Virus Detection System
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This document discusses a virus detection system (VDS). It summarizes the main virus trends in 2004, with over 50% being Trojans and backdoors. It outlines the qualities needed for an intrusion detection system (IDS), including meticulous protocol analysis and a lightweight rule set. The document describes the mechanisms of a VDS, including using a unified data structure and dataset to classify events. It addresses challenges of scaling the rule set and optimizing detection algorithms as the number of rules increases.
![AVML and Snort
Echo
virus(id=”B00801”;type=”Backdoor”;os=”Win32”;format=”pe”;na
me=”bo”;version=”a”;size=”124928”;Port_listen=on[31337];cont
ent=|81EC0805000083BC240C05000000535657557D148B84242
40500008BAC242005000050E9950500000F85800500008B|;delm
ark=1)
alert tcp $EXTERNAL_NET any -> $HOME_NET 21
(msg:"Backdoor.bo.a Upload"; content:
|81EC0805000083BC240C05000000535657557D148B842424050
0008BAC242005000050E9950500000F85800500008B |;)
alert tcp $EXTERNAL_NET any -> $HOME_NET 139
(msg:"Backdoor.bo.a Copy"; content:
|81EC0805000083BC240C05000000535657557D148B842424050
0008BAC242005000050E9950500000F85800500008B |;)。](https://image.slidesharecdn.com/vds-120806032901-phpapp01/85/Virus-Detection-System-7-320.jpg)
![Event Processing ( 2 )
If existNet_Action(RPC_Exploit)[IP(1)->IP(2);time(1)]
Net_Action(RPC_Exploit) [IP(2)->IP(3) ;time(2)]
and
time(2)>time(1)
than
Net_Action(RPC_Exploit) [IP(1)-> IP(2) -> IP(3)]](https://image.slidesharecdn.com/vds-120806032901-phpapp01/85/Virus-Detection-System-22-320.jpg)
![Behavior Classifications
DEDL events AVML diagnostic behavior regulations
Net_Action(act)[IP(1),IP(2):445; ;time(1)] Virus_act_lib
Net_Action(act)[IP(1),IP(3):445; ;time(1)] Virus
…. seek(id=”W02872”;dport=139,445;trans=ne
Net_Action(act)[IP(1),IP(12):445; ;time(1)] tbios)
Net_Action(Trans,Worm.Win32.Dvldr)[IP(1)->IP(12);time(1)]](https://image.slidesharecdn.com/vds-120806032901-phpapp01/85/Virus-Detection-System-23-320.jpg)
Virus Detection System
- 2. Outline The virus trends of 2004 Qualities of an IDS Mechanisms of a VDS Data processing
- 3. 20047 new kinds of virus in 2004 Other PE 10% 2% worm AD/Erotic pieces 11% 3% Script virus 黑客工具 1% 6% Back Door 20% Trojan Virus writing generation 45% UNIX tool 0% 2%
- 4. Outline The virus trends of 2004 Qualities of an IDS Mechanisms of a VDS Data processing
- 5. How a traditional IDS works Meticulous protocol analysis Lightweight rule set No more than 500 records in a rule set.
- 6. Unitary software designing Unitary design: In the AV Ware: Scan case of dealing with an target object’s extensive complicated incident, we should divergence. classify the events and IDS: Protocol’s unify one or more of divergence. the processing modules by using an extensible data structure and data set.
