Hota iitd


Published on

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Hota iitd

  1. 1. P2P Security ThreatsP2P Security Threats And TheirAnd Their CountermeasuresCountermeasures Chittaranjan Hota, PhD Associate Professor, Dept. of Computer Science & Engineering Birla Institute of Technology & Science-Pilani, Hyderabad Campus Shameerpet, Hyderabad, AP, India 3rd August 2013 Workshop on Cyber Security, Bharti School, IIT, Delhi
  2. 2. [Source: Privacy & Security, Eric Byres, Communications of the ACM, August 2013] Air gap MythAir gap Myth
  3. 3. GenesisGenesis P2P appsP2P apps running onrunning on BITS campusBITS campus detected…detected…
  4. 4. Power of InternetPower of Internet Source: Cisco VNI Global Forecast, 2011-2016 Source: Envisional: Internet bandwidth usage estimation report, 2011
  5. 5. Source:
  6. 6. Attack examplesAttack examples Tiversa Inc., 2011 SC Magazine, March 2009 "lol is this your new profile pic?" Times of India, Oct 2012
  7. 7. What is a P2P NetworkWhat is a P2P Network (BYOR)(BYOR) A D E F G H F H GA E C C B P2P overlay layer Native IP layer D B AS1 AS2 AS3 AS4 AS5 AS6
  8. 8. DC++
  9. 9. TorrentsTorrents Threads: 186,123, Posts: 2,383,449, Members: 546,944 Seeders: 56668, Leechers: 8246, Peers: 64914, Torrents: 19197   [Source:, 31st July 2013 (3.00pm)]
  10. 10. P2P Traffic ControlP2P Traffic Control
  11. 11. Security Gap in P2PSecurity Gap in P2P Internet Peer A Peer B Malicious Peer C Protected Network Peer X Firewall A TCP Port
  12. 12. Effect of NATing on P2PEffect of NATing on P2P Private IP Addresses Public IP Addresses Server P2P Application Internet NAT
  13. 13. NAT TraversalNAT Traversal Private IP Addresses Public IP Addresses Internet Private IP Addresses Application Relay
  14. 14. Possible Attacks on P2PPossible Attacks on P2P (target) Query: “star” QueryHit “star”,”” Query: “pop” Query: “star” QueryHit “pop”, ”” “star”,”” Query: “pop” Query: “star” Malicious Peer QueryHit: “star”,”” QueryHit: “pop”, ”” 1 2 3 P1 P2 P3 A GET /index.html HTTP/1.0
  15. 15. File sharing network Alice Possible Attacks on P2PPossible Attacks on P2P Bob
  16. 16. index title location file1 file2 file3 file4 file sharing network Possible Attacks on P2PPossible Attacks on P2P Poisoning
  17. 17. Possible Attacks on P2PPossible Attacks on P2P Attacker Genuine Blocks 2.FakeBitMap 4.FakeBlock 3.BlockRequest Victim Peer 5. Hash Fail Genuine Blocks Genuine Blocks 1.TCPConnection
  18. 18. Victim Possible Attacks on P2PPossible Attacks on P2P Sybil
  19. 19. Possible Attacks on P2PPossible Attacks on P2P Tracker Seeder Free RiderFree Rider
  20. 20. Testbed at BITS HyderabadTestbed at BITS Hyderabad Botnet traffic generation InternetInfo. Sec. Lab Dist. Sys. Lab Multimedia Lab Hostels Wing Firewall/Router Core Switch 6509 Distribution Switch 4500 Access Switch 2500 Content Mgmt. Application Servers DB Cluster Intrusion Detection Sys. Ethernet Data collection for P2P and web traffic Traffic Anonymization (Anon tool) Classifier, and IDS for botnet detection
  21. 21. Privacy aware P2P ClassifierPrivacy aware P2P Classifier 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 Application Number of Flows Web mail, http, https, ftp 23,014 p2p BitTorrent, AntsP2P, Gnutella, Mute, eMule 2,76,093 [ Ref: 34]
  22. 22. Experimental ResultsExperimental Results       +++ + = FNTNFPTP TNTP Accuracy
  23. 23. Identifying FrostWire trafficIdentifying FrostWire traffic
  24. 24. Botnet DetectionBotnet Detection
  25. 25. P2P Botnet TracesP2P Botnet Traces Botnet 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]
  26. 26. Bayesian Regularized NNBayesian Regularized 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%
  27. 27. Feature SelectionFeature Selection • 23 features extracted from flows
  28. 28. Large Botnet TracesLarge Botnet Traces Botnet 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
  29. 29. Experimental ResultsExperimental Results
  30. 30. Distributed Data collectionDistributed Data collection and processingand processing Botnet traffic generation InternetInfo. Sec. Lab Dist. Sys. Lab Multimedia Lab Hostels Wing Firewall/Router Core Switch 6509 Distribution Switch 4500 Access Switch 2500 Content Mgmt. Application Servers DB Cluster 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
  31. 31. Hadoop setup running atHadoop setup running at BITS HydBITS Hyd
  32. 32. ReferencesReferences1. 2. 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 8. Intrusion detection system: At: 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. 15. 16. 17. 18. Quinlan, J. R, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993. 19. 20. 21. 22. 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 2013 35. 36. 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 Security 3.4 (2000): 262-294.
  33. 33. Thank You! QuestionsQuestions