An abstract model of an artificial immune network based on a classifier committee for biometric pattern recognition by the example of keystroke dynamics

An abstract model of an artificial immune network (AIS) based on a classifier committee and robust learning algorithms (with and without a teacher) for classification problems, which are characterized by small volumes and low representativeness of training samples, are proposed. Evaluation of the effectiveness of the model and algorithms is carried out by the example of the authentication task using keyboard handwriting using 3 databases of biometric metrics. The AIS developed possesses emergence, memory, double plasticity, and stability of learning. Experiments have shown that AIS gives a smaller or comparable percentage of errors with a much smaller training sample than neural networks with certain architectures.

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

International Journal of Information Security and Privacy ◽

10.4018/ijisp.2021010101 ◽

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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A Fault Diagnosis Method Based on Artificial Immune Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.385-386.658 ◽

2013 ◽

Vol 385-386 ◽

pp. 658-662 ◽

Cited By ~ 4

Author(s):

Wen Ke Jiang ◽

Yu Juan Chen ◽

Jing Zhang

Keyword(s):

Fault Diagnosis ◽

Success Rate ◽

Basis Function ◽

Immune Network ◽

Artificial Immune ◽

Training Samples ◽

Artificial Immune Network ◽

Diagnosis Method ◽

Gear Case ◽

Hidden Layer

A new fault diagnosis method based on artificial immune network is proposed. The network combined aiNet with radial basis function (RBF) NN. The structure of the network proposed is the same as RBF NN. The training samples are clustered first by the improved aiNet algorithm. The centers of the clustering are saved as the centers of the hidden layer, therefore, the amount and positions of nodes in the hidden layer can be determined automatically. The weight matrix is determined by least squares (LS) algorithm. The network is applied to fault diagnosis of CJK6136 spindle gear case. The results of the experiments confirm the performance of the proposed network through comparing with RBF NN under the same conditions. The diagnosis success rate for the network proposed was 99%, while that for RBF NN is 89.5%.

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A New Artificial Immune Network Algorithm for Classifying Complex Data

JOURNAL OF ELECTRONICS INFORMATION TECHNOLOGY ◽

10.3724/sp.j.1146.2009.00309 ◽

2010 ◽

Vol 32 (3) ◽

pp. 515-521 ◽

Cited By ~ 4

Author(s):

Ruo-chen Liu ◽

Man-chun Niu ◽

Li-cheng Jiao

Keyword(s):

Complex Data ◽

Immune Network ◽

Artificial Immune ◽

Network Algorithm ◽

Artificial Immune Network

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Identification of cavitation in centrifugal pump by artificial immune network

Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering ◽

10.1177/09544089211028402 ◽

2021 ◽

pp. 095440892110284

Author(s):

Seyed M Matloobi ◽

Mohammad Riahi

Keyword(s):

Immune System ◽

Production Systems ◽

Principal Component ◽

Immune Network ◽

Centrifugal Pumps ◽

Artificial Immune ◽

Human Immune System ◽

Time Frequency ◽

Artificial Immune Network ◽

Damage Condition

Reducing the cost of unscheduled shutdowns and enhancing the reliability of production systems is an important goal for various industries; this could be achieved by condition monitoring and artificial intelligence. Cavitation is a common undesired phenomenon in centrifugal pumps, which causes damage and its detection in the preliminary stage is very important. In this paper, cavitation is identified by use of vibration and current signal and artificial immune network that is modeled on the base of the human immune system. For this purpose, first data collection were done by a laboratory setup in health and five stages damage condition; then various features in time, frequency, and time–frequency were extracted from vibration and current signals in addition to pressure and flow rate; next feature selection and dimensions reduction were done by artificial immune method to use for classification; finally, they were used by artificial immune network and some other methods to identify the system condition and classification. The results of this study showed that this method is more accurate in the detection of cavitation in the initial stage compared to methods such as non-linear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means with the same data. Also, selected features with artificial immune system were better than principal component analysis results.

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The integration of association rule mining and artificial immune network for supplier selection and order quantity allocation

Applied Mathematics and Computation ◽

10.1016/j.amc.2014.11.015 ◽

2015 ◽

Vol 250 ◽

pp. 958-972 ◽

Cited By ~ 7

Author(s):

R.J. Kuo ◽

C.M. Pai ◽

R.H. Lin ◽

H.C. Chu

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Supplier Selection ◽

Immune Network ◽

Order Quantity ◽

Artificial Immune ◽

Rule Mining ◽

Artificial Immune Network

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An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

Mathematical Problems in Engineering ◽

10.1155/2015/839081 ◽

2015 ◽

Vol 2015 ◽

pp. 1-14

Author(s):

Mengling Zhao ◽

Hongwei Liu

Keyword(s):

Association Rules ◽

Large Scale ◽

Classification Algorithm ◽

Data Sets ◽

Immune Network ◽

Artificial Immune ◽

Mining Algorithm ◽

Sar Image Segmentation ◽

Artificial Immune Network ◽

Adaptive Pso

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

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