The Research of Intrusion Detection Based on Intelligentization

2014 ◽  
Vol 490-491 ◽  
pp. 1588-1591
Author(s):  
Liang Zhang ◽  
Hao Yue Sun ◽  
Guo Lv ◽  
Xiao Lu Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Convergence Speed ◽  
Global Property ◽  
Detection Accuracy ◽  
Accuracy Rate ◽  
The Neural Network

In this paper, the intelligentized way is applied to detecting anomaly intrusion. Based on the global property of genetic algorithm and the locality of neural network, this method effectively improves the convergence speed of the network and the detection accuracy rate. It not only avoids the defect of the neural network, but also improves the precision.

Implementasi Algoritma Genetika dan Neural Network Pada Aplikasi Peramalan Produksi Mie

2017 ◽  
Vol 9 (1) ◽  
pp. 37-41
Author(s):  
Adhi Kusnadi ◽  
Jansen Pratama
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Optimization ◽  
Test Results ◽  
Accuracy Rate ◽  
Neural Network Optimization ◽  
Production Forecasting ◽  
The Neural Network ◽  
Crossover Probability ◽  
Hidden Layer

Companies that produce products must be able to regulate the amount of production so that it have plan production. Therefore, it is necessary to be able to predict the amount of production. This research aims to create an application that is useful in determining the amount of production. These applications using genetic algorithms and neural network. Genetic algorithm is used to optimize the weights in the neural network. From the test results, this application uses network with 12 inputs, 5 neuron in first hidden layer, 3 neurons in the second hidden layer, and 3 neurons in the last hidden layer. Then for the genetic algorithm parameters used were 10 individuals, 50 generations, crossover probability 0.8 and mutations probability 0.1. Based on the test results, this application has the forecasting’s accuracy rate reaches 86%. Keyword : forecasting, production forecasting, genetic algorithm neural network, optimization.

The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network

2014 ◽  
Vol 599-601 ◽  
pp. 726-730 ◽  
Author(s):  
Gang Ke ◽  
Ying Han Hong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Detection System ◽  
Training Sample ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion

The traditional BP neural network algorithm is applied to intrusion detection system, detection speed slow and low detection accuracy. In order to solve the above problems, this paper proposes a network intrusion detection algorithm using genetic algorithms to optimize neural network weights. which find the most suitable weights of BP neural network by the genetic algorithm, and uses the optimized BP neural network to learn and detect the network intrusion detection data. Matlab simulation results show that the training sample time of the algorithm is shorter, has good intrusion recognition and detection effect, compared with the traditional network intrusion detection algorithm.

scholarly journals Optimizing the hardness of SLA printed objects by using the neural network and genetic algorithm

2019 ◽  
Vol 38 ◽  
pp. 117-124
Author(s):  
Guang Hu ◽  
Zhi Cao ◽  
Michael Hopkins ◽  
Conor Hayes ◽  
Mark Daly ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
The Neural Network

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

The Research on Recognition of the Irregular Objects

2012 ◽  
Vol 241-244 ◽  
pp. 1602-1607
Author(s):  
Guang Hai Han ◽  
Xin Jun Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Know How ◽  
The Neural Network ◽  
Variable Step ◽  
Adaptive Momentum ◽  
The Way

It usually need different ways to process different objects in the manufacturing, Therefore, firstly we need to distinguish the categories of objects to be processed, then the machine will know how to deal with the objects. In order to automatically recognize the category of the irregular object, this paper extracted the improved Hu's moments of each object as the feature by the way of processing images of the working platform that the irregular objects are putting on. This paper adopts the variable step BP neural network with adaptive momentum factor as the classifier. The experiment shows that this method can effectively distinguish different irregular objects, and during the training of the neural network, it has faster convergence speed and better approximation compared with the traditional BP neural network

scholarly journals RICNN: A ResNet&Inception convolutional neural network for intrusion detection of abnormal traffic

2021 ◽  
pp. 55-55
Author(s):  
Benhui Xia ◽  
Dezhi Han ◽  
Ximing Yin ◽  
Gao Na
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Detection Rate ◽  
Recognition Rate ◽  
Identification Accuracy ◽  
Detection Accuracy ◽  
Traffic Classification ◽  
High Detection Rate ◽  
Network Intrusion

