A Preliminary Study on Why Using the Nonself Detector Set for Anomaly Detection in Artificial Immune Systems

2009 ◽  
Author(s):  
Baoliang Xu ◽  
Wenjian Luo ◽  
Xufa Wang
Keyword(s):  
Anomaly Detection ◽  
Artificial Immune Systems ◽  
Artificial Immune ◽  
Immune Systems ◽  
Preliminary Study

Artificial Immune Systems for Anomaly Detection in Ambient Assisted Living Applications

2013 ◽  
Vol 5 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Sebastian Bersch ◽  
Djamel Azzi ◽  
Rinat Khusainov ◽  
Ifeyinwa E. Achumba
Keyword(s):  
Anomaly Detection ◽  
Assisted Living ◽  
Artificial Immune Systems ◽  
Ambient Assisted Living ◽  
Artificial Immune ◽  
Immune Systems ◽  
Long Term Monitoring ◽  
Future Work ◽  
Term Monitoring

This paper makes a case for the use of Artificial Immune Systems (AIS) in the area of Ambient Assisted Living (AAL) for anomaly detection and long term monitoring. A brief literature review of some of the solutions developed for AAL and the use of AIS in other fields of research is presented. The authors advocate the use of AIS in AAL based on their unique features and their ability to address problems specific to the long term monitoring of people. An improved method for the optimisation of detector generation for AIS, which uses a novel intelligent seeding technique, is presented. The new seeding technique is compared with two other detector seeding methods. The simulation results are presented showing an improvement in the classification accuracy and warranting current and future work.

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.

scholarly journals Special Issue on Artificial Immune Systems: Theory and Applications

2007 ◽  
Vol 19 (4) ◽  
pp. 647-647
Author(s):  
Xiao-Zhi Gao ◽  
Mo-Yuen Chow ◽  
David Pelta ◽  
Jon Timmis
Keyword(s):  
Systems Theory ◽  
Artificial Immune Systems ◽  
Special Issue ◽  
Artificial Immune ◽  
Immune Systems
Sign in / Sign up

Export Citation Format

Share Document