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P2P Security Threats P2P Security Threats And Their And Their
CountermeasuresCountermeasures
Chittaranjan Hota, PhDAssociate Professor, Dept. of Computer Science & Engineering
Birla Institute of Technology & Science-Pilani, Hyderabad CampusShameerpet, Hyderabad, AP, India
3rd August 2013Workshop on Cyber Security, Bharti School, IIT, Delhi
[Source: Privacy & Security, Eric Byres, Communications of the ACM, August 2013]
Air gap MythAir gap Myth
GenesisGenesis
P2P apps P2P apps running on running on
BITS BITS campus campus
detected…detected…
Power of InternetPower of Internet
Source: Cisco VNI Global Forecast, 2011-2016 Source: Envisional: Internet bandwidth usage estimation report, 2011
Source: http://www.fbi.gov/scams-safety/peertopeer
Attack examples Attack examples
Tiversa Inc., 2011
SC Magazine, March 2009
"lol is this your new profile pic?"
Times of India, Oct 2012
What is a P2P What is a P2P Network (BYOR)Network (BYOR)
A
D
E F
G
H
FH
GA
EC
C
B
P2P overlay layer
Native IP layer
D
B
AS1
AS2
AS3
AS4
AS5
AS6
DC++
TorrentsTorrents
Threads: 186,123, Posts: 2,383,449, Members: 546,944
Seeders: 56668, Leechers: 8246, Peers: 64914,
Torrents: 19197
[Source: desitorrents.com, 31st July 2013 (3.00pm)]
P2P Traffic ControlP2P Traffic Control
Security Gap in P2PSecurity Gap in P2P
Internet
Peer APeer B
Malicious Peer C
Protected Network
Peer XFirewall
A TCP Port
Effect of NATing on Effect of NATing on P2P P2P
Private IP Addresses Public IP Addresses
Server
P2P Application
Internet
NAT
NAT TraversalNAT Traversal
Private IP Addresses Public IP Addresses
Internet
Private IP Addresses
Application Relay
Possible Attacks on Possible Attacks on P2PP2P
192.168.100.220:80(target)
Query: “star”
QueryHit“star”,” 192.168.100.220:80”
Query: “pop”Query: “star”
QueryHit“pop”, ” 192.168.100.220:80”“star”,” 192.168.100.220:80”
Query: “pop”Query: “star”
MaliciousPeer
192.168.100.40:4442
QueryHit: “star”,” 192.168.100.220:80”QueryHit: “pop”, ” 192.168.100.220:80”
1
2
3 P1
P2
P3
A
GET /index.html HTTP/1.0
File sharing network
Alice
Possible Attacks on Possible Attacks on P2PP2P
Bob
indextitle locationfile1 120.18.89.100file2 46.100.80.23file3 234.8.98.20file4 111.22.22.22
file sharing network
120.18.89.100
46.100.80.23
234.8.98.20
Possible Attacks on Possible Attacks on P2PP2P
Poisoning
Possible Attacks on Possible Attacks on P2PP2P
Attacker
Genuine Blocks
2. F
ake
BitM
ap
4. F
ake
Blo
ck
3. B
lock
Req
uest
Victim Peer
5. Hash Fail
Genuine Blocks
Genuine Blocks
1. T
CP
Con
nect
ion
Victim
Possible Attacks on Possible Attacks on P2PP2P
Sybil
Possible Attacks on Possible Attacks on P2PP2P
Tracker
Seeder
Free RiderFree Rider
Testbed at BITS Testbed at BITS HyderabadHyderabad
Botnet traffic generation
InternetInfo. Sec. Lab
Dist. Sys. Lab Multimedia
Lab
HostelsWing
Firewall/Router
Core Switch 6509
Distribution Switch 4500
Access Switch 2500
Content Mgmt.
ApplicationServers
DBCluster
Intrusion Detection Sys.
