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A Spam Mail-based Solution for Botnet Detection and Network Bandwidth Protection

A Spam Mail-based Solution for Botnet Detection and Network Bandwidth Protection. 許富皓 資訊工程學系 中央大學. 1. Outline. Introduction Background System Design Work Flow Evaluation Related Work Conclusion. 2. Outline. Introduction Background System Design Work Flow Evaluation Related Work

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A Spam Mail-based Solution for Botnet Detection and Network Bandwidth Protection

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  1. A Spam Mail-based Solution for Botnet Detection and Network Bandwidth Protection 許富皓 資訊工程學系 中央大學 1

  2. Outline • Introduction • Background • System Design • Work Flow • Evaluation • Related Work • Conclusion 2

  3. Outline • Introduction • Background • System Design • Work Flow • Evaluation • Related Work • Conclusion 3

  4. Spam Mails and Bots • At 2009, research shows that more than 80% spam mails are sent by the bots, called spam bots hereafter, of botnets. • Spam mails take up more than 50% of network bandwidth. 4

  5. Objectives • Detect members of botnet • Filter spam mails • Save network bandwidth 5

  6. Observation • As our observation, the majority of spam bots are not e-mail servers, spam bots usually only send mails but do not receive mails. 6

  7. Outline • Introduction • Background • System Design • Work Flow • Evaluation • Related Work • Conclusion 7

  8. E-mail Architecture 8

  9. Botnets & Spam Mails 9

  10. Outline • Introduction • Background • System Design • Work Flow • Evaluation • Related Work • Conclusion 10

  11. System Layout redirect/block confirmation host Honeypot Packet Analyzer confirm SMTP, POP3, IMAP SMTP, POP3, IMAP Mail Server End users 11

  12. System Component – Packet Analyzer (PA) (1) • Located at a router • Detect spam bots based on the IP ack-packets • which use SMTP(S), POP3(S), or IMAP(S) protocol and • whose sizes are less than 200 bytes (describe later) • Use credit number to record mail transmission status of an IP address. 12

  13. IP Threat Level Table (IPTLT)

  14. System Component – Packet Analyzer (PA) (2) • Clean IPTLT periodically to solve the problem of dynamic allocated IPs (such as, DHCP). • Add an NAT detection mechanism to avoid harming innocent hosts behind an NAT. 14

  15. Credit Number • Credit Number is a property of an IP address. • PA assigns a credit number to every IP address which has appeared in a mail packet (SMTP/POP/IMAP) as the source IP address. 15

  16. Operations of Credit Number Increasing operation When PA detects a SMTP mail packet, the credit number of the source IP of the mail packet will be increased by 1. Decreasing operation When PA detects a POP/IMAP mail packet, the credit number of the source IP of the mail packet will be decreased by a higher value, 3. P.S.: By analyzing real world traffic, in a network the ratio of sending mails to receiving mails is 1:3. 16

  17. Approach to Reduce Packet Analyzer Performance Overhead • Sampling • A router solution should avoid high performance overhead; hence, Packet Analyzer can use sampling to reduce the performance overhead of packet analyzer. • The sample rate is an adjustable parameter. 17

  18. Avoid Noise Created by Large Size Mails No matter what size a mail has, the number of protocol related packets exchanged between the sender and receiver is similar to each other. The sizes of protocol related packets are usually smaller than 200 bytes. To avoid counting large size mails sending by normal users more times, we filter out e-mail related packets with size larger than 200 bytes. 18

  19. System Component – Confirmer (1) • Located at a confirmation host. • Check if a host is a mail server because a mail server may have the same behavior as a bot. • By connecting to SMTP port of a host to check whether it is a mail server. 19

  20. System Component – Confirmer (2) The record that an IP is used by a confirmed host is kept in the IPTLT until the IPTLT is cleaned up; hence, the IP is only needed to be confirmed once before the IPTLT is cleaned up. 20

  21. Outline • Introduction • Background • System Design • Work Flow • Evaluation • Related Work • Conclusion 21

  22. Work Flow Kernel Space Packet Analyzer NetFilter PREROUTING IP threat level table Kernel thread Clean up periodically e-mail related traffic Suspect IP Packets Fetch action Fill action field Check result Suspect IP Linux Router Accept / Drop Confirmer Check SMTP 22 Suspect Host Confirmation Host

  23. Outline • Introduction • Background • System Design • Work Flow • Evaluation • Related Work • Conclusion 23

  24. Performance Evaluation • Scenario • Send 10000 mails. • Mail size: 3 KB. • Transmitting mails through the router with or without SpamFinder. End user (E-mail client) SpamFinder SMTP server Host: ASUS Desktop AS-D672 CPU: Intel Pentium 4 Dual Core 3.2 GHz RAM: 4G LAN: Gigabit Ethernet NIC OS: Windows 7 Host: ASUS Desktop AS-D360 CPU: Intel Pentium 4 3.0 GHz RAM: 512 MB LAN1: 10 Mb/100 Mb Ethernet Controller LAN2: 10 Mb/100 Mb Ethernet Controller OS: Fedora 10, kernel 2.6.27 24