To secure cloud computing and outsourced data while meeting the requirements of automation, many intrusion detection schemes based on deep learn ing are proposed. Though the detection rate of many network intrusion detection solutions can be quite high nowadays, their identification accuracy on imbalanced abnormal network traffic still remains low. Therefore, this paper proposes a ResNet &Inception-based convolutional neural network (RICNN) model to abnormal traffic classification. RICNN can learn more traffic features through the Inception unit, and the degradation problem of the network is eliminated through the direct map ping unit of ResNet, thus the improvement of the model?s generalization ability can be achievable. In addition, to simplify the network, an improved version of RICNN, which makes it possible to reduce the number of parameters that need to be learnt without degrading identification accuracy, is also proposed in this paper. The experimental results on the dataset CICIDS2017 show that RICNN not only achieves an overall accuracy of 99.386% but also has a high detection rate across different categories, especially for small samples. The comparison experiments show that the recognition rate of RICNN outperforms a variety of CNN models and RNN models, and the best detection accuracy can be achieved.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

scholarly journals Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Chao Wang ◽  
Bailing Wang ◽  
Yunxiao Sun ◽  
Yuliang Wei ◽  
Kai Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Control Systems ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Open Set ◽  
The Neural Network ◽  
Center Loss ◽  
Artificial Neural

The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become connected to the Internet, increasing the potential of cyberattacks. Because ICSs are so tightly linked to human lives, any harm to them could have disastrous implications. As a technique of providing protection, many intrusion detection system (IDS) studies have been conducted. However, because of the complicated network environment and rising means of attack, it is difficult to cover all attack classes, most of the existing classification techniques are hard to deploy in a real environment since they cannot deal with the open set problem. We propose a novel artificial neural network based-methodology to solve this problem. Our suggested method can classify known classes while also detecting unknown classes. We conduct research from two points of view. On the one hand, we use the openmax layer instead of the traditional softmax layer. Openmax overcomes the limitations of softmax, allowing neural networks to detect unknown attack classes. During training, on the other hand, a new loss function termed center loss is implemented to improve detection ability. The neural network model learns better feature representations with the combined supervision of center loss and softmax loss. We evaluate the neural network on NF-BoT-IoT-v2 and Gas Pipeline datasets. The experiments show our proposed method is comparable with the state-of-the-art algorithm in terms of detecting unknown classes. But our method has a better overall classification performance.

scholarly journals Intrusion Detection Method Based on Adaptive Clonal Genetic Algorithm and Backpropagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Lu ◽  
Menghan Liu ◽  
Jie Zhou ◽  
Zhigang Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Poor Performance ◽  
Data Sets ◽  
Backpropagation Neural Network ◽  
External Threats ◽  
Kdd Cup 99

Intrusion Detection System (IDS) is an important part of ensuring network security. When the system faces network attacks, it can identify the source of threats in a timely and accurate manner and adjust strategies to prevent hackers from intruding. Efficient IDS can identify external threats well, but traditional IDS has poor performance and low recognition accuracy. To improve the detection rate and accuracy of IDS, this paper proposes a novel ACGA-BPNN method based on adaptive clonal genetic algorithm (ACGA) and backpropagation neural network (BPNN). ACGA-BPNN is simulated on the KDD-CUP’99 and UNSW-NB15 data sets. The simulation results indicate that, in contrast to the methods based on simulated annealing (SA) and genetic algorithm (GA), the detection rate and accuracy of ACGA-BPNN are much higher than of GA-BPNN and SA-BPNN. In the classification results of KDD-CUP’99, the classification accuracy of ACGA-BPNN is 11% higher than GA-BPNN and 24.2% higher than SA-BPNN, and F-score reaches 99.0%. In addition, ACGA-BPNN has good global searchability and its convergence speed is higher than that of GA-BPNN and SA-BPNN. Furthermore, ACGA-BPNN significantly improves the overall detection performance of IDS.

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