An immunity-based technique to characterize intrusions in computer networks

D. Dasgupta, F. Gonzalez
2002 IEEE Transactions on Evolutionary Computation  
This paper presents a technique inspired by the negative selection mechanism of the immune system that can detect foreign patterns in the complement (nonself) space. In particular, the novel pattern detectors (in the complement space) are evolved using a genetic search, which could differentiate varying degrees of abnormality in network traffic. The paper demonstrates the usefulness of such a technique to detect a wide variety of intrusive activities on networked computers. We also used a
more » ... ve characterization method based on a nearest-neighbor classification. Experiments are performed using intrusion detection data sets and tested for validation. Some results are reported along with analysis and concluding remarks. , TN. His current research interests include scientific computing, tracking real-world problems through interdisciplinary cooperation, artificial intelligence, genetic algorithms, neural networks, immunocomputing, and their applications. He serves as a program committee member in many international conferences, has organized special tracks and workshops on artificial immune systems, and offers tutorials on the topics at international conferences since. He has authored or coauthored more than 70 papers in book chapters, journals, and international conferences, and has edited the book Artificial Immune Systems and Their Applications (New York: Springer-Verlag, 1999). Dr. Dasgupta is a Member of the ACM. Fabio González (S'02) received the B.S. degree in systems engineering and the M.Sc. degree in mathematics from the National
doi:10.1109/tevc.2002.1011541 fatcat:skkebk4yp5a7philtjlsslug5q