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Research Information

Please  browse the information below to see several of our ongoing projects and  research interests.

           Intrusion Detection using Learning Machines, Feature Ranking and            Selection for IDS, and Information Hiding:

    • Professors:

      • Andrew Sung
      • Lorie Liebrock
    • Students:

      • Srinivas Mukkamala
      • Guadalupe Janoski
      • Gopalakrishna Reddy Tadiparthi
      • Anthonius Sulaiman
      • Dennis Xu
      • Koganti Mohan Krishna
      • Ben Edwards

    • Description:

      • Intrusion detection, stegnography, and steganlysis.

    • Papers:

      • A.H. Sung, S. Mukkamala. (2003) Information Assurance. Proceedings of IEEE International Conference on Information Reuse and Integration, IEEE Computer Society Press, Tutorials.
      • S. Mukkamala, A.H. Sung. (2003) Artificial Intelligent Techniques for Intrusion Detection. Proceedings of IEEE International Conference Systems, Man, and Cybernetics, IEEE Computer Society Press, pp.1231-1236.
      • A.H. Sung, S. Mukkamala. (2003) Need for Information Assurance and Security. Proceedings of IEEE International Conference Systems, Man, and Cybernetics, IEEE Computer Society Press, Tutorials.
      • A.H. Sung, S. Mukkamala. (2003) Information Assurance. Proceedings of the Third International Conference on Intelligent Systems Design and Applications, Intelligent Systems Design and Applications, Advances in Soft Computing, Springer Verlag, Germany, Tutorials.
      • S. Mukkamala. (2003) Information Assurance and Security Awareness. City of Socorro Councilors Meeting.
      • S Mukkamala, A H. Sung. (2003) Soft Computing Techniques for Intrusion Detection. Proceedings of Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection, Johns Hopkins University. ONR and AFSOR Sponsored Workshop on Intrusion Detection. [http://www.galaxy.gmu.edu/intrusiondetection/2003schedule]
      • G. K. Tadiparthi, S. Mukkamala, A. H. Sung. (2003) Robust Steganography Using Animations. Proceedings of the 7th IASTED International Conference on Internet and Multimedia Systems and Applications, pp. 467-472.
      • J. Xu, A. H. Sung, P. Shi, Q. Liu. (2003) Text Steganography Using Wavelet Transform. Proceedings of the 7th IASTED International Conference on Internet and Multimedia Systems and Applications, August 13-15, pp. 473-478.
      • S. Mukkamala, A. H. Sung, A. Abraham. (2003) Intrusion Detection Using Ensemble of Soft Computing Paradigms. Proceedings of the Third International Conference on Intelligent Systems Design and Applications, Intelligent Systems Design and Applications, Advances in Soft Computing, Springer Verlag, Germany, pp. 239-248.
      • G. K. Tadiparthi, S. Mukkamala, A. H. Sung. (2003) A Steganography Database. Proceedings of Digital Forensics Research Workshop, Cleveland.
      • S. Mukkamala, A. H. Sung. (2003) Identifying Significant Features for Network Forensic Analysis Using Artificial Intelligence Techniques. In International Journal on Digital Evidence, IJDE Volume 1, Issue 4.
      • S. Mukkamala, A.H. Sung. (2003) Feature Selection for Intrusion Detection Using Neural Networks and Support Vector Machines. To appear in Journal of the Transportation Research Board (of the National Academies).
      • S. Mukkamala, A.H. Sung. (2003) Detecting Denial of Service Attacks Using Support Vector Machines. Proceedings of IEEE International Conference on Fuzzy Systems, IEEE Computer Society Press, pp.1231-1236.
      • A H. Sung, S. Mukkamala. (2003) Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks. In Proceedings of the 2003 International Symposium on Applications and the Internet Technology, IEEE Computer Society Press, pp.209-216.
      • S Mukkamala, A H. Sung. (2003) Feature Selection for Intrusion Detection Using Neural Networks and Support Vector Machines. Proceedings of the 82nd Annual Meeting of the Transportation Research Board, National Academics.
      • S. Mukkamala. (2002) Computer Security. IEEE Computer Society ezine Looking. Forward.
