHighLevelAnalyticsTom LaGatta
 
Ransomware-Recovery-as-a-Service
Ransomware-Recovery-as-a-ServiceRansomware-Recovery-as-a-Service
Ransomware-Recovery-as-a-ServiceSagi Brody
 
Introducing Savvius Vigil
Introducing Savvius VigilIntroducing Savvius Vigil
Introducing Savvius VigilSavvius, Inc
 
Proactive monitoring tools or services - Open Source
Proactive monitoring tools or services - Open Source Proactive monitoring tools or services - Open Source
Proactive monitoring tools or services - Open Source B.A.
 
Sullivan white boxcrypto-baythreat-2013
Sullivan white boxcrypto-baythreat-2013Sullivan white boxcrypto-baythreat-2013
Sullivan white boxcrypto-baythreat-2013Cloudflare
 
Incident Response for the Work-from-home Workforce
Incident Response for the Work-from-home WorkforceIncident Response for the Work-from-home Workforce
Incident Response for the Work-from-home WorkforceChristopher Gerritz
 
Log Management Systems
Log Management SystemsLog Management Systems
Log Management SystemsMehdi Hamidi
 
Nagios Conference 2011 - Jared Bird - Using Nagios As A Security Tool
Nagios Conference 2011 - Jared Bird - Using Nagios As A Security ToolNagios Conference 2011 - Jared Bird - Using Nagios As A Security Tool
Nagios Conference 2011 - Jared Bird - Using Nagios As A Security ToolNagios
 
Javier Hijas & Ori Kuyumgiski - Security at the speed of DevOps [rooted2018]
Javier Hijas & Ori Kuyumgiski	- Security at the speed of DevOps [rooted2018]Javier Hijas & Ori Kuyumgiski	- Security at the speed of DevOps [rooted2018]
Javier Hijas & Ori Kuyumgiski - Security at the speed of DevOps [rooted2018]RootedCON
 
Sullivan red october-oscon-2014
Sullivan red october-oscon-2014Sullivan red october-oscon-2014
Sullivan red october-oscon-2014Cloudflare
 
NTXISSACSC3 - Critical Criteria for (Cloud) Workload Security by Steve Armend...
NTXISSACSC3 - Critical Criteria for (Cloud) Workload Security by Steve Armend...NTXISSACSC3 - Critical Criteria for (Cloud) Workload Security by Steve Armend...
NTXISSACSC3 - Critical Criteria for (Cloud) Workload Security by Steve Armend...North Texas Chapter of the ISSA
 
Sullivan randomness-infiltrate 2014
Sullivan randomness-infiltrate 2014Sullivan randomness-infiltrate 2014
Sullivan randomness-infiltrate 2014Cloudflare
 
CNIT 121: 3 Pre-Incident Preparation
CNIT 121: 3 Pre-Incident PreparationCNIT 121: 3 Pre-Incident Preparation
CNIT 121: 3 Pre-Incident PreparationSam Bowne
 

Was ist angesagt? (20)

Secure Your Apps with NGINX Plus and the ModSecurity WAF
Secure Your Apps with NGINX Plus and the ModSecurity WAFSecure Your Apps with NGINX Plus and the ModSecurity WAF
Secure Your Apps with NGINX Plus and the ModSecurity WAF
 
Try {stuff} Catch {hopefully not} - Evading Detection & Covering Tracks
Try {stuff} Catch {hopefully not} - Evading Detection & Covering TracksTry {stuff} Catch {hopefully not} - Evading Detection & Covering Tracks
Try {stuff} Catch {hopefully not} - Evading Detection & Covering Tracks
 
conf2015_TLaGatta_CHarris_Splunk_BusinessAnalytics_DeliveringHighLevelAnalytics
conf2015_TLaGatta_CHarris_Splunk_BusinessAnalytics_DeliveringHighLevelAnalyticsconf2015_TLaGatta_CHarris_Splunk_BusinessAnalytics_DeliveringHighLevelAnalytics
conf2015_TLaGatta_CHarris_Splunk_BusinessAnalytics_DeliveringHighLevelAnalytics
 
Ransomware-Recovery-as-a-Service
Ransomware-Recovery-as-a-ServiceRansomware-Recovery-as-a-Service
Ransomware-Recovery-as-a-Service
 
Michael Jones-Resume-OCT2015
Michael Jones-Resume-OCT2015Michael Jones-Resume-OCT2015
Michael Jones-Resume-OCT2015
 
