IMMUNOLOGY AND ARTIFICIAL IMMUNE SYSTEMS

2012 ◽  
Vol 21 (06) ◽  
pp. 1250031 ◽  
Author(s):  
MUHAMMAD ROZI MALIM ◽  
FARIDAH ABDUL HALIM
Keyword(s):  
Immune System ◽  
Network Theory ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Selection Mechanism ◽  
Artificial Immune ◽  
Time Line ◽  
Immune Systems

Artificial immune system is inspired by the natural immune system for solving computational problems. The immunological principles that are primarily used in artificial immune systems are the clonal selection principle, the immune network theory, and the negative selection mechanism. These principles have been applied in anomaly detection, pattern recognition, computer and network security, dynamic environments and learning, robotics, data analysis, optimization, scheduling, and timetabling. This paper describes how these three immunological principles were adapted by previous researchers in their artificial immune system models and algorithms. Finally, the applications of various artificial immune systems to various domains are summarized as a time-line.

How Do We Evaluate Artificial Immune Systems?

2005 ◽  
Vol 13 (2) ◽  
pp. 145-177 ◽  
Author(s):  
Simon M. Garrett
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Danger Theory

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of ‘distinctiveness’ and ‘effectiveness.’ In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological ‘danger theory.’ The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

scholarly journals Intrusion detection based on the artificial immune system

2020 ◽  
Vol 68 (4) ◽  
pp. 790-803
Author(s):  
Danijela Protić
Keyword(s):  
Immune System ◽  
Dendritic Cell ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
The Way

Introduction/purpose: The artificial immune system is a computational model inspired by the biological or human immune system. Of particular interest in artificial immune systems is the way the human body reacts to new pathogens and adapts to remain immune for a long period after a disease has been combated, which refers to the recognition of known malicious attacks and the way the immune system identifies self-cells not to be reacted to, which refers to the anomaly detection. Methods: Negative selection, positive selection, clonal selection, immune networks, danger theory, and dendritic cell algorithm are presented. Results: A variety of algorithms and models related to artificial immune systems and two classification principles are presented; one based on the detection of a particular attack and the other based on anomaly detection. Conclusion: Artificial immune systems are often used in intrusion detection since they are accurate and fast. Experiments show that the models can be used in both known attack and anomaly detection. Eager machine learning classifiers show better results in the decision, which is an advantage if runtime is not a significant parameter. Dendritic cell and negative selection algorithms show better results for real-time detection.

Developing an Algorithm for the Application of Bayesian Method to Software Using Artificial Immune Systems

2021 ◽  
Author(s):  
Shafagat Mahmudova
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Bayesian Method ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Computational System ◽  
Malicious Software ◽  
Immune Systems ◽  
Advantages And Disadvantages ◽  
Protection Systems

Abstract This study provides information on artificial immune systems. The artificial immune system is an adaptive computational system that uses models, principles, mechanisms and functions to describe and solve the problems in theoretical immunology. Its application in various fields of science is explored. The theory of natural immune systems and the key features and algorithms of artificial immune system are analyzed. The advantages and disadvantages of protection systems based on artificial immune systems are shown. The methods for malicious software detection are studied. Some works in the field of artificial immune systems are analyzed, and the problems to be solved are identified. A new algorithm is developed for the application of Bayesian method in software using artificial immune systems, and experiments are implemented. The results of the experiment are estimated to be good. The advantages and disadvantages of AIS were shown. To eliminate the disadvantages, perfect AISs should be developed to enable the software more efficient and effective.

