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V ladimir Gorodetsky Head of Laboratory of Intelligent Systems space.iias.spb.su/ai/

Agent and Data Mining Research in Laboratory of Intelligent Systems (St. Petersburg Institute for Informatics and Automation). V ladimir Gorodetsky Head of Laboratory of Intelligent Systems http://space.iias.spb.su/ai/ gor@mail.iias.spb.su. Contents.

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V ladimir Gorodetsky Head of Laboratory of Intelligent Systems space.iias.spb.su/ai/

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  1. Agent and Data Mining Research in Laboratory of Intelligent Systems (St. Petersburg Institute for Informatics and Automation) Vladimir Gorodetsky Head of Laboratory of Intelligent Systems http://space.iias.spb.su/ai/ gor@mail.iias.spb.su

  2. Contents 1. Structure of the research and developments of the Intelligent System Laboratory 2. Multi-Agent System Development Kit (MASDK): A software tool supporting MAS application technology 3. Agent-based distributed data mining and machine learning 4. International collaboration 5. Russian Grant and projects 6. Relevant publications

  3. Laboratory stuff • 11 researchers including • Ph.D. -- 3 • Research analysts and programmers – 4 • Ph.D. students -- 4

  4. 1. Structure of the Research and Developments of the Intelligent System Laboratory

  5. Types of the Research of IS Laboratory Fundamental research: • Machine learning, distributed data mining and decision making • Resource constraint project planning and scheduling • Protocols for distributed data mining and decision making • Agent-based simulation Technology and software tools • Technology and software tool for multi-agent application design, implementation and deployment • Agent-based technology for distributed data mining and decision making system • Technology for resource constraint project planning and scheduling • Software tool kit for machine learning Multi-agent applications (software prototyping) • Intrusion detection, • Design process planning, scheduling and management, • Image processing, • Airspace deconfliction, • Transportation logistics, etc.

  6. Research Structure Multi-agent technology and MASDK software tool Data mining & machine learning tool kit RoboCup (2004 World winner in Simulation league) Problem-oriented multi-agent technology P2P agent-based service-oriented networks (NEW) Distributed data mining and decision making infrastructure Computer Network security Information fusion for situation assessment Project planning and scheduling Transportation logistics Image processing Learning of Intrusion detection Intrusion detection Knowledge-based project planning and scheduling Airspace deconfliction (P2P decision making) Agent-based simulation Simulation of distributed attacks against computer network

  7. 2. Multi-Agent System Development Kit: A Software Tool Supporting MAS Application Technology

  8. General Description of MASDK: Multi-Agent System Development Kit System Core Applied system specification in XML Host Host Agent Agent Agent Agent Agent Agent Portal Portal Multi Agent System Development Kit Integrated editor system Software agent builder Generic agent Communication platform

  9. 3. Agent-based Distributed Data Mining and Machine Learning

  10. User interface User interface User interface User interface Source-based Infrastructure component Source-based Infrastructure component Source-based infrastructure component Source-based Infrastructure component Sensor Sensor Data Source Data Source Interaction Protocols Agent-based (Mediated) Distributed Learning Infrastructure Data Source KE Data Source KE Meta-level KE (manager) Sensor Data Source Host 1 User interface Host k Meta-level infrastructure component Communication Platform Data Source KE Data Source KE Host 2 Host 3 Sensor Data Source Distributed Learning Infrastructure=source host-based components + meta-level component+ interaction protocols + communication platform +user interfaces (not the machine learning algorithms!)

