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Immune systems in computer science 
Imunski sistemi v računalništvu  
Karin Ljubič 
1,
*, Iztok Fister Jr. 
2 
in Iztok Fister 
3
  
1
 Medical student / University of Maribor, Faculty of Medicine, Taborska 8, 2000 Maribor  
2
 Technical staff / University of Maribor, Faculty of Electrical Engineering and Computer Science, Smetanova  
  17, 2000 Maribor 
3
 Assistant professor / University of Maribor, Faculty of Electrical Engineering and Computer Science,  
  Smetanova 17, 2000 Maribor  
E-Mails: lifeisforliving1@gmail.com ; iztok.fister2@uni-mb.si; iztok.fister@uni-mb.si 
 
Abstract: A simplified description of the immune system is as follows: this is an organic system 
intended for protecting the host organism from the threats posed to it from pathogens and toxic 
substances. The architecture of the immune system is such that a series of defensive layers protect the 
host. Once the pathogen makes it inside the host, it must contend with the innate and adaptive immune 
system. These two immunological sub-systems are comprised of several types of cells and molecules 
produced by specialized organs and processes to address the self non-self problem. Artificial Immune 
Systems (AIS) are computational models, based on natural immune systems. They tend to solve 
specific problems in computer science by resembling the natural mechanisms. These systems are more 
widely applied within problem domains like clustering, pattern recognition, classification, 
optimization, and machine learning. Modern AIS are inspired by one of three sub-fields: clonal 
selection, negative selection, and immune network algorithms. In computer science, the clonal 
selection pattern can be used for pattern matching and optimization. The negative selection algorithm 
was designed for change detection, novelty detection, and intrusion detection. Immune network 
algorithms are inspired by the immune network theory of the acquired immune system and it is an 
upgrade of the clonal selection theory. The objective of the immune network process is to prepare a 
repertoire of discrete pattern detectors for a given problem domain, where better performing solutions 
suppress low affinity solutions within the same network. This is an interactive process of exposing 
the pattern to external information to which it responds. This article explains the biological 
background, the mechanisms of AIS, and presents their real-world applications. It presents an 
overview of those important applications of AIS for solving problems from problem domains like 
data analysis, anomaly detection, intrusion detection, and others. 
Key words: natural immune system, artificial immune system, antigen, antibody, algorithm, somatic 
hyper-mutation. 
Povzetek: Poenostavljeno opredelimo imunski sistem kot organski sistem, ki je namenjen za zaščito 
gostiteljevega organizma pred patogeni in toksičnimi snovmi, ki ga ogrožajo. Sestava imunskega 
sistema temelji na vrsti obrambnih plasti, ki ščitijo gostitelja. Ko patogen uspe prodreti v organizem, 
se mora spopasti s prirojeno in pridobljeno imunostjo. Ta dva podsistema sestojita iz mreže 
medsebojno sodelujočih celic in molekul, ki jih proizvajajo specializirani organi, in procesov, ki 
pomagajo organizmu razlikovati lastne molekule od tujih. Umetni imunski sistemi so računalniški 
modeli, ki temeljijo na naravnih imunskih sistemih. Z refleksijo naravnih mehanizmov so sposobni 
reševati kompleksne probleme v računalništvu. Tovrstni sistemi so široko aplicirani znotraj različnih 
problemov, kot so razvrščanje, optimizacija, strojno učenje in razpoznavanje vzorcev. Moderni 
imunski sistemi so narejeni po zgledu treh področij:klonalna selekcija, negativna selekcija in algoritmi 
imunskih mrež. Mehanizmi klonalne selekcije so uporabni pri optimizaciji in razpoznavanju vzorcev. 
Algoritmi negativne selekcije so bili zasnovani za zaznavanje sprememb in vdorov v sisteme. 
Algoritmi imunskih mrež predstvljajo nadgradnjo teorije klonalne selekcije in so zasnovani na modelu 
pridobljenega imunskega sistema. Nadgradnja teorije imunskih mrež v primerjavi s klonalno selekcijo 
je v dejstvu, da so rešitve z boljšimi rezultati sposobne zavreti rešitve, ki dajejo slabši rezultat pri 
reševanju določenega problema oz. imajo slabo afinteto prepoznavanja specifičnih vzorcev. Gre za 
interaktivni proces izpostavljanja sistema zunanjim informacijam, na katere se odzove. Članek 
pojasnjuje biološko ozadje umetnih imunskih sistemov in navaja možnosti njihove aplikacije v 
resničnem svetu. Predstavlja pregled pomembnih aplikacij umetnih imunskih sistemov na različnih 

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področjih, kot so analize podatkov, odkrivanje anomalij, zaznavanje vdorov in drugi. 
Ključne besede: biološki imunski sistem, umetni imunski sistem, antigen, protitelo, algoritem, 
somatska hipermutacija.  
 
