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.

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.

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.

Artificial Immune Systems

Data Mining ◽  
2011 ◽  
pp. 209-230 ◽  
Author(s):  
Jonathan Timmis ◽  
Thomas Knight
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Immune System ◽  
Data Analysis ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
General Introduction ◽  
Immune Systems ◽  
Novel Approaches ◽  
The Creation

The immune system is highly distributed, highly adaptive, self-organising in nature, maintains a memory of past encounters and has the ability to continually learn about new encounters. From a computational viewpoint, the immune system has much to offer by way of inspiration. Recently there has been growing interest in the use of the natural immune system as inspiration for the creation of novel approaches to computational problems; this field of research is referred to as Immunological Computation (IC) or Artificial Immune Systems (AIS). This chapter describes the physiology of the immune system and provides a general introduction to Artificial Immune Systems. Significant applications that are relevant to data mining, in particular in the areas of machine learning and data analysis, are discussed in detail. Attention is paid both to the salient characteristics of the application and the details of the algorithms. This chapter concludes with an evaluation of the current and future contributions of Artificial Immune Systems in data mining.

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 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.

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.

Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models

2012 ◽  
pp. 371-387 ◽  
Author(s):  
Cagatay Catal ◽  
Soumya Banerjee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Software Metrics ◽  
Prediction Models ◽  
Artificial Immune Systems ◽  
Fault Prediction ◽  
Artificial Immune ◽  
Software Fault Prediction ◽  
Immune Systems ◽  
Software Fault

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).

THE USAGE OF DISTRIBUTED ARTIFICIAL IMMUNE SYSTEMS FOR SOLVING PROBLEMS OF IDENTIFICATION IN ECOLOGY

2016 ◽  
Vol 8 (3) ◽  
pp. 10-14
Author(s):  
Ушаков ◽  
S. Ushakov ◽  
Астахова ◽  
I. Astakhova ◽  
Хицкова ◽  
...  
Keyword(s):  
Artificial Immune System ◽  
Artificial Immune Systems ◽  
Identification Problem ◽  
Distributed Network ◽  
Heterogeneous Environment ◽  
Artificial Immune ◽  
Original Function ◽  
Power Sources ◽  
Immune Systems ◽  
Cross Platform

There is a problem of ecological forecasting identification which is finding power sources for the available experimental data. This task is an inverse problem, the solution of which will be considered by applying the method of symbolic regression. The artificial immune system (AIS) – a model that allows you to solve various problems of recognition, its concept was borrowed from biology. This distributed network can operate in any heterogeneous environment, which is achieved through the use of cross-platform programming language Python. AIS demonstrates the ability to restore the original function in the identification problem, a plot of the solution for test data is presented.

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