Ethernet
Data collection for P2P and web traffic
Traffic Anonymization (Anon tool)
Classifier, and IDS for botnet detection
Privacy aware P2P Privacy aware P2P ClassifierClassifier
public Conversation(String sender, String receiver, Int src, int dst, boolean tcp){
sender_ip = sender;receiver_ip = receiver;this.setSender(new Flow(sender, receiver, src, dst, tcp));this.setReceiver(new Flow(receiver,sender, dst, src, tcp));sndr_port = src;rcvr_port = dst;set =false; last = 0;first = 0;timestamps = new TreeSet<Long>();
}for(Packet p : plist){
if(p.isTcp() && !p.getTcp_flag()[7] && !p.getTcp_flag()[6] && !p.getTcp_flag()[5]){
++nonsyn_count;}else if(!p.isTcp()){
++nonsyn_count;}if(p.isTcp()&&p.getTcp_flag()[4]){
++psh_count;}++count;
hdr_size_total = hdr_size_total + p.getHdr_size();pkt_size_total = pkt_size_total + p.getPacket_size(); pktsize.add(p.getPacket_size());}
Categories ApplicationNumber of
Flows
Web mail, http, https, ftp 23,014
p2pBitTorrent, AntsP2P,
Gnutella, Mute, eMule2,76,093
[ Ref: 34]
Experimental ResultsExperimental Results
FNTNFPTP
TNTPAccuracy
Identifying FrostWire Identifying FrostWire traffictraffic
Botnet DetectionBotnet Detection
P2P Botnet TracesP2P Botnet TracesBotnet name
What it does? Size of data
Source of data
Kelihos-Hlux Email spam, DoS, steal Bitcoin wallets
5 MB Generated on testbed + obtained form online sources [35]
Waledac Email spam, password stealing 25 MB ISOT dataset [36]
ZeuS Steals banking information by MITM key logging and form grabbing
5 MB Generated on testbed
TRAINING DATA TEST DATA
ZeuS Steals banking information by MITM key logging and form grabbing
25 MB ISOT dataset [36]
Storm Email spam 30 MB ISOT dataset [36]
Conficker Disables important system services and security products
50 GB Obtained from CAIDA [37]
Bayesian Regularized Bayesian Regularized NN NN
• Bayesian Regularized Neural Network based Real-time Peer-to-Peer Botnet Detection, Pratik Narang, Sharat Chandra, Chittaranjan Hota, Accepted in IEEE P2P 2013, Trento, Italy (Sept 2013)
• 23 features extracted from flows.
• Information Gain with ranking used to rank the features .
• Top 16 features chosen.
Output Correct Classification
Incorrect Classification
Malicious samples
25898 276
Percentage 98.9455% 1.0545%
Feature SelectionFeature Selection• 23 features extracted from flows
Large Botnet TracesLarge Botnet TracesBotnet name
What it does? Type of data/Size of data
Source of data
Sality Infects executable files, attempts to disable security software.
Binary (.exe) file Generated on testbed
Storm Email Spam .pcap file/ 4.8 GB Obtained from Uni. of Georgia [34]
Waledac
Email spam, password stealing
.pcap file/ 68 GB Obtained from Uni. of Georgia [34]
ZeuS Steals banking information by MITM key logging and form grabbing
.pcap file/ 105 MB
Obtained from Uni. of Georgia [34] + Generated on test bed
Experimental ResultsExperimental Results
Distributed Data Distributed Data collection and processingcollection and processing
Botnet traffic generation
InternetInfo. Sec. Lab
Dist. Sys. Lab Multimedia
Lab
HostelsWing
Firewall/Router
Core Switch 6509
Distribution Switch 4500
Access Switch 2500
Content Mgmt.
ApplicationServers
DBCluster
Intrusion Detection Sys.
Ethernet
Data collection for P2P and web traffic
Classifier, and IDS for botnet detection
Traffic Anonymization (Anon tool)
Hadoop Name node
Hadoop Data nodes
Hadoop setup running at Hadoop setup running at BITS HydBITS Hyd
ReferencesReferences1. http://news.netcraft.com/archives/2007/05/23/p2p_networks_hijacked_for_ddos_attacks.htm2. S Mcbride, and G A Flower, Estimate of Film-piracy cost soars: Hollywood loss is put at $6.1b a year, The Wall Street Journal Europe, may 4th, 2006. 3. Thomas Karagiannis, Andre Broido, Michalis Faloutsos, Kc claffy, Transport Layer Identification of P2P Traffic, in Proc. 4th ACM SIGCOMM conference on Internet measurement, pp. 121-134, 2004. 4. Subhabrata Sen, Oliver Spatscheck, and Dongmei Wang, Accurate, Scalable InNetwork Identification of P2P Traffic Using Application Signatures, WWW 2004, May 2004.5. S Sen, Jia Wang, Analyzing Peer-To-Peer Traffic Across Large Networks, IEEE/ACM Transactions on Networking, Vol. 12, No. 2, April 2004.6. Thuy T T N, and G Armitage, A survey of Techniques for Internet Traffic Classification using Machine Learning, IEEE Communications Surveys & Tutorials, Vol. 10, No. 4, 2008.7. Hassan Khan, S A Khayam, L Golubchik, M. Rajarajan, and Michael Orr, Wirespeed, Privacy-Preserving P2P Traffic Detection on Commodity Switches, Available Online at www.xflowresearch.com 8. Intrusion detection system: At: http://en.wikipedia.org/wiki/Intrusion_detection_system.9. P. Garcia-Teodoroa, J. Diaz-Verdejo, G.Macia-Fernandeza, and E. Vazquezb, Anomaly-based network intrusion detection: Techniques, systems and challenges, Computers and Security, vol. 28, Issue: 1-2, pp. 18-28,