1. Introduction 
The following article presents the usages of 
immune system concepts in computer science. 
Immunology is a relatively new science. Its origins 
have been attributed to Edward Jenner, who in 1796 
discovered that by introducing a small amount of 
vaccine in an animal would induce protection against 
the often lethal disease smallpox. When Jenner 
discovered the process of vaccination, very little was 
known about the functioning of the immune system. In 
the 19
th
 century the discoveries of Koch and other 
researchers contributed to the development of the 
science of immunology, and this development is still 
continuing.  
Artificial Immune Systems (AIS) is a sub-field of 
Computational Intelligence (CI) motivated by 
Immunology which emerged during the early 1990s 
[1] 
[2]
. It is based on a proposal for applying theoretical 
immunological models to automated problem solving 
and machine learning 
[3]
. The first attempts were 
inspired by immune network theoretical models and 
were applied to machine learning, control, and 
optimization problems. The more successful attempts 
were those that proposed the immune systems as 
analogies for information protection systems within 
the field of computer security. The two prominent 
examples include Forrest et al.'s Computer Immunity 
[4]
 and Kephart's Immune Anti-Virus 
[5]
. Immune 
systems are inspired by one of three sub-fields: clonal 
selection, negative selection, and immune network 
algorithms. The techniques are commonly used for 
clustering, pattern recognition, classification, 
optimization, and other similar machine learning 
problem domains.  
 
This article analyses the concepts of modern 
artificial immune systems. Chapter 2 explains their 
biological background and the basics of immune 
systems. The chapter is divided into innate and 
adaptive immune systems for providing the basic 
biological explanations of those mechanisms which 
are used within the field of artificial immune systems. 
Chapter 3 focuses on artificial immune systems and 
their differentiation and principles. The correlations 
between the natural and artificial immune systems are 
explained as to how and where they work. Chapter 4 
upgrades the previous chapter by presenting 
applications of immune-based algorithms in practice. 
It also includes some new fields by considering 
immune system investigations that might be important 
in the future.  
2. Biological basics of immune systems 
The immune system can be divided into two basic 
components, the innate and adaptive immune systems.
 
[6]
 The innate immune system lacks the specificity. 
Therefore it is also called non-specific response. The 
adaptive immune system adapts its response during an 
infection and improves its recognition of a pathogen 
after the first encounter. This improved response is 
then retained in the form of an immunological 
memory. The immunological memory allows the 
adaptive immune system to act much quicker and 
stronger when there is a second encounter with the 
same pathogen
 [7]
.
 
Table 1 presents the basic 
differences between the innate and adaptive immune 
system.
  
Table 1. The two basic components of the immune system. The table shows two basic 
distinctions of the immune system and the differences between them. The adaptive immune 
system is present only in jawed vertebrates and has many advantages over the innate 
immune system, e.g., antigen specific response, lag time, and immunological memory. 
 
2.1. Innate immune system 
The innate immune system provides a non-specific 
response, which is maximal even at the first encounter 
with an intruder. It is not able to adapt its response. 
This means that at any encounter with the same 
pathogen, the response will be the same. The innate 
immune system relies on four important 
characteristics: surface barriers, inflammation factors, 
complement system, and some specially designed 
cells.  
Innate immune system Adaptive immune system 
Non-specific response Antigen specific response 
Exposure leads to immediate maximal response Lag time between exposure and maximal response 
No immunological memory Immunological memory after exposure 
In nearly all forms of life Only in jawed vertebrates 

