Good Guys vs. the Bad Guys: Can Big Data Tools Counteract Advanced Threats?
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1 Good Guys vs. the Bad Guys: Can Big Data Tools Counteract Advanced Threats? Will Froning, Information Security Manager, American University of Sharjah Mark Seward, Senior Director, Security and Compliance Marketing Session ID: SPO-209 Session Classification: Intermediate
2 Agenda Understanding data indexing Security New thinking wanted The Security Intelligence Platform Splunk at American University of Sharjah Visualizations wanted Questions slide 4 for directions 2
3 Security What s at Stake? On average, organizations are experiencing a staggering 643 Web-based malicious events each week incidents that effectively penetrate the traditional security infrastructure. FireEye Advanced Threat Report 1H 2012 Released August 29, 2012 slide 4 for directions 3
4 Understanding Unknown Threats, or Thinking like a Criminal Where is the most important and valuable data? What s the typical patch cycle for applications and operating systems? What are the typical security defenses? How does the IT team prioritize vulnerabilities? What structural information silos that exist for the security team? Are normal IT service user activities routinely monitored and correlated? slide 4 for directions 4
5 Gather as Much Data as Possible for Forensics Velocity Volume Variety Variability All data is security relevant Security is a Big Data problem Practice the art of un-concealing slide 4 for directions 5
6 Defining Security Intelligence Enterprise Security Intelligence is: The collection of data from all IT systems in the enterprise that could be security relevant and; The application of the security team s knowledge and skill Resulting in risk reduction Prepare for the Emergence of Enterprise Security Intelligence, Joseph Feiman, Gartner, June 29, slide 4 for directions 6
7 Big Data Indexing Solutions - how do it work? Text Based Search + Statistical Analysis Time Index Ingestion Text Base Search Nested Search Cross Data-type Search Apend Abstract Cluster Bucket Multikv Scrub Join Copyright 2011, Splunk Inc. Rare Data manipulation and visualization commands Cluster Associate Stats AVG Transaction Addtotals Delta Eval Stddev Rare Outlier Streamstats Listen to your data. Timechart slide 4 for directions 7
8 Big data indexing engines collects any Machine Data Customer Facing Data Click-stream data Shopping cart data Online transaction data Logfiles Configs Messages Big data indexing engine Traps Alerts Metrics Scripts Changes Tickets Outside the Datacenter Manufacturing, logistics CDRs & IPDRs Power consumption RFID data GPS data Windows Registry Event logs File system sysinternals Copyright 2011, Splunk Inc. Linux/Unix Configurations syslog File system ps, iostat, top Virtualization & Cloud Hypervisor Guest OS, Apps Cloud Applications Web logs Log4J, JMS, JMX.NET events Code and scripts Databases Configurations Audit/query logs Tables Schemas Networking Configurations syslog SNMP netflow Listen to your data. slide 4 for directions 8
9 Extract Additional Value from Point Products Create the reports you need Trend data over long periods Use a big data system as the glue between point products slide 4 for directions 9
10 Enabling IT Business Risk Scenarios App Mgmt Security Relevant Data Confidentiality / Integrity / Availability IT Ops Compliance Web Analytics Business Analytics CSO / CIO / CEO Views Applying IT Risk Scenarios Finding Abnormal Behaviors Big-Data indexing engine 10 slide 4 for directions 10
11 The spheres of security data All Security Relevant Data Security Relevant Data Normal user and machine generated data credentialed activities unknown threats are behavior bases -- require analytics SIEM Known events as seen by current security architecture vendor supplied slide 4 for directions 11
12 Other Uses for Statistical Analysis Action Phase Source SQL Injection Infiltration WebLogs Password Brutes DNS Exfil Web Crawling slide 4 for directions Infiltration Exfiltration Reconnaissance Auth Logs DNS logs/fw Logs Web/FTP Logs Port Knocking Exfil/CnC Firewall 12 Splunk Search len(_raw) +2.5stddev short delta _time count +2.5stddev count(src_ip) +2.5stddev Count outbound (deny) by ip Why Hacker puts SQL commands in the URL; URL length is standard deviations higher than normal Automated password guessing tools enter credentials much faster than humanly possible Hackers exfiltrate the data in DNS packet; standard deviations more DNS requests from a single IP Web crawlers (copying the web site for comments, passwords, addresses, etc) will be the source IP behind page requests standard deviations higher than normal Threat does inside-out port scan to identify exfiltration paths
13 The New Security Intelligence Platform Machine Data Security Intelligence for Business Security Visualizations for Executives Big Data indexing Engine Statistical Analysis Proactive Monitoring Search and Investigation slide 4 for directions 13
14 The Way Forward Understanding Unknown Threats The old way The NEW way Rigid Signatures Data reduction Vendor dependence Passive Flexible Statistical analysis Data inclusion Team creativity Proactive slide 4 for directions 14
15 Splunk at American University of Sharjah 15
16 At bit about Me Will Froning, CISSP, GWAPT Came to the United Arab Emirates by way of St. Louis Been a security professional 7 years Big fan of automation and integration Investigations are the best part of my job slide 4 for directions 16
17 Keeping up with the kids Monitoring for academic integrity Data required: Logon / Log-off data Database logs NAC data Net Disco - Configuration information and connection data for network devices are retrieved via SNMP (used for asset discovery) Firewall data File change data Zimbra (VMware Zimbra is an enterprise-class , calendar and collaboration solution) slide 4 for directions 17
18 Discoveries Stopping cheating scandals Stopping the use of stolen answer keys Stopping Grades for Sale schemes Password sharing ring broken up Students come forward to admit to cheating Using a bit of theatrics can prove a point! Don t need to find all fraud just enough to get the students to know that getting caught can be unattractive. slide 4 for directions 18
19 Security Monitoring AD Failed Authentication Internal Virus Outbreak Firewall Attacks Zimbra multiple Logins slide 4 for directions 19
20 IT Operations Use Cases Monitoring self-service mail restores to prepare for support calls Phishing Check URL logs for people that visited phishing links Bandwidth monitoring Bandwidth very $$$$$$$ in the UAE Understanding bandwidth usage saved $25,000 per month Product pays for itself in bandwidth savings alone. In essence academic integrity use is free. slide 4 for directions 20
21 Summary - American University of Sharjah Big Data and Analytics: Provides security analysis Provides academic integrity Addresses student that attempt to game the system Provides mail management Saves money through analysis of bandwidth useage slide 4 for directions 21
22 Applying a New Strategy to Security Be less dependent on canned responses to security threats Look at security as a Big Data problem Think about security issues as IT risk scenarios Use statistical analysis to find abnormal patterns in normal IT activity data Start thinking like a criminal (or the cheating student) slide 4 for directions 22
23 Questions 23
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