Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication

Gu, Zheng; Nazir, Shah; Hong, Cheng Shong; Khan, Sulaiman

doi:10.1155/2020/8830903

Cited by 22 publications

(9 citation statements)

References 47 publications

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“…Various applications of neural networks are available [18][19][20][21]. These applications exists from diverse perspective.…”

Section: Analysis Of Advantages Of Neural Network Prediction Methodsmentioning

confidence: 99%

House Price Prediction Model Based on Neural Network

Wang¹

2021

Preprint

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Through an in-depth understanding of house price prediction issues, the paper aims to establish a BP neural network model for house price prediction based on ideas and methods of the BP neural network. By the BP neural network method, the paper realizes sorting, statistics, and analysis of house prices in Chongqing for 10 years as well as the main factor analysis of house prices. Then, correlative analysis is conducted with the software on house prices and their influential factors. Significant correlations are manifested. MATLAB was used to compile relevant programs and a BP neural network model was established for prediction and verification of house prices. Through the market survey, house prices are finally predicted by software.

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“…Various applications of neural networks are available [18][19][20][21]. These applications exists from diverse perspective.…”

Section: Analysis Of Advantages Of Neural Network Prediction Methodsmentioning

confidence: 99%

House Price Prediction Model Based on Neural Network

Wang¹

2021

Preprint

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“…e combination of the attributes of BRNN and LSTM models is collectively known as BLSTM. Gu et al [19] proposed the convolution neural network for intrusion identification purposes, while He et al [20] proposed the LSTM-based classification model for the development of the trusted model for pervasive computing. Figure 1 shows the conventional model for the BLSTM architecture.…”

Section: Deep Learning-based Blstm Modelmentioning

confidence: 99%

Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers

Wang

Khan

et al. 2020

Complexity

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With the increase in the number of electronic devices and developments in the communication system, security becomes one of the challenging issues. Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. Such communication needs a secure medium through which users can communicate in a secure and reliable way so that their information may not be lost. The proposed study is an endeavor toward the detection of phishing by using random forest and BLSTM classifiers. The experimental results of the proposed study are promising in phishing detection, and the study reflects the applicability of the proposed algorithms in the information security. The experimental results show that the BLSTM-based phishing detection model is prominent in ensuring the network security by generating a recognition rate of 95.47% compared to the conventional RF-based model that generates a recognition rate of 87.53%. This high recognition rate for the BLSTM-based model reflects the applicability of the proposed model for phishing detection.

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“…ese devices are communicating with each other and facilitating human life. e connection of these devices has provided openings directions for the smart applications which are one of the growing areas of research [1][2][3]. Among these opportunities, security and privacy are considered to be one of the major issues for researchers to tackle.…”

Section: Introductionmentioning

confidence: 99%

Acquiring Data Traffic for Sustainable IoT and Smart Devices Using Machine Learning Algorithm

Nazir

et al. 2021

Security and Communication Networks

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Billions of devices are connected via the Internet which has produced various challenges and opportunities. The increase in the number of devices connected to the Internet of things (IoT) is nearly beyond imagination. These devices are communicating with each other and facilitating human life. The connection of these devices has provided opening directions for the smart applications which are one of the growing areas of research. Among these opportunities, security and privacy are considered to be one of the major issues for researchers to tackle. Proper security measures can prevent attackers from interrupting the security of IoT network inside the smart city for secure data traffic. Keeping in view the security consideration of data traffic for smart devices and IoT, the proposed study presented machine learning algorithms for securing the data traffic based on a firewall for smart devices and IoT network. The study has used the dataset of “Firewall” for validation purposes. The experimental results of the approach show that the hybrid deep learning model (based on convolution neural network and support vector machine) outperforms than decision1 rules and random forest by generating a recognition rate of 95.5% for the hybrid model, 68.5% for decision rules, and 78.3% accuracy for random forest. The validity of the proposed model is also tested based on other performance metrics such as f score, error rate, recall, and precision. This high accuracy rate and other performance values show the applicability of the proposed hybrid model to secure data traffic purposes in smart devices. This can be used in many research areas of the smart city for security purposes.

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Convolution Neural Network-Based Higher Accurate Intrusion Identification System for the Network Security and Communication

Cited by 22 publications

References 47 publications

House Price Prediction Model Based on Neural Network

House Price Prediction Model Based on Neural Network

Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers

Acquiring Data Traffic for Sustainable IoT and Smart Devices Using Machine Learning Algorithm

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