A Combination of Negative Selection Algorithm and Artificial Immune Network for Virus Detection

2014 ◽  
pp. 97-106 ◽  
Author(s):  
Vu Thanh Nguyen ◽  
Toan Tan Nguyen ◽  
Khang Trong Mai ◽  
Tuan Dinh Le
Keyword(s):  
Negative Selection ◽  
Virus Detection ◽  
Immune Network ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Negative Selection Algorithm ◽  
Artificial Immune Network

A Smart System of Malware Detection Based on Artificial Immune Network and Deep Belief Network

2019 ◽  
Author(s):  
Nguyen Vu Thanh ◽  
Dung Hoang Le ◽  
Tuan Dinh Le
Keyword(s):  
Detection Rate ◽  
False Positive Rate ◽  
Virus Detection ◽  
Deep Belief Network ◽  
Immune Network ◽  
Artificial Immune ◽  
High Detection Rate ◽  
Smart System ◽  
Belief Network ◽  
Artificial Immune Network

This paper proposes a smart system of virus detection that can classify a file as benign or malware with high accuracy detection rate. The approach is based on the aspects of the artificial immune system and the deep learning technique. The first stage is data extraction to create the main feature set. In the second stage, the Artificial Immune Network (aiNet) is used to build a clonal generation of malware detectors and improve the accuracy of unknown virus detection rate. Then they are trained with a deep belief network model to evaluate the performance of the system. As a result, our method can achieve a high detection rate of 98.86% on average with a very low false positive rate.

A Negative Selection Algorithm Based on Hierarchical Clustering of Self Set

2010 ◽  
Vol 121-122 ◽  
pp. 486-489
Author(s):  
Wen Chen ◽  
Tao Li ◽  
Xiao Jie Liu ◽  
Yuan Quan Shi
Keyword(s):  
Negative Selection ◽  
The Self ◽  
Hierarchical Level ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Data Set ◽  
Cluster Radius ◽  
Negative Selection Algorithm ◽  
Value Range ◽  
Level Cluster

In this article, we proposed a negative selection algorithm which based on hierarchical level cluster of self dataset CB-RNSA. First the self data set is clustered by different cluster radius, and then the self data are substituted by cluster centers to compare with candidate detectors to reduce the number of distance counting. In the detector creating process, the value of each detector property was restricted to a given value range so as to decrease the redundancy of detectors. The stimulation result shows that CB-RNSA is an effective algorithm for the creation of artificial immune detectors.

Artificial Immune Systems Applied to the Analysis of Structural Integrity of a Building

2014 ◽  
Vol 472 ◽  
pp. 544-549 ◽  
Author(s):  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette ◽  
Simone Silva Frutuoso de Souza ◽  
Adriano dos Santos e Souza ◽  
Mara Lúcia Martins Lopes
Keyword(s):  
Negative Selection ◽  
Structural Integrity ◽  
Artificial Immune Systems ◽  
Structural Failure ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Immune Systems ◽  
Negative Selection Algorithm ◽  
Identification And Characterization

This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This paper presents the application of artificial immune systems for analysis of the structural integrity of a building. Inspired by a biological process, it uses the negative selection algorithm to perform the identification and characterization of structural failure. This methodology can assist professionals in the inspection of mechanical and civil structures, to identify and characterize flaws, in order to perform preventative maintenance to ensure the integrity of the structure and decision-making. In order to evaluate the methodology was made modeling a two-story building and several situations were simulated (base-line condition and improper conditions), yielding a database of signs, which were used as input data for the negative selection algorithm. The results obtained by the present method efficiency, robustness and accuracy.

scholarly journals An Immune Based Patient Anomaly Detection using RFID Technology

2013 ◽  
Vol 2 (1) ◽  
pp. 121-142 ◽  
Author(s):  
Sri Listia Rosa ◽  
Siti Mariyam Shamsuddin ◽  
Evizal Evizal
Keyword(s):  
Immune System ◽  
Monitoring System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Intelligent System ◽  
Patient Data ◽  
Rfid Technology ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Negative Selection Algorithm

Detecting of anomalies patients data are important to gives early alert to hospital, in this paper will explore on anomalies patient data detecting and processing using artificial computer intelligent system. Artificial Immune System (AIS) is an intelligent computational technique refers to human immunology system and has been used in many areas such as computer system, pattern recognition, stock market trading, etc. In this case, real value negative selection algorithm (RNSA) of artificial immune system used for detecting anomalies patient body parameters such as temperature. Patient data from monitoring system or database classified into real valued, real negative selection algorithm results is real values deduction by RNSA distance, the algorithm used is minimum distance and the value of detector generated for the algorithm. The real valued compared with the distance of data, if the distance is less than a RNSA detector distance then data classified into abnormal. To develop real time detecting and monitoring system, Radio Frequency Identification (RFID) technology has been used in this system. Keywords: AIS, RNSA, RFID, AbnormalDOI: 10.18495/comengapp.21.121142

scholarly journals DETECÇÃO DE FALHAS ESTRUTURAIS DE UM PRÉDIO DE DOIS ANDARES UTILIZANDO SISTEMA IMUNOLÓGICO ARTIFICIA

2019 ◽  
Vol 11 (4) ◽  
pp. 73-84
Author(s):  
Thiago Carreta Moro ◽  
Fábio Roberto Chavarette ◽  
Luiz Gustavo Pereira Roéfero ◽  
Roberto Outa
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Structural Integrity ◽  
Selection Algorithm ◽  
Artificial Immune ◽  
Intelligent Computing ◽  
Negative Selection Algorithm ◽  
Story Building

This work presents the innovative proposal for the development of SHMs with focus on physical cyber systems applied in a two-story building based on Intelligent Computing (CI) techniques, the negative selection algorithm from the Artificial Immune System, to perform the analysis and monitoring of structural integrity in a building.

A Smart System of Malware Detection Based on Artificial Immune Network and Deep Belief Network

2021 ◽  
Vol 15 (1) ◽  
pp. 1-25
Author(s):  
Dung Hoang Le ◽  
Nguyen Thanh Vu ◽  
Tuan Dinh Le
Keyword(s):  
Detection Rate ◽  
Binary Classification ◽  
False Positive Rate ◽  
Virus Detection ◽  
Immune Network ◽  
Classification Problems ◽  
Artificial Immune ◽  
Smart System ◽  
Artificial Immune Network ◽  
Positive Rate

This paper proposes a smart system of virus detection that can classify a file as benign or malware with high accuracy detection rate. The approach is based on the aspects of the artificial immune system, in which an artificial immune network is used as a pool to create and develop virus detectors that can detect unknown data. Besides, a deep learning model is also used as the main classifier because of its advantages in binary classification problems. This method can achieve a detection rate of 99.08% on average, with a very low false positive rate.

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