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SIEM Based Intrusion Detection Jim Beechey May 2010 GSEC, GCIA, GCIH, GCFA, GCWN twitter: jim_beechey.

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Presentation on theme: "SIEM Based Intrusion Detection Jim Beechey May 2010 GSEC, GCIA, GCIH, GCFA, GCWN twitter: jim_beechey."— Presentation transcript:

1 SIEM Based Intrusion Detection Jim Beechey May 2010 GSEC, GCIA, GCIH, GCFA, GCWN twitter: jim_beechey

2 2 Objective Attackers are more sophisticated and targeted in their attacks. Defenders need systems which help provide visibility and altering across numerous security systems. SIEM adoption driven by compliance Gartner says “more than 80%” Put “Security” back into SIEM using real world examples.

3 3 SIEM System Setup

4 4 Basics – Outbound Traffic Outbound SMTP, DNS and IRC Unexpected outbound connections

5 5 New Hosts and Services Scanner integration for new host and service discovery

6 6 Darknets Network segments without any live systems, but are monitored Any traffic considered suspicious Qradar defines Darknets at setup Qradar Rule: Suspicious Activity: Communication with Known Watched Networks

7 7 Brute-force Attacks Create reports to generate statistical data on failed logins by device, source IP and locked accounts per day. Qradar provides several alerts for brute force attacks. Login Failures Followed by Success and Repeated Login Failures Single Host being the most helpful Customize alerts for maximum impact

8 8 Brute-force Attacks

9 9 Windows Accounts Report of accounts created by whom Alerts for: – accounts not using std naming convention – outside of creation script timeframe – workstation account created – group membership adds to key groups Understand the account management process and alert accordingly

10 10 IDS Context/Correlation Reduce noise by reporting based upon high value systems or asset weights Add context of target operating system Add knowledge of vulnerabilities Rules Target Vulnerable to Detected Exploit Vulnerable to Detected Exploit on Different Port Vulnerable to Different Exploit than Detected on Attacked Port

11 11 Web Application Attacks Analyze WAF logs if possible as header data (POST) not available in server logs Create regular expressions to look for signs of attack, for example /(\%27)|(\')|(\-\-)|(\%23)|(#)/ix – Detects ‘ or -- Create and alert on web honeytokens Fake admin page in robots.txt Fake credentials in html code

12 12 Data Exfiltration Collection of flows or session data is extremely helpful Reports/Alerts based upon – Size/destination of outbound flows “Large Outbound Data Transfer” – Application data inside specific protocols – Frequency of requests/application usage – Session Duration “Long Duration Flow”

13 13 Client Side Attacks Information in Windows event logs: – Process Information Start (592/4688) Ends (593/4689) – New Service Installed (601/4697) – Scheduled Tasks Created (602/4689) – Audit Policy Changed and Cleared (612/4719) and (517/1102) Integration with third-party tools

14 14 Sample Attack

15 15 Summary Defenders need to look for indicators of compromise across many sources SIEM solution centralize data Start small with basic methods, test, and move to more advanced techniques Goal is to detect compromise and provide as much information as possible before starting incident response

16 System Options Commercial SIEM Solutions – ArcSight (www.arcsight.com)www.arcsight.com – Q1Labs Qradar (www.q1labs.com)www.q1labs.com – RSA Envision (www.rsa.com)www.rsa.com Lower Cost/Free Log Search – Q1Labs FE (www.q1labs.com)www.q1labs.com – OSSEC (www.ossec.net)www.ossec.net – Splunk (www.splunk.com)www.splunk.com

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