Introducing Savvius Vigil
Introducing Savvius VigilIntroducing Savvius Vigil
Introducing Savvius Vigil
 
Proactive monitoring tools or services - Open Source
Proactive monitoring tools or services - Open Source Proactive monitoring tools or services - Open Source
Proactive monitoring tools or services - Open Source
 
Sullivan white boxcrypto-baythreat-2013
Sullivan white boxcrypto-baythreat-2013Sullivan white boxcrypto-baythreat-2013
Sullivan white boxcrypto-baythreat-2013
 
Incident Response for the Work-from-home Workforce
Incident Response for the Work-from-home WorkforceIncident Response for the Work-from-home Workforce
Incident Response for the Work-from-home Workforce
 
Log Management Systems
Log Management SystemsLog Management Systems
Log Management Systems
 
Ntxissacsc5 blue 4-the-attack_life_cycle_erich_mueller
Ntxissacsc5 blue 4-the-attack_life_cycle_erich_muellerNtxissacsc5 blue 4-the-attack_life_cycle_erich_mueller
Ntxissacsc5 blue 4-the-attack_life_cycle_erich_mueller
 
how to simulate ACI
how to simulate ACIhow to simulate ACI
how to simulate ACI
 
Action1 overview
Action1 overviewAction1 overview
Action1 overview
 
Nagios Conference 2011 - Jared Bird - Using Nagios As A Security Tool
Nagios Conference 2011 - Jared Bird - Using Nagios As A Security ToolNagios Conference 2011 - Jared Bird - Using Nagios As A Security Tool
Nagios Conference 2011 - Jared Bird - Using Nagios As A Security Tool
 
BlueHat v18 || Scaling security scanning
BlueHat v18 || Scaling security scanningBlueHat v18 || Scaling security scanning
BlueHat v18 || Scaling security scanning
 
Javier Hijas & Ori Kuyumgiski - Security at the speed of DevOps [rooted2018]
Javier Hijas & Ori Kuyumgiski	- Security at the speed of DevOps [rooted2018]Javier Hijas & Ori Kuyumgiski	- Security at the speed of DevOps [rooted2018]
Javier Hijas & Ori Kuyumgiski - Security at the speed of DevOps [rooted2018]
 
Sullivan red october-oscon-2014
Sullivan red october-oscon-2014Sullivan red october-oscon-2014
Sullivan red october-oscon-2014
 
NTXISSACSC3 - Critical Criteria for (Cloud) Workload Security by Steve Armend...
NTXISSACSC3 - Critical Criteria for (Cloud) Workload Security by Steve Armend...NTXISSACSC3 - Critical Criteria for (Cloud) Workload Security by Steve Armend...
NTXISSACSC3 - Critical Criteria for (Cloud) Workload Security by Steve Armend...
 
Sullivan randomness-infiltrate 2014
Sullivan randomness-infiltrate 2014Sullivan randomness-infiltrate 2014
Sullivan randomness-infiltrate 2014
 
CNIT 121: 3 Pre-Incident Preparation
CNIT 121: 3 Pre-Incident PreparationCNIT 121: 3 Pre-Incident Preparation
CNIT 121: 3 Pre-Incident Preparation
 

Ähnlich wie Large enterprise SIEM: get ready for oversize

A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)Spark Summit
 
Hacklu2011 tricaud
Hacklu2011 tricaudHacklu2011 tricaud
Hacklu2011 tricaudstricaud
 
Agile infrastructure
Agile infrastructureAgile infrastructure
Agile infrastructureTarun Rajput
 
Kaseya Connect 2012 - THE ABC'S OF MONITORING
Kaseya Connect 2012 - THE ABC'S OF MONITORINGKaseya Connect 2012 - THE ABC'S OF MONITORING
Kaseya Connect 2012 - THE ABC'S OF MONITORINGKaseya
 
Iot meets Serverless
Iot meets ServerlessIot meets Serverless
Iot meets ServerlessNarendran R
 
Getting Started with Real-time Analytics
Getting Started with Real-time AnalyticsGetting Started with Real-time Analytics
Getting Started with Real-time AnalyticsAmazon Web Services
 
AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly
AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly
AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly SolarWinds Loggly
 
Circonus: Design failures - A Case Study
Circonus: Design failures - A Case StudyCirconus: Design failures - A Case Study
Circonus: Design failures - A Case StudyHeinrich Hartmann
 
Présentation ELK/SIEM et démo Wazuh
Présentation ELK/SIEM et démo WazuhPrésentation ELK/SIEM et démo Wazuh
Présentation ELK/SIEM et démo WazuhAurélie Henriot
 