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The surface barriers provide chemical, mechanical, 
and biological protection against intruders. Human 
skin, the flushing actions of tears and urine, and the 
surface of a respiratory tract secreting mucus are all 
good examples of surface barriers.  
Inflammation factors include eicosanoids and 
cytokines, which are released by injured or infected 
cells. They are important for the production of fever 
and dilatation of the blood vessel associated with 
inflammation. Common cytokines include chemocines 
that promote chemotaxis (directed movement of cells) 
[8]
. 
The third important part of the innate immune 
reaction is the complement system. This is an 
important cascade of molecules that attack the surfaces 
of foreign cells. It contains over 20 different proteins 
and is a major component of the innate immune 
response
 [9]
. This catalytic cascade that works in a 
positive feedback mechanism opsonizes (coats) in the 
surface of a pathogen and marks it for destruction.  
Lastly, specially designed cells also take part in 
innate response. These cells are macrophages, 
dendritic cells, mast cells, neutrophils, eosinophils, 
basophils, and natural killer cells. These cells of innate 
immunity play an important role in activating and 
shaping the adaptive immune response upon activation 
of their innate immunity receptors. Before the 
discovery of special receptors on these cells, called 
Toll-like receptors (TLRs), innate immunity was seen 
as a crude and unsophisticated part of the immune 
system. TLR receptors are a class of single membrane-
spanning receptors and they recognize structurally 
conserved molecules derived from microbes. After the 
activation of TLRs, immune cells produce cytokines. 
Cytokines are involved in cellular activation and 
therefore we can say that the innate immunity is 
responsible for triggering the adaptive immunity 
[10]
. 
Dendritic cells are antigen-presenting cells with a 
unique ability to induce and control the development 
of adaptive immune response. They capture and 
transfer information from the outside world to the cells 
of the adaptive immune system. Their main function is 
to process antigens and present it to the specific clones 
of lypmhocytes, thus inducing their proliferation and 
expansion. They are antigen-presenting cells and act as 
messengers between the innate and adaptive immune 
system 
[11]
. They have many spine-like projections and 
are mainly located in the skin, nose, lungs, stomach, 
and intestines. Macrophages are also phagocytocis 
cells. Basophils, neutrophils and eosinophils secrete 
chemical mediators that are involved in defending 
against pathogens (Figure 1) 
[8]
. 
 
 
Figure 1. Neutrophils, eosinophils, and 
basophils. Collectively they are referred 
to as granulocytes because of the small 
granules inside which contain chemical 
mediators.  
2.2. Adaptive immune system 
The adaptive immune system remembers a 
pathogen by its signature. This signature is called the 
antigen and so this is an antigen-specific response. 
Antigen is a part of pathogen or self-molecules that 
evokes the adaptive, antigen-specific immune 
response, be it the production of antibodies or T-cell 
mediated immunity. Each antibody or T-cell receptor 
binds to a specific antigen by way of an interaction 
similar to the fit between a lock and a key. There is a 
lag-time between exposure and the production of 
antibodies, and during this lag-time the immunological 
memory is formed and the response is upgraded. The 
two main parts of the adaptive immune system are 
lymphocytes B and T. We will describe some 
important mechanisms of an adaptive immune 
response which have inspired computer science.  
Central tolerance is the mechanism by which 
lymphocytes B and T mature. B-cell maturation occurs 
in bone marrow and T-cell maturation occurs in 
thymus. These developing B and T cells are first 
exposed to positive selection to self-antigens. The 
positive selection selects for survival of those cells 
which can respond through their receptors. Those that 
do not respond, fail to survive 
[12]
. In the next stage, 
developing B and T lymphocytes are exposed to self-
antigens in the process called negative selection. The 
negative selection deletes the clones which respond to 
self antigens strongly. The affinity part seems to play 
an important role, since those clones that still respond, 
but with a lower affinity, will be regulatory clones 
which spread the tolerance to self signals (Figure 2) 
[8] 
[13] [14] 
.
  