  11. Example of Application: Distributed Learning of Intrusion Detection (Hierarchical Architecture) NETWORL TRAFFIC Preprocessing procedures Data Source 1 Data Source 2 Data Source 3 Data Source 4 Data Source 5 Source-based classifiers Source-based classifiers Source-based classifiers Source-based classifiers Source-based classifiers Decision stream 4 Decision stream 1 Decision stream 2 Decision stream 3 Decision stream 5 Input: composition of asynchronous data streams Two-level meta-classification Computer security status: {Normal or attack of a class} Output:

  12. International Collaboration (Projects) • US Air Force Research Laboratory - European Office of Aerospace Research and Development--8 year collaboration since 1998, 5 projects successfully completed, 1 - in progress until August 2007, new one is discussed) • FP4, FP5, FP6: “AgentLink: Coordination Action for Agent-based Computing”, • FP6 FET Project: “POSITIF” – “Formal specification and verification of computer network security policy”, • FP5 KDNet NoE: “Data Mining and Knowledge Discovery”, • FP6 KDUbiq NoE: “Knowledge Discovery for Ubiquitous Computing” (WG2 member) • Cadence Design System Ltd. (USA, German Research office) – “Multi-agent system for design activity support in microelectronics” (2004-2006) • INTEL (USA)–”Preprocessing algorithms for intrusion detection” (2004-2005) • Fraunhofer First Institute, BMBF (Germany) – MIND–”Machine Learning in Intrusion Detection System” (2004-2006)

  13. Grants and Projects: Russia Grants of Russian Foundation for Basic Research: • Multi-agent technology for distributed learning and decision making (2004-2006); Projects from Department of Information Technology and Computer Systems of the Russian Academy of Sciences: • Agent-based stochastic modeling and simulation of adversarial competition of teams in the Internet environment (2003-2005); • Mathematical models of active audit of computer network vulnerabilities, intrusion detection and response: Multi-agent approach (2003-2005); • Multi-agent technology and software tool (2004-2006)

  14. International Conferences etc. Organized by IS Laboratory 1-4. Mathematical methods, model and architectures for computer network security (MMM-ACNS): 2001, 2003, 2005 (Proceedings in LNCS of Springer, vol. 2952, 2776, 3685), MMM-ACNS-2007 will be held in September of 2007 (St. Petersburg, Russia). 5. International Workshop of Central and Eastern Europe on Multi-agent Systems (CEEMAS): 1999. 6-7. International Workshop on Autonomous Intelligent Systems: Agents and Data Mining (AIS-ADM): June 2005 (Proceedings in LNAI of Springer, vol.3505), AIS-ADM-2007 will be held in June of 2007 (St. Petersburg, Russia).

  15. Distributed Data Mining and Decision Making – related Publications V.Gorodetsky, O.Karsaev and V.Samoilov. On-Line Update of Situation Assessment: Generic Approach. In International Journal of Knowledge-Based & Intelligent Engineering Systems.IOS Press, Netherlands, 2005, V.Samoylov, V.Gorodetsky. Ontology Issue in Multi–Agent Distributed Learning. In V.Gorodetsky, J.Liu, V. Skormin (Eds.). Autonomous Intelligent Systems: Agents and Data Mining.Lecture Notes in Artificial Intelligence, vol. 3505, 2005, 215-230. O.Karsaev. Technology of Agent-Based Decision Making System Development. In V.Gorodetsky, J.Liu, V. Skormin (Eds.). Autonomous Intelligent Systems: Agents and Data Mining. Lecture Notes in Artificial Intelligence, vol. 3505, 2005, 107-121. V.Gorodetsky, O.Karsaev and V.Samoilov. Direct Mining of Rules from Data with Missing Values. Studies in Computational Intelligence, Volume 6, Chapter in book T.Y.Lin, S.Ohsuga, C.J. Liau, X.T.Hu, S.Tsumoto (Eds.). Foundation of Data Mining and Knowledge Discovery, Springer, 2005, 233-264 V.Gorodetsky, O.Karsaev, V.Samoylov, A.Ulanov. Asynchronous Alert Correlation in Multi-Agent Intrusion Detection Systems, Lecture Notes in Computer Science, Vol.3685, Springer, 2005, 366-379