Fixing twitter
Fixing twitterFixing twitter
Fixing twitterRoger Xia
 
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter  Improving The Performance And Scalability Of The Worlds Most ...Fixing Twitter  Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...smallerror
 
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter  Improving The Performance And Scalability Of The Worlds Most ...Fixing Twitter  Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...xlight
 
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Amazon Web Services
 
Chirp 2010: Scaling Twitter
Chirp 2010: Scaling TwitterChirp 2010: Scaling Twitter
Chirp 2010: Scaling TwitterJohn Adams
 
Architecture at Scale
Architecture at ScaleArchitecture at Scale
Architecture at ScaleElasticsearch
 
Log management principle and usage
Log management principle and usageLog management principle and usage
Log management principle and usageBikrant Gautam
 
Cloud Security Monitoring and Spark Analytics
Cloud Security Monitoring and Spark AnalyticsCloud Security Monitoring and Spark Analytics
Cloud Security Monitoring and Spark Analyticsamesar0
 

Ähnlich wie Large enterprise SIEM: get ready for oversize (20)

Security data deluge
Security data delugeSecurity data deluge
Security data deluge
 
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
A Big Data Lake Based on Spark for BBVA Bank-(Oscar Mendez, STRATIO)
 
Hacklu2011 tricaud
Hacklu2011 tricaudHacklu2011 tricaud
Hacklu2011 tricaud
 
Agile infrastructure
Agile infrastructureAgile infrastructure
Agile infrastructure
 
Kaseya Connect 2012 - THE ABC'S OF MONITORING
Kaseya Connect 2012 - THE ABC'S OF MONITORINGKaseya Connect 2012 - THE ABC'S OF MONITORING
Kaseya Connect 2012 - THE ABC'S OF MONITORING
 
Iot meets Serverless
Iot meets ServerlessIot meets Serverless
Iot meets Serverless
 
Getting Started with Real-time Analytics
Getting Started with Real-time AnalyticsGetting Started with Real-time Analytics
Getting Started with Real-time Analytics
 
AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly
AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly
AWS re:Invent presentation: Unmeltable Infrastructure at Scale by Loggly
 
Circonus: Design failures - A Case Study
Circonus: Design failures - A Case StudyCirconus: Design failures - A Case Study
Circonus: Design failures - A Case Study
 
Microservices
MicroservicesMicroservices
Microservices
 
Présentation ELK/SIEM et démo Wazuh
Présentation ELK/SIEM et démo WazuhPrésentation ELK/SIEM et démo Wazuh
Présentation ELK/SIEM et démo Wazuh
 
Fixing twitter
Fixing twitterFixing twitter
Fixing twitter
 
Fixing_Twitter
Fixing_TwitterFixing_Twitter
Fixing_Twitter
 
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter  Improving The Performance And Scalability Of The Worlds Most ...Fixing Twitter  Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
 
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter  Improving The Performance And Scalability Of The Worlds Most ...Fixing Twitter  Improving The Performance And Scalability Of The Worlds Most ...
Fixing Twitter Improving The Performance And Scalability Of The Worlds Most ...
 
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
Infrastructure at Scale: Apache Kafka, Twitter Storm & Elastic Search (ARC303...
 
Chirp 2010: Scaling Twitter
Chirp 2010: Scaling TwitterChirp 2010: Scaling Twitter
Chirp 2010: Scaling Twitter
 
Architecture at Scale
Architecture at ScaleArchitecture at Scale
Architecture at Scale
 
Log management principle and usage
Log management principle and usageLog management principle and usage
Log management principle and usage
 
Cloud Security Monitoring and Spark Analytics
Cloud Security Monitoring and Spark AnalyticsCloud Security Monitoring and Spark Analytics
Cloud Security Monitoring and Spark Analytics
 

Kürzlich hochgeladen

What is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWhat is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWave PLM
 
CRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceCRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceBrainSell Technologies
 
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样umasea
 
A healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfA healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfMarharyta Nedzelska
 
Software Project Health Check: Best Practices and Techniques for Your Product...
Software Project Health Check: Best Practices and Techniques for Your Product...Software Project Health Check: Best Practices and Techniques for Your Product...
Software Project Health Check: Best Practices and Techniques for Your Product...Velvetech LLC
 
How to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationHow to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationBradBedford3
 
Cyber security and its impact on E commerce
Cyber security and its impact on E commerceCyber security and its impact on E commerce
Cyber security and its impact on E commercemanigoyal112
 
