Deciphering and Mitigating Blackhole Spam from -borne Threats
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1 Deciphering and Mitigating Blackhole Spam from -borne Threats Samir Patil Symantec Deciphering and Mitigating Blackhole Spam from -borne Threats 1
2 Outline 1 Background 2 Detection Challenges 3 How to get over it? 4 Dataset and Result 5 Conclusion Deciphering and Mitigating Blackhole Spam from -borne Threats 2
3 Spam Filter should be.. Effective Fast in detection Create very low number of false positives Low maintenance Deciphering and Mitigating Blackhole Spam from -borne Threats 3
4 Blackhole Spam Deciphering and Mitigating Blackhole Spam from -borne Threats 4
5 Lifecycle Blackhole Exploit Kit User receives fake notification containing malicious URL User is redirected to compromised site and then redirected to malicious site hosting Blackhole Exploit Blackhole Exploit determines software vulnerability and drops the malware Deciphering and Mitigating Blackhole Spam from -borne Threats 5
6 Thousands Daily BH Spam Volume at Symantec Spam Trap Deciphering and Mitigating Blackhole Spam from -borne Threats 6
7 Abuse of Brand Templates 9% 3% 3% Social Network Payroll Services 8% 11% 35% Fax Services Consumer & Business Services Tax Services Courier express services 31% Other Deciphering and Mitigating Blackhole Spam from -borne Threats 7
8 Characteristics of a BH attack Legit templates Hijacked domain Use of Botnets Randomization Short URL life Deciphering and Mitigating Blackhole Spam from -borne Threats 8
9 Proposal Message features Database profiling Static feature analyzer JavaScript analysis Obfuscation and encoded code Dynamic analysis Feature Extraction Pre-filter Feature Analyzer JavaScript Analyzer Downloader Deciphering and Mitigating Blackhole Spam from -borne Threats 9
10 Pre-filter Narrow the processing sample set Template matching Used features Volume features URI features Template features Relaxed yet powerful! Deciphering and Mitigating Blackhole Spam from -borne Threats 10
11 Feature Analyzer URL Patterns domain>/<8 alphanumeric characters>/index.html domain>/<short dictionary word>.html domain>/wp-content/<path>/<short dictionary word>.htm index page).php?t=<16-digit-hex-number> Template Subject: Verify your account From: [removed] Suspect URL: hxxp://zixxxxame.co.za/ffn1dcv8/index.html Deciphering and Mitigating Blackhole Spam from -borne Threats 11
12 Feature Analyzer IP Lookup IP reputation Dotted Quad IP reputation DNSBL Deciphering and Mitigating Blackhole Spam from -borne Threats 12
13 IP Lookup IP reputation Reputation Score Msgs Spam RDNS Lists Ancestors Bad TRUE css xx.x - added as SMTP server DNSBL Deciphering and Mitigating Blackhole Spam from -borne Threats 13
14 Feature Analyzer URL Reputation and Heuristics Deciphering and Mitigating Blackhole Spam from -borne Threats 14
15 Dynamic Checks Deciphering and Mitigating Blackhole Spam from -borne Threats 15
16 JS Analyzer JS Analyzer Analyze compromised webpage Analyze landing page Blackhole Exploit Kit Compromised webpage containing obfuscated JS Landing page containing encoded JS Deciphering and Mitigating Blackhole Spam from -borne Threats 16
17 JavaScript Obfuscation and <iframe> visibility:hidden src= main.php?page=298e0c1b8 9821c16 Deciphering and Mitigating Blackhole Spam from -borne Threats 17
18 Downloader Payload downloader Heuristic engine Connecting IP reputation Deciphering and Mitigating Blackhole Spam from -borne Threats 18
19 Connecting IP Reputation Example Deciphering and Mitigating Blackhole Spam from -borne Threats 19
20 System Deciphering and Mitigating Blackhole Spam from -borne Threats 20
21 Illustrative Example Feature Values Headers Origin IP DNS Initial URI JavaScript Subject: Option: Can New-Pope Benedict be Sued for [removed] Abuse Cases? [removed].com Source IP: xx.xxx Reputation: Neutral RDNS: false Tokens from JS [not shown] URI Tokens Dotted-quad: 0 Length: 1 Domain: 1 Index page: 1 Tokens: Domain, popeabuse.html Final URI Dotted-quad: 0 Length: 4 Domain: 1 Index page: 0 Tokens: Domain, /app, /data, /ap1.php?f=6189f Deciphering and Mitigating Blackhole Spam from -borne Threats 21
22 Result Stage Attribute Confidence Description Stage 1 IP_REP 0% IP reputation: neutral No evidence of spam messages HTML_ANALYSIS 10% Historical HTML pattern Template mapping MALFORMED_JS 10% Obfuscated JS Randomized JS URI_REP 0% URI reputation: neutral JS_ANALYSIS 60% Hidden iframe URI reputation: known bad URI pattern: bad Stage 2 BLACKHOLE_URI 60% Code analysis: bad System scan Deciphering and Mitigating Blackhole Spam from -borne Threats 22
23 Detection FP Detection Attack 1 Attack 2 Attack 3 Attack 4 Attack 5 Attack 6 Deciphering and Mitigating Blackhole Spam from -borne Threats 23
24 Detection % 99.80% 99.71% 99.60% 99.40% 99.20% 99.00% Attack 1 Attack 2 Attack 3 Attack 4 Attack 5 Attack 6 Deciphering and Mitigating Blackhole Spam from -borne Threats 24
25 Conclusions Spam is a bigger problem! Malicious attacks are becoming sophisticated by the day Just reputation and content filtering is not enough Solution Static and dynamic analysis Payload analysis Deciphering and Mitigating Blackhole Spam from -borne Threats 25
26 Questions? Deciphering and Mitigating Blackhole Spam from -borne Threats 26
27 Thank you! Copyright 2011 Symantec Corporation. All rights reserved. Symantec and the Symantec Logo are trademarks or registered trademarks of Symantec Corporation or its affiliates in the U.S. and other countries. Other names may be trademarks of their respective owners. This document is provided for informational purposes only and is not intended as advertising. All warranties relating to the information in this document, either express or implied, are disclaimed to the maximum extent allowed by law. The information in this document is subject to change without notice. Deciphering and Mitigating Blackhole Spam from -borne Threats 27
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