GM-NAINO: Global Mutation based Novel Artificial Immune Network Optimization for Enhancing Data Security in Public Cloud Storage System

In recent years, numerous research works have been established to obtain secure data in the cloud storage system. But the data privacy regarding information outsourcing on cloud services is considered a crucial problem. In order to provide secure data, it is necessary to encrypt the information before storing it in the public cloud storage system. To provide security and data integrity during encryption and decryption, this paper proposes a global mutation-based novel artificial immune network optimization algorithm for RSA cryptosystem. Here, the Global Mutation Based Novel Artificial Immune Network Optimization (GM-NAINO) Algorithm is employed to attain optimal generation of keys thereby enhancing safe and secure data transmission and improving the data integrity during the transmission of data. Thus, the proposed GM-NAINO based RSA framework provides an effective system in improving data integrity. In addition to this, to determine the effectiveness of the proposed GM-NAINO algorithm seven benchmark functions are utilized in this paper. The performance evaluation and the comparative analysis are carried out and the proposed GM-NAINO based RSA framework outperforms other approaches.

Identification of cavitation in centrifugal pump by artificial immune network

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.

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

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.

Artificial Immune Network Clustering Based on a Cultural Algorithm

Data mining technology has been applied in many fields. Prototype-based cluster analysis is an important data mining method, but its ability to discover knowledge is limited because of the need to know the number of target data categories and cluster prototypes in advance. Artificial immune evolutionary network clustering is a clustering method based on network structure. Compared with prototype-based cluster analysis, it has the advantage of realizing unsupervised learning and clustering without any prior knowledge of data. However, artificial immune evolutionary network clustering also has problems such as a lack of guidance in the clustering process, fuzzy boundary sensitivity, and difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. First, three kinds of knowledge are constructed: normative knowledge is used to regulate the spatial range of population initialization to avoid blindness; state knowledge is used to distinguish the type of antigen, and immune defense measures are taken to prevent the network structure caused by noise and boundaries from being unclear; topology knowledge is used to guide the antigen for optimal antibody search. Second, topology knowledge in the cultural algorithm is used to characterize the distribution of antigens and antibodies in space, and elite learning is used to improve the traditional clone mutation operator. Based on the shadow set theory, a method for adaptively determining the compression threshold is proposed. Finally, the results of simulation experiments show that the proposed algorithm can effectively overcome the above problems, and the clustering performances on a synthetic dataset and an actual dataset are satisfactory.

Artificial Immune Network Clustering Based on a Cultural Algorithm

There are some problems in evolutionary immune network clustering, such as the lack of guidance in the clustering process, the sensitivity of the fuzzy boundary and the difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. Three kinds of knowledge are constructed: normative knowledge is used to standardize the spatial scope of population initialization, avoiding blindness; state knowledge is used to distinguish antigens and take immune defense measures to prevent noise and unclear network structure caused by boundary; topology knowledge is used to guide the optimal antibody search. The clone mutation operation of the traditional method is improved, and a compression threshold adaptive determination method is proposed based on the shadow sets theory. The experimental results show that the proposed method can effectively overcome the above problems, and the clustering performance on a synthetic dataset and an actual dataset is satisfactory.