[Google Scholar Profile]

Editor

Pietro Liò, Giuseppe Nicosia, Thomas Stibor (Eds.): Artificial Immune Systems - 10th International Conference, ICARIS 2011, Cambridge, UK, July 18-21, 2011. Proceedings. Lecture Notes in Computer Science 6825, Springer 2011, ISBN 978-3-642-22370-9.

Publications

  • Han Xiao, Thomas Stibor and Claudia Eckert. Query Algorithm for Near-Optimal Evasion of Multi-Class Linear Classifier. In 16th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Kuala Lumpur, Malaysia, 2012. Springer-Verlag. [Download PDF]
  • Han Xiao and Thomas Stibor. A Supervised Topic Transition Model for Detecting Malicious System Call Sequences. In KDD Workshop on Knowledge Discovery, Modeling, and Simulation, San Diego, USA, 2011, ACM Press. [Download PDF] (Best Student Paper Award).
  • Han Xiao and Thomas Stibor. Toward Artificial Synesthesia: Linking Images and Sounds via Words. In NIPS Workshop on Machine Learning for Next Generation Computer Vision Challenges, Whistler, Canada, 2010. [Download PDF]
  • Han Xiao and Thomas Stibor. Efficient Collapsed Gibbs Sampling For Latent Dirichlet Allocation. In 2nd Asian Conference on Machine Learning (ACML), Tokyo, Japan, 2010. JMLR: Workshop and Conference Proceedings. [Download PDF]
  • Thomas Stibor and Anastasio Salazar-Banuelos. On Immunological Memory as a Function of a Recursive Proliferation Process. In 15th IEEE International Conference on Engineering of Complex Computer Systems (ICECSS), Oxford, England, 2010. IEEE Press. [Download PDF]
  • Thomas Stibor. A Study of Detecting Computer Viruses in Real-Infected Files in the n-gram Representation with Machine Learning Methods. In 23rd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems (IEA-AIE), Lecture Notes in Artificial Intelligence, 2010. Springer. [Download PDF]
  • Thomas Stibor. Foundations of r-contiguous Matching in Negative Selection for Anomaly Detection. Natural Computing, 8(3):613-641, September 2009, Springer. [Download PDF]
  • Thomas Stibor, Robert Oates, Graham Kendall and Jonathan M. Garibaldi. Geometrical Insights into the Dendritic Cell Algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 1275-1282, Montreal, Canada, July 2009, ACM Press. [Download PDF]
  • Jon Timmis, Andrew Hone, Thomas Stibor and Ed Clark. Theoretical Advances in Artificial Immune Systems. Theoretical Computer Science, 403(1):11-32, August 2008, Elsevier. [Download PDF]
  • Thomas Stibor. Discriminating Self from Non-Self with Finite Mixtures of Multivariate Bernoulli Distributions. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 127-134, Atlanta, GA, USA, July 2008. ACM Press. [Download PDF]
  • Thomas Stibor. An Empirical Study of Self/Non-Self Discrimination in Binary Data with a Kernel Estimator. In Proceedings of the 7th International Conference on Artificial Immune Systems (ICARIS), pages 352-363, Phuket, Thailand, August 2008, Springer. [Download PDF]
  • Thomas Stibor and Jon Timmis. Comments on Real-Valued Negative Selection vs. Real-Valued Positive Selection and One-Class SVM. In Proceedings of the Congress on Evolutionary Computation (CEC), pages 3727-3734, Singapore, September 2007, IEEE Press. [Download PDF]
  • Thomas Stibor and Jon Timmis. An Investigation on the Compression Quality of aiNet. In IEEE Symposium on Foundations of Computational Intelligence (FOCI), pages 495-502, Hawaii, USA, 2007, IEEE Press. [Download PDF]
  • Thomas Stibor. Phase Transition and the Computational Complexity of Generating r-contiguous Detectors. In Proceedings of the 6th International Conference on Artificial Immune Systems (ICARIS), Lecture Notes in Computer Science, pages 142-155, Santos/SP, Brazil, 2007, Springer. [Download PDF] (Best Paper Award).
  • Thomas Stibor. On the Appropriateness of Negative Selection for Anomaly Detection and Network Intrusion Detection. PhD Thesis, Technische Universität Darmstadt, March 2006. [Download PDF]
  • Thomas Stibor, Jon Timmis and Claudia Eckert. The Link between r-contiguous Detectors and k-CNF Satisfiability. In Proceedings of the Congress on Evolutionary Computation (CEC), Vancouver, Canada, July 2006, IEEE Press. [Download PDF]
  • Thomas Stibor, Jon Timmis and Claudia Eckert. Generalization Regions in Hamming Negative Selection. In Intelligent Information Systems (IIS), Advances in Soft Computing, pages 447-456, Ustron, Poland, June 2006, Springer. [Download PDF]
  • Thomas Stibor, Jon Timmis and Claudia Eckert. On the Use of Hyperspheres in Artificial Immune Systems as Antibody Recognition Regions. In Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS), volume 4163 of Lecture Notes in Computer Science, pages 215-228, Oeiras, Portugal, 2006, Springer. [Download PDF]
  • Thomas Stibor, Jon Timmis and Claudia Eckert. On Permutation Masks in Hamming Negative Selection. In Proceedings of the 5th International Conference on Artificial Immune Systems (ICARIS), volume 4163 of Lecture Notes in Computer Science, pages 122-135, Oeiras, Portugal, 2006, Springer. [Download PDF]
  • Thomas Stibor, Jon Timmis and Claudia Eckert. Artificial Immune Systems for IT-Security. It-Information Technology (Systems Biology and Information Technology), 48(3):168-173, 2006. [Download PDF]
  • Thomas Stibor, Jon Timmis and Claudia Eckert. On the Appropriateness of Negative Selection defined over Hamming Shape-Space as a Network Intrusion Detection System. In Proceedings of the Congress on Evolutionary Computation (CEC), Edinburgh, UK, September 2005, IEEE Press. [Download PDF]
  • Thomas Stibor, Jon Timmis and Claudia Eckert. A Comparative Study of Real-Valued Negative Selection to Statistical Anomaly Detection Techniques. In Proceedings of the 4th International Conference on Artificial Immune Systems (ICARIS), volume 3627 of Lecture Notes in Computer Science, pages 262-275, Banff, Canada, 2005, Springer. [Download PDF] (Best Paper Award).
  • Thomas Stibor, Philipp Mohr, Jon Timmis and Claudia Eckert. Is Negative Selection Appropriate for Anomaly Detection?. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pages 321-328, Washington, D.C., 2005, ACM Press. [Download PDF]
  • Thomas Stibor, Kpatcha M. Bayarou and Claudia Eckert. An Investigation of R-Chunk Detector Generation on Higher Alphabets. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), volume 3102 of Lecture Notes in Computer Science, pages 299-307, Seattle, WA, USA, June 26-30, 2004, Springer. [Download PDF]