  25. Performance Evaluation • Evaluation Result • Overhead, O(n%) • n: sample rate O(100%) = 4.13 % O(0.2%) = 3.8 % 25

  26. Effectiveness Evaluation • Scenario • Analyze the real world traffic (about 1300 computers of NCTU dorm network) offered by NBL (Network Benchmarking Lab)@NCTU • Analyze the whole day traffic of 6/13/2010 (about 2TB) • Replay traffic (250 ~ 350 Mb/s) Host: HP CQ-45 Notebook CPU: Intel Core 2 Duo P7450 / 2.13 GHz RAM: 4G LAN: 10/100/1000 Gigabit Ethernet LAN OS: Fedora 12 kernel 2.6.32 Real world traffic replay SpamFinder Traffic logs 26

  27. Effective Evaluation • According to the result of analyze, we get the follows information: • The rate of sending and receiving data is 1:3 • With credit threshold = 150, SpamFinder can save 25% e-mail related traffic • Average packet dropped ratio : 0.31 % • NBL uses CISCO 7609 router to collect packet traces. • We use a notebook to make our analysis. 27

  28. Effective Evaluation • According to the result of analses, we get the follows information: • SpamFinder detect 2 spam bots after analyzing 1 day traffic of NCTU dorm network • P.S.: that according to tyc.edu.tw reports: in average in the NCU campus there are 4.1 hosts per day be reported as spam hosts. 28

  29. Effective Evaluation 29

  30. Effective Evaluation [5068] ip: 140.#.#.135, credit: 18147, nat: 0, mail server: 0, action: 0 。。。 Jun 17 14:11:51 [SEND] 140.#.#.135:4552 -> 74.125.157.27:25, total_len:1470 Jun 17 14:11:51 [SEND] 140.#.#.135:4552 -> 74.125.157.27:25, total_len:1470 Jun 17 14:11:51 [SEND] 140.#.#.135:4552 -> 74.125.157.27:25, total_len:1326 Jun 17 14:11:52 [SEND] 140.#.#.135:4839->165.131.174.40:25, total_len:1500 Jun 17 14:11:52 [SEND] 140.#.#.135:4839->165.131.174.40:25, total_len:1500 Jun 17 14:11:52 [SEND] 140.#.#.135:4839->165.131.174.40:25, total_len:896 Jun 17 14:11:53 [SEND] 140.#.#.135:4832 -> 74.86.7.196:25, total_len:1500 Jun 17 14:11:53 [SEND] 140.#.#.135:4832 -> 74.86.7.196:25, total_len:1500 Jun 17 14:11:53 [SEND] 140.#.#.135:4832 -> 74.86.7.196:25, total_len:811 。。。 repeat this action 3628 times 30

  31. Effective Evaluation [3370] ip: 140.#.#.148, credit: 8203, nat: 0, mail server: 0, action: 0 。。。 Jun 14 16:24:14 [SEND] 140.#.#.148:6508 -> 148.123.15.75:25, total_len:1500 Jun 14 16:24:14 [SEND] 140.#.#.148:6508 -> 148.123.15.75:25, total_len:1500 Jun 14 16:24:14 [SEND] 140.#.#.148:6508 -> 148.123.15.75:25, total_len:1142 Jun 14 16:24:17 [SEND] 140.#.#.148:6534->75.126.136.141:25, total_len:1500 Jun 14 16:24:17 [SEND] 140.#.#.148:6534->75.126.136.141:25, total_len:1500 Jun 14 16:24:17 [SEND] 140.#.#.148:6534->75.126.136.141:25, total_len:1500 Jun 14 16:24:17 [SEND] 140.#.#.148:6526 -> 74.125.43.27:25, total_len:1470 Jun 14 16:24:17 [SEND] 140.#.#.148:6526 -> 74.125.43.27:25, total_len:1470 Jun 14 16:24:17 [SEND] 140.#.#.148:6526 -> 74.125.43.27:25, total_len:1470 。。。 repeat this action 2050 times 31

  32. Outline • Introduction • Background • System Design • Work Flow • Evaluation • Related Work • Conclusion 32

  33. Related Work • BotGraph, Large Scale Spamming Botnet Detection, USENIX’09 • Webmail botnet account detection • BotMiner, Clustering Analysis of Network Traffic for Protocol- and Structure-Independent Botnet Detection, USENIX’08 • Network behavior based detection • Wide-scale botnet detection and characterization, HotBots’07, USENIX 33

  34. Outline • Introduction • Background • System Design • Work Flow • Evaluation • Related Work • Conclusion 34

  35. Limitation • If the e-mail sending traffic passes through the router, but the e-mail receiving traffic doesn’t, then the host would be considered as a spam bot. • SpamFinder cannot detect e-mails that are sent and received through a Webmail, but popular web mail services have their effective anti-spam mechanism to filter spam mails. 35

  36. Attack Analysis and Future Work Attackers might send fake IP packets to defame some target hosts, we could check the existence of related connections to detect these behavior. In the future, the spam mails (or IP packets) sent from bots will be redirected to a honeypot for further analysis. 36

  37. Conclusion • We propose a network level spam bot detection mechanism, SpamFinder • Implement it on a Linux router and make evaluations using real world traffic that offered by NBL(Network Benchmarking Lab)@NCTU • The evaluation result show that SpamFinder has low performance overhead and could detect spam bots and protect network bandwidth effectively 37

  38. End • Q&A 38

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