  16. Distributed Data Mining and Decision Making – related Publications V.Gorodetsky, O.Karsaev, V.Samoilov, and A.Ulanov. Multi-Agent Framework for Intrusion Detection and Alert Correlation. NATO ARW Workshop "Security of Embedded Systems", Patras, Greece, August 22-26, 2005. In Proceedings of the Workshop, IOS Press, 2005. V.Gorodetsky, O.Karsaev, and V.Samoilov. On-Line Update of Situation Assessment Based on Asynchronous Data Streams. In M.Negoita, R.Howlett, L.Jain (Eds.) Knowledge-Based Intelligent Information and Engineering Systems, Lecture Notes in Artificial Intelligence, vol. 3213, Springer Verlag, 2004, pp.1136–1142 (Received The Best Paper Award) V.Gorodetsky, O.Karsaev, V.Samoilov. Multi-agent and Data Mining Technologies for Situation Assessment in Security Related Application. In B.Dunin-Keplicz, A. Jankovski, A.Skowron, M.Szczuka (Eds.) Monitoring, Security, and Rescue Techniques in Multi-agent Systems. Series of books Advances in Soft Computing, Springer, 2004, 411-422. V.Gorodetsky, O.Karsaev, I.Kotenko, and V.Samoilov. Multi-Agent Information Fusion: Methodology, Architecture and Software Tool for Learning of Object and Situation Assessment. International Conference "Fusion-04", Stockholm, 2004, pp. 346–353

  17. Distributed Data Mining and Decision making – related Publications V.Gorodetsky, O.Karsaev, and V.Samoilov. Distributed Learning of Information Fusion: A Multi-agent Approach. Proceedings of the International Conference "Fusion 03", Cairns, Australia, July 2003, 318–325. V.Gorodetsky, O.Karsaeyv, and V.Samoilov. Multi-agent Technology for Distributed Data Mining and Classification. Proceedings of the IEEE Conference Intelligent Agent Technology (IAT03), Halifax, Canada, October 2003, 438–441. V.Gorodetsky, O.Karsaev, and V.Samoilov. Software Tool for Agent-Based Distributed Data Mining. Proceedings of the IEEE Conference Knowledge Intensive Multi-agent Systems (KIMAS 03), Boston, USA, October 2003, 710–715, etc.

  18. Contact data For more information and related publications please contact E-mail: gor@mail.iias.spb.su http://space.iias.spb.su/ai/gorodetsky

  19. Future Research and Development in Agent and Data Mining Area Vladimir Gorodetsky Head of Laboratory of Intelligent Systems http://space.iias.spb.su/ai/ gor@mail.iias.spb.su

  20. Focus of the Laboratory Current and Forthcoming Research Projects The main idea: From hierarchical agent-based distributed decision making to P2P (serverless) ad-hoc agent-based service-oriented decision making networks 1. Algorithms for P2P rule extraction from distributed data sources with overlapping attributes -- DDM area. 2. P2P Agent platform –Agent area (now it is subject of activity of FIPA Nomadic Agent Working Group). 3. Software tool kit supporting agent-based P2P rule extraction from distributed data sources – integrated area

  21. Example: Hierarchical Architecture of Distributed Decision Making for Intrusion Detection Task NETWORL TRAFFIC Preprocessing procedures Data Source 1 Data Source 2 Data Source 3 Data Source 4 Data Source 5 Source-based classifiers Source-based classifiers Source-based classifiers Source-based classifiers Source-based classifiers Decision stream 4 Decision stream 1 Decision stream 2 Decision stream 3 Decision stream 5 Input: composition of asynchronous data streams Two-level meta-classification Computer security status: {Normal or attack of a class} Output:

  22. Hierarchical Architecture: Multi-Agent IDSIntended for Heterogeneous Alert Correlation Heterogeneous alerts notify about various classes of attacks, either DoS, or Probe, or U2R Preprocessing procedures NETWORK TRAFFIC

  23. P2Pclassifiers Data sources P2P Architecture of Distributed Decision Making for Intrusion Detection Task: Example : Serverless (P2P) network for intrusion detection (no meta-classifiers).Each agent detecting an alert acts as combiner of decisions provided by other agents (“service providers”) on its request

