A Study On Artificial Immune System Computer Science Essay

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Artificial immune system was developed in the 1990.It was a new computational research area.The artificial immune system is a new computational approach for the computational intelligent community.Like other biologically inspired techniques, it tries to extract ideas from a natural system ,in order to develop computational tools for solving engineering problems. Many ideas inspired by the innate immune system such as danger theory and algorithms. An artificial Immune Systems application in collaborative filtering approach in the implementation of recommender system helps to verify this method and create novel technologies for that kind of systems. The research will gather the knowledge about different applications of AIS in recommender systems as well as enable to develop user interface and content recommendation for wiki-based systems

Basically AIS is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology.

Artificial Immune System Basis of The Presented System:-

Whenever the pathogen enters the body,than our immune system is activated than ,immune system in response secrets B-cells which secrets antibodies in order to destroy antigens.

Likewise, when an event occurs in sensor range some sensor nodes are stimulated and move in order to minimize distance and more accurately monitor the event

  • B-Cells: Sensor Nodes
  • Pathogens: Events of Interest
  • Antigen: Distance to Event
  • Antibody : Movement
  • Antibody Density: Speed


The human immune systems protect the human body against pathogens which try to info the body. The human immune system consists of three main components:-

  • SKIN:-
  • The biggest component and the first defence line of the immune system against pathogens.

  • - Protects human body against most basic pathogens and remove these pathogens quickly. The innate immune system does not learn a lot, it applies the given knowledge of basic pathogens.

  • The adaptive immune system protects the human body against complex, mutated attacks. It reacts slowly and normally it learns from a local infection by the attacking pathogen. It protects the human body against known attacks; this functionality is used for vaccination.


The main significance of immune system is that it protect the human body from any infection. Infact we can say that physical barriers prevent pathogens(infection causing microorganisms) such as bacteria and viruses from entering the body.If these pathogens tries to enter this barrier, than immediately, innate system is activated and it response to particular pathogen. We see that innate immune system is present in all plants and animals. If innate system is unable to affect the pathogen and then it enters than there is another layer of protection i.e. adaptive immune system which is activated by the innate response. Here, the immune system adapts its response during an infection to improve its recognition of the pathogen.


  • Barriers protect the body from infection, including mechanical, chemical and biological barriers.
  • These barriers include skin and mucous membranes, which line all body cavities.
  • Chemical barriers such as enzymes in saliva and tears also protect against Infection by destroying harmful bacteria


The innate immune system consists of the cells which perform certain mechanism that defend the host from infection by other organisms, in a non-specific manner. This means that the cell of the innate system recognize, and respond to, pathogens in a generic way, but unlike the adaptive immune system ,it does not affect long-lasting or protective immunity to the host. Innate immune systems provide proper and complete defense against infection.


  • Varous different chemicals known as cytokines are present which are used to immunize different infection and inflammations.
  • To identify bacteria, activate cells and to promote clearance of dead cells.
  • Basically white blood cells are used to identify and remove all the foregn bodies from the particular organ or tissue.
  • Activation of the adaptive immune system.


  • The cell of the adaptive immune system is a type of leukocyte, called a lymphocyte.
  • B cell and T cell are the major types of lymphocytes.
  • The human body has about 2 trillion lymphocytes, constituting 20-40% of the body's white blood cells; their total mass is about the same as the brain or liver.
  • The peripheral blood contains 20-50% of circulating lymphocytes; the rest move within the lymphocytes system.


  • Recognize the pathogen and become activated
  • Multiply rapidly to fight the pathogen.
  • Assign various tasks to destroy infected or diseased cells
  • Activated lymphocytes multiply quickly in greater numbers(billions) to produce antibody as well as killer cells to wipe out the enemy
  • Memory cells- the long -lived survivors of past infections live on to activate the immune system faster when the pathogen invaded again


  • Protect our bodies from infection
  • Primary immune response
  • Launch a response to invading pathogens
  • Secondary immune response
  • Remember past encounters
  • Faster response the second time around

Multiple layers of the immune system:-


The human immune system works in the human body with the following Attributes:

  • Unsupervised/Autonomous:

The human immune system consists of lots of different components and all components work autonomous in order to guarantee the functionality even if parts of the immune system break down.