Performance of Negative Selection Algorithms in Patient Detection and Classification

2018 ◽  
pp. 78-102
Author(s):  
Orhan Bölükbaş ◽  
Harun Uğuz
Keyword(s):  
Immune System ◽  
Error Detection ◽  
Classification Accuracy ◽  
Negative Selection ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Detection Accuracy ◽  
Data Sets ◽  
Selection Algorithm ◽  
Artificial Immune

Artificial immune systems inspired by the natural immune system are used in problems such as classification, optimization, anomaly detection, and error detection. In these problems, clonal selection algorithm, artificial immune network algorithm, and negative selection algorithm are generally used. This chapter aims to solve the problem of correct identification and classification of patients using negative selection (NS) and variable detector negative selection (V-DET NS) algorithms. The authors examine the performance of NSA and V-DET NSA algorithms using three sets of medical data sets from Parkinson, carotid artery doppler, and epilepsy patients. According to the obtained results, NSA achieved 92.45%, 91.46%, and 92.21% detection accuracy and 92.46%, 93.40%, and 90.57% classification accuracy. V-DET NSA achieved 94.34%, 94.52%, and 91.51% classification accuracy and 94.23%, 94.40%, and 89.29% detection accuracy. As can be seen from these values, V-Det NSA yielded a better result. Artificial immune system emerges as an effective and promising system in terms of problem-solving performance.

THE MODEL AND ALGORITHM ARTIFICIAL IMMUNE SYSTEM

2016 ◽  
Vol 8 (3) ◽  
pp. 5-10
Author(s):  
Астахова ◽  
I. Astakhova ◽  
Ушаков ◽  
S. Ushakov
Keyword(s):  
Neural Networks ◽  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Program Complex ◽  
Immune Systems ◽  
The Creation

In particular, models had only one type of cages , they applied V-lymphocytes. The distribution and a decentralization were the second feature for using artificial immune systems. This article is devoted to creation the artificial immune system (AIS), the creation model and algorithm of IIS is considered. The model for realization of a problem is consid-ered. Accuracy of calculations is compared to other methods, especially to neural networks. The structure of a program complex is described.

Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune Systems

2009 ◽  
Vol 25 (25) ◽  
pp. 159-164 ◽  
Author(s):  
Mikhail Gorobetz ◽  
Ivars Alps ◽  
Anatoly Levchenkov
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Flow Shop ◽  
Mathematical Formulation ◽  
Artificial Immune Systems ◽  
Optimal Schedule ◽  
Electric Transport ◽  
Artificial Immune ◽  
Human Immune System ◽  
Immune Systems

Mathematical Formulation of Public Electric Transport Scheduling Task for Artificial Immune SystemsThis paper describes mathematical formulation and application of artificial immune system for scheduling tasks for public electric transport. Artificial immune system is inspired by human immune system to simulate the process of interaction between antigens and antibodies. The task of scheduling in transport system is represented as one of the most well-known flow shop problem. Artificial immune system as a genetic based method is used to solve such task. Mathematical model and algorithm is proposed to create optimal schedule for public electric transport for minimization of electric energy consumption and time. Numerical example shows several steps of algorithm for artificial immune system for scheduling task solution.

scholarly journals Optimization of the Configuration and Operating States of Hybrid AC/DC Low Voltage Microgrid Using a Clonal Selection Algorithm with a Modified Hypermutation Operator

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6351
Author(s):  
Łukasz Rokicki
Keyword(s):  
Immune System ◽  
Power Systems ◽  
Evolutionary Algorithm ◽  
Distribution System ◽  
Artificial Immune System ◽  
Low Voltage ◽  
Clonal Selection ◽  
Artificial Immune Systems ◽  
Clonal Selection Algorithm ◽  
Artificial Immune