 

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Figure 2. The clonal selection and the negative selection principle. Some lymphocytes carry the 
receptors that bind to self-antigens. They are eliminated during the early phase of development 
before they are able to perform and attract an immune response. This process leads to self-tolerance 
and is called ‘negative selection’. When the foreign antigen interacts with the receptor within a 
mature lymphocyte, the cell is activated and commences proliferating. This is the ‘clonal selection 
principle’. The clone will arise and differentiate between effector and memory cells. During the 
differentiation these cell undergo changes called ‘somatic hyper-mutation’.
Lymphocytes B produce antibodies. Antibodies are 
their way of detecting antigens. Each antibody 
produced by lymphocyte B has its own unique variable 
region which allows an antibody to recognize its 
matching antigen. When an antibody binds to an 
antigen, this antigen/antibody complex is taken up by 
B lymphocytes and processed by proteolysis into small 
particles, peptides.  
3. Artificial immune systems  
Artificial immune systems simulate the basic 
concepts of natural immune systems in order to solve 
real-world problems. Immunology is the researching 
of immune systems and the body's protection against 
viruses, bacteria, cancer and other intruders. 
Nowadays, artificial immune systems are based on 
three important types, as follows: 
- clonal selection algorithm, 
- negative selection algorithm,  
- immune network algorithm.  
The clonal selection algorithm is inspired by the 
clonal selection theory of acquired immunity 
[15] [16]
. 
This theory proposes that when a lymphocyte binds to 
an antigen, it starts to proliferate, making many more 
thousands of copies of itself and differentiates into 
different cell types (Figure 2).  
The important feature of the theory is that when the 
cell proliferates, it goes through the process of small 
copying errors 
[17]
.This causes the changes in the 
genome and these changes are called ‘somatic hyper-
mutation’ 
[18] 
. The result of this process is the change 
of the shape of the expressed receptors and subsequent 
antigen recognition capabilities. In other words, 
starting with the initial repertoire of general immune 
cells, the system is able to change itself in composition 
and density. The general model of the clone selection 
algorithm involves the selection of antibodies 
(possible solutions) and these selected antibodies are 
subjected to cloning (proliferation) and hyper-
mutation. The goal is to produce antibodies which have 
the highest affinity to a certain antigen pattern. The 
resultant clonal set competes with the existent antibody 
population for membership in the next generation. In 
addition, all low affinity population members are 
replaced by randomly generated antibodies. The result 
is antibodies which express the highest affinity for a 
certain pattern. As seen from the description, the clonal 
selection pattern in computer science can be used for 
pattern matching and optimization 
[19]
. 
A negative selection algorithm is inspired by the 
process of negative selection described earlier in this 
article. This self-nonself discriminatory behavior is 
responsible for the fact that only the cells selected for 
potentially harmful and foreign material in the body 
are included in the ongoing proliferation (Figure 2). In 
this way, self-reactive immune cells are destroyed. The 
negative selection algorithm was designed for change 
detection, novelty detection, and intrusion detection 
[15]
. This is achieved by building a model of changes, 
anomalies or unknown data by generating patterns 
which do not match with an existing corpus of 
available (self or normal) patterns. After that this 
prepared abnormal model is used to monitor the 
existing normal data or streams of new data by seeking 
matches to the abnormal patterns 
[20]
. 

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Immune network algorithms are inspired by the 
immune network theory of the acquired immune 
system. This is an upgrade of the clonal selection 
theory. A concern of this theory is that it presumes that 
immune cells remain idle when there are no pathogens 
to which to respond 
[20]
. The immune network theory 
proposes that immune cells are not at rest in the 
absence of pathogen, instead the antibody and immune 
cells recognize and respond to each other 
[21]
. 
Antibodies, both free-floating and surface-bound, 
possess surface features to which the receptors of other 
antibodies can bind. As a result of this interaction, 
antibodies both excite and inhibit each other within 
complex regulatory networks (chains of receptors)
 [22]
. 
Regarding this principle, in addition to a cell which 
reacts directly with pathogen, there are cells that 
interact with those reactive cells. The objective of the 
immune network process is to prepare a repertoire of 
discrete pattern detectors for a given problem domain, 
where better performing cells suppress low affinity 
cells within the same network 
[23]
. This is an interactive 
process of exposing the population to external 
information to which it responds.  
4. Applications of AIS 
Currently, the negative selection algorithm is the 
most popular in computer applications. The purpose of 
negative selection is to provide tolerance for self-cells. 
The pseudo-code of this AIS algorithm is presented in 
Pseudo-code 1.
  