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdfGOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdfAlina Yurenko
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsChristian Birchler
 
PREDICTING RIVER WATER QUALITY ppt presentation
PREDICTING  RIVER  WATER QUALITY  ppt presentationPREDICTING  RIVER  WATER QUALITY  ppt presentation
PREDICTING RIVER WATER QUALITY ppt presentationvaddepallysandeep122
 
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanySuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanyChristoph Pohl
 
Intelligent Home Wi-Fi Solutions | ThinkPalm
Intelligent Home Wi-Fi Solutions | ThinkPalmIntelligent Home Wi-Fi Solutions | ThinkPalm
Intelligent Home Wi-Fi Solutions | ThinkPalmSujith Sukumaran
 
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...Natan Silnitsky
 
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...confluent
 
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Angel Borroy López
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringHironori Washizaki
 
Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Mater
 
Implementing Zero Trust strategy with Azure
Implementing Zero Trust strategy with AzureImplementing Zero Trust strategy with Azure
Implementing Zero Trust strategy with AzureDinusha Kumarasiri
 
Unveiling Design Patterns: A Visual Guide with UML Diagrams
Unveiling Design Patterns: A Visual Guide with UML DiagramsUnveiling Design Patterns: A Visual Guide with UML Diagrams
Unveiling Design Patterns: A Visual Guide with UML DiagramsAhmed Mohamed
 

Kürzlich hochgeladen (20)

What is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need ItWhat is Fashion PLM and Why Do You Need It
What is Fashion PLM and Why Do You Need It
 
CRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. SalesforceCRM Contender Series: HubSpot vs. Salesforce
CRM Contender Series: HubSpot vs. Salesforce
 
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
办理学位证(UQ文凭证书)昆士兰大学毕业证成绩单原版一模一样
 
A healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdfA healthy diet for your Java application Devoxx France.pdf
A healthy diet for your Java application Devoxx France.pdf
 
Hot Sexy call girls in Patel Nagar🔝 9953056974 🔝 escort Service
Hot Sexy call girls in Patel Nagar🔝 9953056974 🔝 escort ServiceHot Sexy call girls in Patel Nagar🔝 9953056974 🔝 escort Service
Hot Sexy call girls in Patel Nagar🔝 9953056974 🔝 escort Service
 
Software Project Health Check: Best Practices and Techniques for Your Product...
Software Project Health Check: Best Practices and Techniques for Your Product...Software Project Health Check: Best Practices and Techniques for Your Product...
Software Project Health Check: Best Practices and Techniques for Your Product...
 
How to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion ApplicationHow to submit a standout Adobe Champion Application
How to submit a standout Adobe Champion Application
 
Cyber security and its impact on E commerce
Cyber security and its impact on E commerceCyber security and its impact on E commerce
Cyber security and its impact on E commerce
 
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdfGOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
GOING AOT WITH GRAALVM – DEVOXX GREECE.pdf
 
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving CarsSensoDat: Simulation-based Sensor Dataset of Self-driving Cars
SensoDat: Simulation-based Sensor Dataset of Self-driving Cars
 
PREDICTING RIVER WATER QUALITY ppt presentation
PREDICTING  RIVER  WATER QUALITY  ppt presentationPREDICTING  RIVER  WATER QUALITY  ppt presentation
PREDICTING RIVER WATER QUALITY ppt presentation
 
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte GermanySuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
SuccessFactors 1H 2024 Release - Sneak-Peek by Deloitte Germany
 
Intelligent Home Wi-Fi Solutions | ThinkPalm
Intelligent Home Wi-Fi Solutions | ThinkPalmIntelligent Home Wi-Fi Solutions | ThinkPalm
Intelligent Home Wi-Fi Solutions | ThinkPalm
 
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
Taming Distributed Systems: Key Insights from Wix's Large-Scale Experience - ...
 
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
Catch the Wave: SAP Event-Driven and Data Streaming for the Intelligence Ente...
 
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
Alfresco TTL#157 - Troubleshooting Made Easy: Deciphering Alfresco mTLS Confi...
 