The issue of optimization of the configuration and operating states in low voltage microgrids is important both from the point of view of the proper operation of the microgrid and its impact on the medium voltage distribution network to which such microgrid is connected. Suboptimal microgrid configuration may cause problems in networks managed by distribution system operators, as well as for electricity consumers and owners of microsources and energy storage systems connected to the microgrid. Structures particularly sensitive to incorrect determination of the operating states of individual devices are hybrid microgrids that combine an alternating current and direct current networks with the use of a bidirectional power electronic converter. An analysis of available literature shows that evolutionary and swarm optimization algorithms are the most frequently chosen for the optimization of power systems. The research presented in this article concerns the assessment of the possibilities of using artificial immune systems, operating on the basis of the CLONALG algorithm, as tools enabling the effective optimization of low voltage hybrid microgrids. In his research, the author developed a model of a hybrid low voltage microgrid, formulated three optimization tasks, and implemented an algorithm for solving the formulated tasks based on an artificial immune system using the CLONALG algorithm. The conducted research consisted of performing a 24 h simulation of microgrid operation for each of the formulated optimization tasks (divided into 10 min independent optimization periods). A novelty in the conducted research was the modification of the hypermutation operator, which is the key mechanism for the functioning of the CLONALG algorithm. In order to verify the changes introduced in the CLONALG algorithm and to assess the effectiveness of the artificial immune system in solving optimization tasks, optimization was also carried out with the use of an evolutionary algorithm, commonly used in solving such tasks. Based on the analysis of the obtained results of optimization calculations, it can be concluded that the artificial immune system proposed in this article, operating on the basis of the CLONALG algorithm with a modified hypermutation operator, in most of the analyzed cases obtained better results than the evolutionary algorithm. In several cases, both algorithms obtained identical results, which also proves that the CLONALG algorithm can be considered as an effective tool for optimizing modern power structures, such as low voltage microgrids, including hybrid AC/DC microgrids.

Intrusion Detection System using Artificial Immune Systems: A Case Study

2018 ◽  
Vol 8 (2) ◽  
pp. 19
Author(s):  
. Ojasvini ◽  
. Nitesh ◽  
. Piyush ◽  
Narina Thakur ◽  
Arvind Rehalia
Keyword(s):  
Immune System ◽  
Intrusion Detection ◽  
Large Scale ◽  
Artificial Immune System ◽  
Detection System ◽  
Dynamic Networks ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Malicious Nodes ◽  
Immune Systems

Networks are working at their apical efficiency and are increasing in size by every second; emergence of various threats becomes hindrance in the growth and privacy of the users. The network is vulnerable to security breaches, due to malicious nodes. Intrusion detection systems aim at removing this vulnerability. In this paper, intrusion detection mechanisms for large-scale dynamic networks are investigated. Artificial immune system is a concept that works to protect a network the way immune systems of vertebrates work in nature. This paper also illustrates this artificial immune system, the integration of bio-inspired algorithms, and its functionality with the computer networks.

scholarly journals METHOD OF SPAM DETECTION BASED ON ARTIFICIAL IMMUNE SYSTEMS

2018 ◽  
pp. 38-48
Author(s):  
Maria Petrovna Malykhina ◽  
Vera Arkadyevna Chastikova ◽  
Alexandr Aleksandrovich Biktimirov
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Low Frequency ◽  
Spam Detection ◽  
Artificial Immune ◽  
New Approach ◽  
Immune Systems ◽  
Initial Stage ◽  
Living Organisms

The task of developing tools to combat spam is currently focused on creating such techniques for detecting spam, which are endowed with the skills and qualities inherent in a person whose work is not limited to patterns and therefore highly effective. Man has the ability to detect spam signs, which is based on his own knowledge, experience and preferences. There has been substantiated the need to develop a new approach to solving the problem of detecting spam messages, which is based on heuristic methods of optimization, is effective at the initial stage of training and has a low frequency of false operations. This formulation of the problem fully corresponds to modeling mechanisms of the immune systems of living organisms that ensure their survival, these mechanisms being represented, investigated and used by software. There have been identified and described main mechanisms of artificial immune systems intended for solving the problem of spam detection, as well as software and system interacting. The basic concepts of constructing an artificial immune system for the purpose formulated above are determined: class of detectors, presentation of receptors and pathogens. A model of the relationships between them has been worked out. A technique for detecting spam based on the work of an artificial immune system is proposed, an algorithm for its implementation is developed, and the specifics of its members to identify spam messages are described. A software package with advanced research capabilities has been created. Testing and analysis of the results to determine the optimum values of the system operation parameters have been conducted.

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