      • S. Mukkamala. (2002) Mining Key Features from Network Audit Trails for Intrusion Detection Systems Using Machine Learning Techniques. Criminal Intelligence Digest, Central Bureau of Investigation.
      • S. Mukkamala, A H. Sung. (2002) Performance Based Feature Identification for Intrusion Detection Using Support Vector Machines. Proceedings of Second International Conference on Hybrid Intelligent Systems, published as Soft Computing Systems, Design, Management, and Applications, Frontiers in Artificial Intelligence and Applications Series, Vol. 87, IOS Press, pp.351-361.
      • S Mukkamala, A H. Sung, S. Veeramachaneni. (2002) Learning Machines for Intrusion Detection. Proceedings of ANNIE: Artificial Neural Networks in Engineering, published as Intelligent Engineering Systems through Artificial Neural Networks, Vol. 12 (C. H. Dagli, et al. Editors), ASME Press, pp.465-470.
      • S Mukkamala, A H. Sung. (2002) Identifying Key Features for Intrusion Detection Using Neural Networks. Proceedings of 15th International Conference on Computer Communications, pp. 1132-1138.
      • Srinivas Mukkamala, Andrew H. Sung. (2002) Audit Data Reduction Using Neural Networks and Support Vector Machines. Proceedings of Digital Forensics Research Workshop, August 7-9, Syracuse University, Center for Systems Assurance.
      • S Mukkamala, A H. Sung. (2002) Learning machines for Intrusion Detection: Support Vector Machines and Neural Networks. Proceedings of International Conference on Security and Management, pp.525-531.
      • S Mukkamala, A H. Sung. (2002) Feature ranking and Selection for Intrusion detection Systems. Proceedings of International Conference on Information and Knowledge Engineering, pp.503-509.
      • S Mukkamala, A H. Sung. (2002) Comparison of Neural Networks and Support Vector Machines in Intrusion Detection. Proceedings of Workshop on Statistical and Machine Learning Techniques in Computer Intrusion Detection, Johns Hopkins University. [http://www.mts.jhu.edu/~cidwkshop/abstracts.html]
      • S. Mukkamala, G. Janoski, A H. Sung. (2002) Intrusion Detection Using Neural Networks and Support Vector Machines. Proceedings of IEEE International Joint Conference on Neural Networks, IEEE Computer Society Press, pp.1702-1707.
      • Srinivas Mukkamala, Guadalupe Janoski, Andrew H. Sung. (2002) Intrusion Detection Using Support Vector Machines. Proceedings of Advanced Simulation Technologies Conference, pp.178-183.
      • G. Janoski, S. Mukkamala, A.H. Sung. (2002) A Comparison of Soft Computing Methods for Reservoir Simulation, Soft Computing and Industry – Recent Applications, Springer Verlag, ISBN 1-85233-539-4, pp.821-832.
      • S Mukkamala, G Janowski, A H. Sung. (2001) Intrusion Detection Using Neural Networks and Support Vector Machines. Proceedings of Hybrid Information Systems Advances in Soft Computing, Physica Verlag, Springer Verlag, ISBN 3790814806, pp.121-138.
      • S Mukkamala, G Janowski, A H. Sung. (2001) Monitoring Information System Security. Proceedings of the 11th Annual Workshop on Information Technologies & Systems, pp.139-144.
      • G. Janoski, N. Benslimame, S. Mukkamala, and A. Sung. (2001) Using Neural Networks for System Security Monitoring. Proceedings of International Conference Intelligent Systems and Control, pp. 360-365.
      • G. Janoski, S. Mukkamala, A.H. Sung. (2001) A Comparison of Soft Computing Methods for Reservoir Simulation, World Conference on Soft Computing in Industrial Applications (WSC6), (selected as a best paper )

    • Demonstrations:

      • The steganography section is temporarily unavailable
        for updates.

           Efficient and Secure Storing/Communicating Information Utilizing            Fiber Optics infrastructure, Image/Data  Compression, and  Secure            Dynamic Encryption:

    • Professors:

      • Hamdy Soliman
    • Students:
      • Mohamed Omari (Ph.D.)
      • Chad Peyton (Ph.D.)
      • Sailaja Mummidi  (M.S)
      • Jason Coombs  (M.S.)
      • Clark Poore  (M.S.)
      • Yu Feng (M.S.)

 
 
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