The self-non-self theory has dominated immunology since the 1950s. In the 1990s, Matzinger and her colleagues suggested a new, competing theory, called the "danger theory." This theory has provoked mixed acclaim: enthusiasm and criticism. Here we assess the danger theory vis-à-vis recent experimental data on innate immunity, transplantation, cancers and tolerance to foreign entities, and try to elucidate more clearly whether danger is well defined.
The “danger theory” of Polly Matzinger posed in 1994 (Matzinger, 1994) principally holds that the immune system is far less concerned with “foreign” (microorganisms, cells, and molecules) than with signals that cause dangerous damage, and, furthermore, that antigen-presenting cel
Within immunology, new theories are constantly being proposed that challenge current ways of thinking. These include new theories regarding how the immune system responds to pathogenic material. This conceptual paper takes one relatively new such theory: the Danger theory, and ex
* We propose an artificial immune model for intrusion detection in distributed systems based on a relatively recent theory in immunology called Danger theory. Based on Danger theory, immune response in natural systems is a result of sensing corruption as well as sensing unknown s
An artificial immune system algorithm, based on the Danger Theory, is an algorithm which emulates, in a computational context, the defense mechanism of the human immune system when presented with a danger to its wellbeing. Over its evolution the human immune system has developed
The natural immune system exhibits many properties that are of interest to the area of Web mining. Of particular interest is the dynamic nature of the immune system when compared with the dynamic nature of mining information from the Web. As part of a larger project to construct
Existing artificial immune optimization algorithms reflect a number of shortcomings, such as premature convergence and poor local search ability. This paper proposes a danger-theory-based immune network optimization algorithm, named dt-aiNet. The danger theory emphasizes that dan