2009.10. Gupta R, and Somani A K, Game theory as a tool to strategize as well as predict node’s behavior in peer-to-peer networks , International conf. on PDS, 2005, pp. 244-249.11. Roberto G Cascella, 2nd ENISA Workshop on Authentication Interoperability Languages held at the ENISA/EEMA European eIdentity conference, Paris, France, June 12-13, 2007.12. C Wang, Li Chen, H Chen, and K Zhou, Incentive Mechanism Based on Game Theory in P2P Networks, ITCS 2010, pp. 190-193.13. Sarraute, C., et al., Simulation of Computer Network Attacks, CoreLabs, Core Security Technologies, 2010.14. http://www.metasploit.com/15. www.metasploit.com/modules/exploit/multi/browser/java_atomicreferencearray16. www.metasploit.com/modules/auxiliary/dos/windows/rdp/ms12_020_maxchannelids17. http://www.metasploit.com/modules/exploit/windows/smb/ms08_067_netapi18. Quinlan, J. R, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993. 19. http://www.cs.waikato.ac.nz/ml/weka/20. http://pytbull.sourceforge.net/21. http://www.secdev.org/projects/scapy22. Massicotte, F. and Labiche, Y, An analysis of signature overlaps in Intrusion Detection Systems, Dependable Systems & Networks (DSN) IEEE/IFIP 41st International Conference, pp. 109-120, 2011.23. Cheng-Yuan Ho, Yuan-Cheng Lai, I-Wei Chen, Fu-Yu Wang, and Wei-Hsuan Tai, Statistical analysis of false positives and false negatives from real traffic with intrusion detection/prevention systems, Communication
Magazine, IEEE, pp.146-154, 2012.24. Sardar Ali, Hassan Khan, and Syed Ali Khayam, What is the Impact of P2P Traffic on Anomaly Detection?, Proceeding of 13th International symposium, Recent Advances in Intrusion Detection (RAID) 2010, pp. 1-7,
2010. 25. Jeffrey Erman, et al. Identifying and Discriminating Between Web and Peer-to-Peer in the Network Core, WWW 2007, ACM, pp. 883-892.26. Genevieve B, et al., Estimating P2P traffic volume at USC, Technical Report, USC, June 2007.27. Alok Madhukar, Carey W, A Longitudinal Study of P2P Traffic Classification, IEEE International Symposium on Modeling, Analysis, and Simulation, CA, 2006, pp. 179-188.28. Hongwei C, et al., A SVM method for P2P traffic identification based on multiple traffic mode, Journal of Networks, Nov 2010, pp. 1381-1388.29. K Ilgun, et al, State transition analysis: A rule based intrusion detection approach, IEEE transactions on software engineering, Vol 21, 1995.30. F Jemili, et al, A framework for an adaptive intrusion detection system using bayesian network, IEEE Intelligence and Security Informatics, May 2007, pp.66-70.31. Soysal, Murat, and Ece Guran Schmidt. "Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison." Performance Evaluation 67.6 (2010): 451-467.32. Williams, Nigel, Sebastian Zander, and Grenville Armitage. "A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification." ACM SIGCOMM Computer
Communication Review36.5 (2006): 5-16.33. Berg, Peter Ekstrand. "Behavior-based Classification of Botnet Malware." Thesis Report 2011, Gjovik University College, Norway.34. Rahbarinia, Babak, Roberto Perdisci1 Andrea Lanzi, and Kang Li. "PeerRush: Mining for Unwanted P2P Traffic.“ DIMVA 201335. www.contagiodump.blogspot.in36. Saad, Sherif, et al. "Detecting P2P botnets through network behavior analysis and machine learning." Privacy, Security and Trust (PST), 2011 Ninth Annual International Conference on. IEEE, 2011.37. CAIDA, UCSD. "Network Telescope" Three Days Of Conficker“ 21st Nov. 2008."Paul Hick, Emile Aben, Dan Andersen and kcclaffy http://www. caida. org/data/passive/telescope-3days-conficker_dataset. xml.38. Abbes, Tarek, Adel Bouhoula, and Michaël Rusinowitch. "Protocol analysis in intrusion detection using decision tree." Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. International
Conference on. Vol. 1. IEEE, 2004.39. S. Chebrolu, A. Abraham, and J. P. Thomas. Feature deduction and ensemble design of intrusion detection systems. Computers & Security, 24(4):295–307, 2005.40. A.H.Sung and S. Mukkamala. The feature selection and intrusion detection problems. In Advances in Computer Science-ASIAN 2004. Higher-Level Decision Making, pages 468–482. Springer, 2005.41. McHugh, John. "Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory." ACM transactions on Information and system
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