Machine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their EngineeringMachine Learning Software Engineering Patterns and Their Engineering
Machine Learning Software Engineering Patterns and Their Engineering
 
Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)Ahmed Motair CV April 2024 (Senior SW Developer)
Ahmed Motair CV April 2024 (Senior SW Developer)
 
Implementing Zero Trust strategy with Azure
Implementing Zero Trust strategy with AzureImplementing Zero Trust strategy with Azure
Implementing Zero Trust strategy with Azure
 
Unveiling Design Patterns: A Visual Guide with UML Diagrams
Unveiling Design Patterns: A Visual Guide with UML DiagramsUnveiling Design Patterns: A Visual Guide with UML Diagrams
Unveiling Design Patterns: A Visual Guide with UML Diagrams
 

Large enterprise SIEM: get ready for oversize

  • 1. Large enterprise SIEM: get ready for oversize Svetlana/Mona Arkhipova Qiwi OWASP Meetup, Moscow, 28 Feb 2015
  • 2. What are we talking about? • Log collecting != Security Information and Event Management • Systems monitoring is not enough • Logs as a ‘Big Data’ •
  • 3. WTF is qRadar? Hello IBM! • Log management • Network activity/anomaly detection • SIEM • Nice API
  • 4. WTF is qRadar? Administrator’s nightmare: • Frontend: Java+Tomcat • Backend: Java daemons • DB: Ariel for collected+ indexed data, PostgreSQL for ‘static’ data • Painful performance metrics and load balancing
  • 6. To log or not to log Guides/best practices • https://www.owasp.org/index.php/Logging_Cheat_ Sheet • http://www.syslog.org/logged/logging-and-syslog- best-practices/ • http://sniperforensicstoolkit.squarespace.com/stora ge/logging/Windows%20Logging%20Cheat%20Shee t%20v1.1.pdf • https://zeltser.com/media/docs/security-incident- log-review-checklist.pdf • …
  • 7. To log or not to log Huston, we got a problem: • Standard syslog message size (RFC 5424) • Windows security logs permissions on W7/2008+ • Database audit – what to log? • Log files on FS (IIS and so on) • In-house developed apps
  • 8. To log or not to log Standard sources: Windows • Event collectors vs. agents • Extended system audit • Non-English logs:
  • 9. To log or not to log Standard sources: *nix, network devices • Syslog as a standard • TCP syslog+network issues=pain (google: “TCP is not reliable”) • UDP syslog message size • Auditd – what to log?
  • 10. To log or not to log Standard sources: Databases • Is login history enough? • Syslog vs DB connection
  • 11. To log or not to log Non-Standard sources: • Exotic network devices • In-house developed apps • 1C (OMG…) and other specific apps • Integration with other security systems (NGFW, DBFW, AV, Security scanners…)
  • 12. To log or not to log When syslog is powerless: WAF CEF log file
  • 13. Normalizing/indexing Event at a glance • Standard properties: timestamp, src IP, dst IP, log source identifier and so on • Custom event properties – KISS principle • No search – no property. Indexing • Standard properties – index, index, index! • Custom event properties indexing: with great power comes great responsibility… • BTW, watch your index size.
  • 14. Over(sizing) Current Qiwi SIEM metrics: • 1800 log sources • 10 000 - 24 000 RAW events per second (EPS) • ~11 600 network flows per second (FPS), ~700 000 flows per minute(FPM) SIEM system: 39 virtual servers, 2 hardware servers with Napatech 2x10G cards, 1 archive server
  • 15. Over(sizing) Expectations (sizing) Reality vCPU 140 160 vRAM 272 Gb 521 Gb vHDD 15 TB 61 TB Once upon a time in a far far galaxy we decided to build our own SIEM…
  • 16. Online/offline storage Daily stats: • 67-145 Gb raw event logs per day • 37-53 Gb network communication events per day • Online storage – fast access (realtime + some previoius data) • Offline – archive storage
  • 18. Internal security scanners “Normal paranormal” activity inside and outside. • Butthurt :( • Log or drop events? • Custom rules set for nodes • Keep an eye on credentials! • Balancers NAT/SNAThttps://f5.com/resources/white- papers/load-balancing-101-nuts-and-bolts
  • 19. Autopilot: ON • Simple rules • Chained rules:
  • 21. Questions? Svetlana/Mona Arkhipova Lead information security expert QIWI infrastructure security team mona@qiwi.com mona.sax m0na_sax

Hinweis der Redaktion

  1. RFC limitations: UDP 1024 bytes, TCP 4096 bytes. Winsecurity – network service permissions, winRM,
  2. Collectors vs agents – our case. Extended audit – registry pain.
  3. Pain about TCP:
  4. 3 log src for DB, why DB conn is better
  5. Xml log source extensions
  6. Events – common.
  7. Conf.management systems, wincollect reconfig, license threshold