  • Decentralised/Distributed:

The components of the immune system are distributed over the whole human body. Furthermore, the components flow through the network.

  • Adaptive:

The human immune system learns from infections and immunizes the human body against these.

  • Efficient:

If a novel or modified pathogen enters the human body, the immune system cannot identify it and remove it. Thus, the pathogen infects some areas of the human body; the adaptive immune system reacts on it; and develops a disinfection-strategy quickly.

We see that the human immune system is a nearly perfect protection-system for the human body. Now, we will look on the artificial immune system which tries to model the human immune system for computer science - here only for network security - for obtaining the advantages. In the artificial immune system, the components are artificial cells or agents which flow through a computer network and which process several tasks in order to identify and prevent attacks from intrusions. Therefore, the artificial cells are equipped with the same attributes as the human immune system. The artificial cells try to model the behaviour of the immune-cells of the human immune system.

v Examples for application of artificial immune systems: -

  • Network Security
  • Optimisation Problems
  • Distributed Computing


The common techniques are as follows..

  • Clonal Selection Algorithm
  • Negative Selection Algorithm
  • Immune Network Algorithms
  • Dendritic Cell Algorithms

Clonal Selection Algorithm:-

Clonal selection algorithms are most commonly applied to optimization and pattern recognition domains, some of which resemble parallel hill climbing and the genetic algorithm without the recombination operator

Negative Selection Algorithm:-

Negative selection refers to the identification and deletion (apoptosis) of self-reacting cells, that is T cells that may select for and attack self tissues. This class of algorithms are typically used for classification and pattern recognition problem domains where the problem space is modeled in the complement of available knowledge.

Immune Network Algorithms:-

Immune network algorithms have been used in clustering, data visualization, control, and optimization domains, and share properties with artificial neural networks

Dendritic Cell Algorithms:-

The DCA is abstracted and implemented through a process of examining and modeling various aspects of DC function, from the molecular networks present within the cell to the behaviour exhibited by a population of cells as a whole.

What problems are Artificial Immune Systems most suitable for?

We believe that although using Artificial Immune Systems for pure optimization, e.g. the Travelling Salesman Problem or Job Shop Scheduling, can be made to work, this is probably missing the point. Artificial Immune Systems are powerful when a population of solution is essential either during the search or as an outcome.

Furthermore, the problem has to have some concept of 'matching'. Finally, because at their heart Artificial Immune Systems are evolutionary algorithms, they are more suitable for problems that change over time rather and need to be solved again and again, rather than one-off optimizations.

Hence, the evidence seems to point to Data Mining in its wider meaning as the best area for Artificial Immune Systems.


The immune system relies on the prior formation of an incredibly diverse population of:

  • B cells (B lymphocytes) each with its surface covered with thousands of identical copies of a receptor for antigen (the B-cell receptor for antigen = BCR)
  • T cells (T lymphocytes) each with its surface covered with thousands of identical copies of a T-cell receptor for antigen (TCR)

Clonal selection leads to the eventual production of:

  • A pool of antibody-secreting plasma cells. Plasma cells are B-cells that have tooled up (e.g., forming a large endoplasmic reticulum) for massive synthesis and secretion of an antibody. The antibody is the secreted version of the BCR. (For clarity, each BCR is shown with a single binding site for the epitope; actually, the BCRs are IgM and each has 10 identical binding sites.
  • A pool of "memory" cells. These are B lymphocytes with receptors of the same specificity as those on the original activated B cell.


  • In early simulations, the Artificial Immune System-Based mobile node movement system consistently outperforms the static scheme with regards to proportion of time an event is sensed.
  • Primitive data on sensing data utility suggests that it is not merely quantity, but quality of the sensor data that improves when the proposed scheme is utilized.
  • The proposed scheme incurs a relatively low level of computational and communication overhead.
  • However, further results are needed.