21,095 research outputs found
A Computer Virus Propagation Model Using Delay Differential Equations With Probabilistic Contagion And Immunity
The SIR model is used extensively in the field of epidemiology, in
particular, for the analysis of communal diseases. One problem with SIR and
other existing models is that they are tailored to random or Erdos type
networks since they do not consider the varying probabilities of infection or
immunity per node. In this paper, we present the application and the simulation
results of the pSEIRS model that takes into account the probabilities, and is
thus suitable for more realistic scale free networks. In the pSEIRS model, the
death rate and the excess death rate are constant for infective nodes. Latent
and immune periods are assumed to be constant and the infection rate is assumed
to be proportional to I (t) N(t), where N (t) is the size of the total
population and I(t) is the size of the infected population. A node recovers
from an infection temporarily with a probability p and dies from the infection
with probability (1-p).Comment: International Journal of Computer Networks & Communications (IJCNC)
Vol.6, No.5, September 201
Immune System Approaches to Intrusion Detection - A Review (ICARIS)
The use of artificial immune systems in intrusion detection is an appealing
concept for two reasons. Firstly, the human immune system provides the human
body with a high level of protection from invading pathogens, in a robust,
self-organised and distributed manner. Secondly, current techniques used in
computer security are not able to cope with the dynamic and increasingly
complex nature of computer systems and their security. It is hoped that
biologically inspired approaches in this area, including the use of
immune-based systems will be able to meet this challenge. Here we collate the
algorithms used, the development of the systems and the outcome of their
implementation. It provides an introduction and review of the key developments
within this field, in addition to making suggestions for future research.Comment: Proceedings of the 3rd International Conference on Artificial Immune
Systems (ICARIS), 316-329, 200
Towards a Conceptual Framework for Innate Immunity
Innate immunity now occupies a central role in immunology. However,
artificial immune system models have largely been inspired by adaptive not
innate immunity. This paper reviews the biological principles and properties of
innate immunity and, adopting a conceptual framework, asks how these can be
incorporated into artificial models. The aim is to outline a meta-framework for
models of innate immunity.Comment: 14 pages, 5 figures, 2 tables, 4th International Conference on
Artificial Immune Systems (ICARIS 2005
Scale Invariance of Immune System Response Rates and Times: Perspectives on Immune System Architecture and Implications for Artificial Immune Systems
Most biological rates and times decrease systematically with organism body
size. We use an ordinary differential equation (ODE) model of West Nile Virus
in birds to show that pathogen replication rates decline with host body size,
but natural immune system (NIS) response rates do not change systematically
with body size. This is surprising since the NIS has to search for small
quantities of pathogens through larger physical spaces in larger organisms, and
also respond by producing larger absolute quantities of antibody in larger
organisms. We call this scale-invariant detection and response. We hypothesize
that the NIS has evolved an architecture to efficiently neutralize pathogens.
We investigate a range of architectures using an Agent Based Model (ABM). We
find that a sub-modular NIS architecture, in which lymph node number and size
both increase sublinearly with body size, efficiently balances the tradeoff
between local pathogen detection and global response using antibodies. This
leads to nearly scale-invariant detection and response, consistent with
experimental data. Similar to the NIS, physical space and resources are also
important constraints on Artificial Immune Systems (AIS), especially
distributed systems applications used to connect low-powered sensors using
short-range wireless communication. We show that AIS problems, like distributed
robot control, will also require a sub-modular architecture to efficiently
balance the tradeoff between local search for a solution and global response or
proliferation of the solution between different components. This research has
wide applicability in other distributed systems AIS applications.Comment: 23 pages, 4 figures, Swarm Intelligence journa
Artificial Immune Systems (INTROS 2)
The biological immune system is a robust, complex, adaptive system that
defends the body from foreign pathogens. It is able to categorize all cells (or
molecules) within the body as self or non-self substances. It does this with
the help of a distributed task force that has the intelligence to take action
from a local and also a global perspective using its network of chemical
messengers for communication. There are two major branches of the immune
system. The innate immune system is an unchanging mechanism that detects and
destroys certain invading organisms, whilst the adaptive immune system responds
to previously unknown foreign cells and builds a response to them that can
remain in the body over a long period of time. This remarkable information
processing biological system has caught the attention of computer science in
recent years.