Pseudo-code 1: Algorithm AIS with negative selection 
01: 
02: 
03: 
04: 
05: 
06: 
07: 
08: 
09: 
10: 
11: 
12: 
13: 
Input: S = set of known self samples  
Output: D = set of collected detectors  
repeat 
    Randomly generate potential set of detectors pP  ; 
    Determine the affinity of each member pP  with each member of the set sS  ;  
    if At least one element sS  match a detector pP  then 
        Discard pP  ; 
    else 
        Add pP  to detector set D ; 
    endif 
until Termination condition has been met; 
 
As can be seen from Pseudo-code 1, let us assume 
an input set of seen known self-samples, while the set 
of generated detectors is obtained. In main loop (lines 
03.13), the algorithm randomly generates a potential 
set of detectorspP  . Then the determining of the 
affinities of each number inpP  against each 
member ofsS  , needs to be performed (line 04). 
Finally, detection stage (lines 07-12) is conducted in 
order to verify whether at least one element in sS  
matches the detectorpP  . In that case, the detector 
is rejected while, in contrast, it is added to the set of 
available detectors D. The principle of this detection 
stage is illustrated in Figure 3. 
Figure 3. Principle of the detection 
stage. The detection stage consists of 
two input sets: the set of detectors 
pP  and the set of self samplessS 
. If any element sS  matches the 
detector pP  this is rejected, 
otherwise it is added to the set of 
available detectors   D D p  . 
Table 2 represents some important applications of 
AIS algorithms. For more profound knowledge about 
an application of AIS in real-world, readers are invited 
to follow references as proposed in this table. As can 
see from the tables, the AIS applications can be found 
within many computational domains as well as in real-
world practice.  
As many sources have suggested, AIS is very 
useful for intrusion 
[24]
 or anomaly detection 
[25]
. In line 
with this, some AIS applications are used for computer 
virus detection too. On the other hand, very interesting 
applications also focus on the optimization. For 
example, Gong et al. 
[30]
 applied AIS to numerical 
optimization. Moreover, Freschi and Repetto 
[29]
 
solved multi-objective optimization problems with 
AIS. On the other hand Coello 
[26]
 applied AIS to job 
shop scheduling which is a well-known problem of 
combinatorial optimization. Some tests have also 
showed that AIS provides promising results by data 
analysis 
[27] 
and also by clustering 
[31]
. From these facts, 

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it can be concluded that AIS is a suitable method for 
data mining. Actually, data mining is currently one of 
the hottest areas of research in computer science. 
Nowadays, AIS is also a proven tool in industrial 
[28]
 
as well as control system 
[32] 
domains.
 
Table 2. Applications of algorithms based on AIS 
Application Author 
Intrusion detection Aickelin et. al. 
[24]
 
Anomaly detection Greensmith et. al. 
[25]
 
Numerical optimization Gong et. al. 
[30]
 
Multi-objective optimization Freschi and Repetto 
[29]
 
Job shop scheduling Coello et. al. 
[26]
 
Data analysis  Timmis 
[27]
 
Dociment clustering Tang and Vemuri 
[31]
 
Industrial applications Dasgupta and Forrest 
[28]
 
Control systems Wei and Zhang 
[32]
 
 
AIS are still under development and further 
improvement. In the future, we can expect more real-
world applications using AIS for solving hard 
problems. Therefore, many novel variants and hybrids 
of AIS will come out in the following years. 
5. Conclusion 
This paper presented the principles of the natural 
immune system which in turn inspire artificial 
algorithms for solving complex problems in computer 
science. The immune system is a remarkable natural 
defense mechanism that exhibits capabilities like 
learning, memory, and adaptation. In line with this, it 
is hoped that this article has served to convince the 
reader that the immune system is worthy of study from 
a computational point of view. This article exhibited 
three main types of AIS: clonal selection, negative 
selection, and immune networks.  
The goal of AIS in the future should be to further 
extract what can be considered as inspiration from the 
components and processes from the immune system 
for creating effective and powerful computational 
systems. A significant number of concepts could be (or 
currently are being) used in the development of AIS. 
Some of these concepts are clonal expansion and 
affinity maturation, cross-reactivity, epitope, idiotope, 
paratope, B-cell and BCR, T-cell and TCR, network 
structure, dynamics and meta-dynamics, etc. It can be 
seen that the possibilities of extracting useful 
metaphors and constructing new AIS theories are still 
wide-open.  
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