scholarly journals An Optimization Method for Intrusion Detection Classification Model Based on Deep Belief Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 87593-87605 ◽  
Author(s):  
Peng Wei ◽  
Yufeng Li ◽  
Zhen Zhang ◽  
Tao Hu ◽  
Ziyong Li ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Optimization Method ◽  
Deep Belief Network ◽  
Classification Model ◽  
Belief Network ◽  
Model Based

scholarly journals Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5529 ◽  
Author(s):  
Xiaopeng Tan ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
Xiaojun Guo ◽  
Xiaoyong Sun
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Simulated Annealing Algorithm ◽  
Rapid Development ◽  
Pso Algorithm ◽  
Deep Belief Network ◽  
Classification Model ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Belief Network

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.

scholarly journals MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Surendran Rajendran ◽  
Osamah Ibrahim Khalaf ◽  
Youseef Alotaibi ◽  
Saleh Alghamdi
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Selection Process ◽  
Data Classification ◽  
Deep Belief Network ◽  
Feature Subset Selection ◽  
Classification Model ◽  
Feature Subset ◽  
Belief Network ◽  
Big Data Classification

AbstractIn recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.

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