Title :
Artificial Immune System for Anomaly Detection
Author_Institution :
Dept. of Electron. Eng., Huaihai Inst. of Technol., Lianyungang
Abstract :
One significant feature of the theory immunology is the ability to adapt to changing environments and dynamically learning continuously. Based on this idea, artificial immune systems (AISs) provide an ideal inspiration for computer security in general and intrusion detection systems (IDSs) in particular. A hybrid immune learning algorithm is presented in this paper with the aim of combining the advantages of real-valued negative selection algorithm (RNSA) and a classification algorithm. The basic idea is to use the RNSA algorithm to generate non-self samples. Then, apply a classification algorithm to find a characteristic function of the self (or non-self). This algorithm allows the application of a supervised learning technique even when samples from only one class (normal) are available.
Keywords :
artificial immune systems; learning (artificial intelligence); security of data; anomaly detection; artificial immune system; classification algorithm; computer security; hybrid immune learning algorithm; intrusion detection systems; real-valued negative selection algorithm; supervised learning technique; Artificial immune systems; Biological system modeling; Biology computing; Classification algorithms; Computational modeling; Computer networks; Computer security; Immune system; Information security; Intrusion detection; anomaly detection; artificial immune system; intrusion detection systems; real-valued negative selection algorithm;
Conference_Titel :
Knowledge Acquisition and Modeling Workshop, 2008. KAM Workshop 2008. IEEE International Symposium on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-3530-2
Electronic_ISBN :
978-1-4244-3531-9
DOI :
10.1109/KAMW.2008.4810493