A novel computational intelligence technique, inspired by immunology, has
emerged, called Artificial Immune Systems. Several concepts from the immune
system have been extracted and applied for solution to real world science and
engineering problems. In this tutorial, we briefly describe the immune system
metaphors that are relevant to existing Artificial Immune Systems methods. We
will then show illustrative real-world problems suitable for Artificial Immune
Systems and give a step-by-step algorithm walkthrough for one such problem. A
comparison of the Artificial Immune Systems to other well-known algorithms,
areas for future work, tips & tricks and a list of resources will round this
tutorial off. It should be noted that as Artificial Immune Systems is still a
young and evolving field, there is not yet a fixed algorithm template and hence
actual implementations might differ somewhat from time to time and from those
examples given here.Comment: Search Methodologies: Introductory Tutorials in Optimization and
Decision Support Techniques, 2nd edition, Springer, Chapter 7, 2014. arXiv
admin note: substantial text overlap with arXiv:0803.3912, arXiv:0910.4899,
arXiv:0801.431
The pandemic of viruses with a long incubation phase in the small world
A model of the spread of viruses in selected city and in a network of cities
is considered, taking into account the delay caused by the long incubation
period of the virus. The effect of delay effects is shown in comparison with
pandemics without such delay. A temporary asymmetry of the spread of infection
has been identified, which means that the time for a pandemic to develop
significantly exceeds the time for its completion. Model calculations of the
spread of viruses in a network of interconnected large and small cities were
carried out, and dynamics features were revealed in comparison with the spread
of viruses in a single city, including the possibility of reinfection of
megalopolis
The Danger Theory and Its Application to Artificial Immune Systems
Over the last decade, a new idea challenging the classical self-non-self
viewpoint has become popular amongst immunologists. It is called the Danger
Theory. In this conceptual paper, we look at this theory from the perspective
of Artificial Immune System practitioners. An overview of the Danger Theory is
presented with particular emphasis on analogies in the Artificial Immune
Systems world. A number of potential application areas are then used to provide
a framing for a critical assessment of the concept, and its relevance for
Artificial Immune Systems
Artificial Immune Systems
The biological immune system is a robust, complex, adaptive system that
defends the body from foreign pathogens. It is able to categorize all cells (or
molecules) within the body as self-cells or non-self cells. It does this with
the help of a distributed task force that has the intelligence to take action
from a local and also a global perspective using its network of chemical
messengers for communication. There are two major branches of the immune
system. The innate immune system is an unchanging mechanism that detects and
destroys certain invading organisms, whilst the adaptive immune system responds
to previously unknown foreign cells and builds a response to them that can
remain in the body over a long period of time. This remarkable information
processing biological system has caught the attention of computer science in
recent years. A novel computational intelligence technique, inspired by
immunology, has emerged, called Artificial Immune Systems. Several concepts
from the immune have been extracted and applied for solution to real world
science and engineering problems. In this tutorial, we briefly describe the
immune system metaphors that are relevant to existing Artificial Immune Systems
methods. We will then show illustrative real-world problems suitable for
Artificial Immune Systems and give a step-by-step algorithm walkthrough for one
such problem. A comparison of the Artificial Immune Systems to other well-known
algorithms, areas for future work, tips & tricks and a list of resources will
round this tutorial off. It should be noted that as Artificial Immune Systems
is still a young and evolving field, there is not yet a fixed algorithm
template and hence actual implementations might differ somewhat from time to
time and from those examples given here.Comment: 29 pages,4 figures
Sample NLPDE and NLODE Social-Media Modeling of Information Transmission for Infectious Diseases:Case Study Ebola
We investigate the spreading of information through Twitter messaging related
to the spread of Ebola in western Africa using epidemic based dynamic models.
Diffusive spreading leads to NLPDE models and fixed point analysis yields
systems of NLODE models. When tweets are mapped as connected nodes in a graph
and are treated as a time sequenced Markov chain, TSMC, then by the Kurtz
theorem these specific paths can be identified as being near solutions to
systems of ordinary differential equations that in the large N limit retain
many of the features of the original Tweet dynamics. Constraints on the model
related to Tweet and re-Tweet rates lead to different versions of the system of
equations. We use Ebola Twitter meme based data to investigate a modified four
parameter model and apply the resulting fit to an accuracy metric for a set of
Ebola memes. In principle the temporal and spatial evolution equations
describing the propagation of the Twitter based memes can help ascertain and
inform decision makers on the nature of the spreading and containment of an
epidemic of this type.Comment: 14 pages, 4 tables, 2 figure
Artificial Immune Systems (2010)
The human immune system has numerous properties that make it ripe for
exploitation in the computational domain, such as robustness and fault
tolerance, and many different algorithms, collectively termed Artificial Immune
Systems (AIS), have been inspired by it. Two generations of AIS are currently
in use, with the first generation relying on simplified immune models and the
second generation utilising interdisciplinary collaboration to develop a deeper
understanding of the immune system and hence produce more complex models. Both
generations of algorithms have been successfully applied to a variety of
problems, including anomaly detection, pattern recognition, optimisation and
robotics. In this chapter an overview of AIS is presented, its evolution is
discussed, and it is shown that the diversification of the field is linked to
the diversity of the immune system itself, leading to a number of algorithms as
opposed to one archetypal system. Two case studies are also presented to help
provide insight into the mechanisms of AIS; these are the idiotypic network
approach and the Dendritic Cell Algorithm.Comment: 29 pages, 1 algorithm, 3 figures, Handbook of Metaheuristics, 2nd
Edition, Springe
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