  24. Ground Object Recognition Based on Infra Red Images Produced by Airborne Equipment Infra red data preprocessing and their transformation into feature spaces Object recognition components of the agent-based software Object models (set of features) Scale Invariant Feature Transform (SIFT) Recognized object 2D Views SIFT 1 Classifier 1 Model 1 Meta-agent SIFT 2 Classifier 2 Model 2 Wavelet Transform (WT) Decision combining WT 1 Classifier 3 Model 3 … … WT 2 Structural Description (SD) Classifier 16 Model 16 SD 1 SD 2 Agent-classifiers Objects’ models The Task: On-line automatic recognition of ground objects based on infra-red images perceived by airborne surveillance system.

  25. Ground Object Recognition: Structure of Decision Making and Decision Combining Meta-classifier combining decision of particular meta-classifiers Recognized objects Combined decision of the classifiers trained to detect the object class1 Combined decision of the classifiers trained to detect the object class M60 3-SIFT-based Object of class 1 - right 2-SIFT-basedObject of class 1 - right 2–SIFT-based Object of class 2-left 3–SIFT-based Object of class 2-left Combined decision of the classifiers trained to detect the object class3 2–SIFT-based Object of class 2-right 3–SIFT-based Object of class 2-right 2–SIFT-based Object of class 3- front 3–SIFT-based Object of class 3- front Combined decision of the classifiers trained to detect the object class4 3–SIFT-based Object of class 3- right 2–SIFT-based Object of class 3- right 2–SIFT-based Object of class 4-front 3–SIFT-based Object of class 4-front 2–SIFT-based Object of class 3- back 3–SIFT-based Object of class 3- back 2–SIFT-based Object of class 4–l eft 3–SIFT-based Object of class 4-left

  26. Agent-based P2P Classification Network Implementing Ground Object Recognition System Agent providing user interface

  27. Software Prototype of Agent-based Service- oriented P2P Classification Network for Ground Object Recognition The main window of the user interface of the P2P classification network for ground object recognition

  28. Agent 1-1 Agent 1-1 Agent 1-2 Agent 1-2 Agent 1-k Agent 1-k … … PEER 1 PEER 1 P2P agent platform P2P agent platform Existing P2P networking middleware Existing P2P networking middleware Architecture of Agent-based Service-oriented P2P Network … Network Transport General requirements to P2P agent platform architecture are formulated in the document of Nomadic Agent Working Group (NAWG) of FIPA. Our expected contribution is a version of its implementation and verification (via software prototyping on the basis of particular classification networks).

  29. Architecture of a Peer of Agent-based Service-oriented P2P Network Agent 1-1 Agent 1-2 Agent 1-k … OnReceive Handler OnReceive Handler OnReceive Handler Transport System (TCP/IP) (UDP) … interface PEER : P2P Agent Platform instance Message Transport System Interface Existing P2P networking middleware OnReceive Handler OnReceive Handler Routing Book Interface AMS (dll, Agent) Message history Interface Yellow Pages (dll, Agent) Agent book Peer Address book Search Results Service book Search Results

  30. Hot Problems 1. Development of P2P agent platformdecoupling peers and applications and supporting open service–oriented architectures, self–optimization of the network structure through on-line learning. Although the last problem is currently the subject of the intensive research in the networking scope, for agent-based architecture it will require specific efforts. 2. Combining of decisions produced by P2P agents within distributed heterogeneous environment. A peculiarity of this task is that in each particular case, the classifications incoming from the peers may be very diverse in the sense that different peers may be involved in service provision. That is why, distributed learning of decision combing that is a challenging task of P2P data mining and ubiquitous computing should be an important component of the technology in question.

  31. Contact data For more information and related publications please contact E-mail: gor@mail.iias.spb.su http://space.iias.spb.su/ai/gorodetsky

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