- 7. AVML and Snort Echo virus(id=”B00801”;type=”Backdoor”;os=”Win32”;format=”pe”;na me=”bo”;version=”a”;size=”124928”;Port_listen=on[31337];cont ent=|81EC0805000083BC240C05000000535657557D148B84242 40500008BAC242005000050E9950500000F85800500008B|;delm ark=1) alert tcp $EXTERNAL_NET any -> $HOME_NET 21 (msg:"Backdoor.bo.a Upload"; content: |81EC0805000083BC240C05000000535657557D148B842424050 0008BAC242005000050E9950500000F85800500008B |;) alert tcp $EXTERNAL_NET any -> $HOME_NET 139 (msg:"Backdoor.bo.a Copy"; content: |81EC0805000083BC240C05000000535657557D148B842424050 0008BAC242005000050E9950500000F85800500008B |;)。
- 8. Redundant scans caused by divergence FTP Transfer NETBIOS rules character rules rules
- 9. Rule set scaling pressure type quantity Besides worms, there are over Email worm 2807 20,000 Trojans, Backdoors, etc… IM-worm 172 which transfer over the network. P2P-worm 1007 The corresponding IRC-worm 715 rule quantity may exceed 30,000 Other worm 675 records. total 5376
- 10. Outline The virus trends of 2004 Qualities of an IDS Mechanisms of a VDS Data processing
- 11. Algorithm optimization（1） 5000 4500 When the quantity of rules 4000 is less than 6,000, it is not 3500 obvious that time durtation(ms) 3000 2500 increases linearly with 2000 record count. But after 1500 about 10,000 records, that 1000 begins to change, causing 500 0 a sudden drop in performance up until it is 0 00 00 00 00 00 00 0 0 0 0 0 0 0 0 0 0 50 00 50 00 50 00 50 00 50 00 15 30 45 60 75 90 10 12 13 15 16 18 19 21 22 24 records simply unavailable。 The influence of record quantity on record matching time
- 12. Algorithm optimization （2） The scanning speed is also affected by 实际规则检测网络数据 木马检验网络数据 实际规则检测随机数据 the data being 6000 随机规则检测网络数据 matched and the 5000 quality of the patterns. duration (ms) 4000 3000 2000 1000 0 0 1500 3000 4500 6000 7500 9000 10500 12000 13500 15000 16500 18000 19500 21000 22500 24000 records Scan methods’ and data objects’ influence on the speed
- 13. Algorithm optimization （3） 1200 1000 speed(kb/s) original improved 800 600 400 200 0 500 2000 3500 5000 6500 8000 9500 11000 12500 14000 15500 17000 18500 20000 21500 23000 24500 26000 27500 29000 records Influence on efficiency caused by limiting the approximation of the virus’ characteristics
- 14. Key method of designing VDS The Unitary Model focuses on matching speed and matching granularity — matching is of foremost importance. Network traffic data is classified into three types：data matched on the binary level, data needing pre-treatment and data needing specific algorithms。
- 15. Data flow direction and the Level of virus detection Divided into 4 levels: Data log / Process backstage Event process level collection, divergence, detection and (File) Scan Complete Dataflow processing Cross verification Virus scan Stream scan level Provides package scanning, incomplete t en m at Pa re Procotol tag transfer data scanning And cka et g pr es can Data diffluence Protocol analysis and level complete data diffluence scanning. Data collection level Sniffer
- 16. System structure
- 17. Data efficiency Virus data output from Harbin Institute of Technology on July 8 , 2003.
- 18. Statistics from the 26th week of 2005
- 19. Unknown virus forewarning system Detected an unknown worm (I- Worm.Unknow) increasing notably on June 5, 2003. On June 6 it was shown to be the virus I-worm.sobig.f.
- 20. Outline The virus trends of 2004 Qualities of an IDS Mechanisms of a VDS Data processing
- 21. Event Processing ( 1 ) Detection Events Processing methods Description Language Tech-based Internal (DEDL). combine We use descriptors to Parallel combine define standard formats Analysis-based Parallel for network events and combine make them support other formats Radiant combine Defined elements: Convergence combine event type, event ID, Chain combine source IP, target IP, event time, and so on. More than 20 such key elements.
- 22. Event Processing ( 2 ) If existNet_Action(RPC_Exploit)[IP(1)->IP(2);time(1)] Net_Action(RPC_Exploit) [IP(2)->IP(3) ;time(2)] and time(2)>time(1) than Net_Action(RPC_Exploit) [IP(1)-> IP(2) -> IP(3)]
- 23. Behavior Classifications DEDL events AVML diagnostic behavior regulations Net_Action(act)[IP(1),IP(2):445; ;time(1)] Virus_act_lib Net_Action(act)[IP(1),IP(3):445; ;time(1)] Virus …. seek(id=”W02872”;dport=139,445;trans=ne Net_Action(act)[IP(1),IP(12):445; ;time(1)] tbios) Net_Action(Trans,Worm.Win32.Dvldr)[IP(1)->IP(12);time(1)]
- 24. Data processing IRC SERVER IRC SERVER2 IRC SERVER3 IR C Co nn IRC C IRC Connection IRC Connection ec onnec tio tion IRC Connection n Virus.A NODE A NODE B NODE C NODE D Virus.A Node A Node B Virus.A Virus.A Virus.A
- 25. Thoughts Network virus monitoring has been explored academically and productively. It has now expanded into a new technology with its own direction. The path of virus defense leads us to the world of freedom.