A Hierarchical Artificial Immune Model for Virus Detection

2009 ◽  
Author(s):  
Wei Wang ◽  
Pengtao Zhang ◽  
Ying Tan ◽  
Xingui He
Keyword(s):  
Virus Detection ◽  
Artificial Immune

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.

scholarly journals A Combination of Artificial Neural Network and Artificial Immune System for Virus Detection

2016 ◽  
Author(s):  
Mai Trong Khang ◽  
Vu Thanh Nguyen ◽  
Tuan Dinh Le
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
False Positive Rate ◽  
Virus Detection ◽  
Malicious Code ◽  
Immune Network ◽  
Artificial Immune ◽  
Positive Rate ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper, we propose an Artificial Neural Immune Network (ANIN) for virus detection. ANIN is a combination of Artificial Neural Network (ANN) and Artificial Immune Network (AiNet). In ANIN, each ANN is considered as a detector. A pool of initial detectors then undergoes a mature process, called AiNet, to improve its recognizing ability. Thus, more than one ANN objects can cooperate to detect malicious code. The experimental results show that ANIN can achieve a detection rate of 87.98% on average with an acceptable false positive rate.

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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