A Step-Based Deep Learning Approach for Network Intrusion Detection

Zhang, Yanyan; Ran, Xiangjin

doi:10.32604/cmes.2021.016866

Cited by 8 publications

(11 citation statements)

References 47 publications

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“…Currently, deep learning has been introduced into the field of NIDS and has achieved successful applications. Random forest, long and short-term memory (LSTM) network and other network architectures can be used as basic classifiers to construct network intrusion algorithms, which will help achieve highly effective intrusion detection analysis [5,6].…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

Xiang

2023

J Wireless Com Network

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Network intrusion detection system (NIDS) can effectively sense network attacks, which is of great significance for maintaining the security of cyberspace. To meet the requirements of efficient and accurate network status monitoring, a NIDS model using Transformer-based fusion deep learning architecture is proposed. Firstly, GAN-Cross is used to expand minority class sample data, thereby alleviating the issues of imbalanced minority class about the original dataset. Then, the Transformer module is used to adjust the ML-CNN-BiLSTM model to enhance the feature encoding ability of the intrusion model. Finally, the data enhancement model and feature enhancement model are integrated into the NIDS model, the detection model is optimized, the features of network state data are extracted at a deeper level, and the generalization ability of the detection model is enhanced. Some simulation experiments using UNSW-NB15 datasets show that the proposed fusion architecture can achieve efficient analysis of complex network traffic datasets, with an accuracy of 0.903, effectively improving the detection accuracy of NIDS and its ability to detect unknown attacks. The proposed model has good application value in ensuring the stable operation of network systems.

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Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

Xiang

2023

J Wireless Com Network

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“…Other applications of deep learning include a real-time maskless-face detector using deep residual networks [356], topology optimization with embedded physical law and physical constraints [357], prediction of stress-strain relations in granular materials from triaxial test results [358], surrogate model for flight-load analysis [359], classification of domestic refuse in medical institutions based on transfer learning and convolutional neural network [360], convolutional neural network for arrhythmia diagnosis [361], e-commerce dynamic pricing by deep reinforcement learning [362], network intrusion detection [363], road pavement distress detection for smart maintenance [364], traffic flow statistics [365], multi-view gait recognition using deep CNN and channel attention mechanism [366], mortality risk assessment of ICU patients [367], stereo matching method based on space-aware network model to reduce the limitation of GPU RAM [368], air quality forecasting in Internet of Things [369], analysis of cardiac disease abnormal ECG signals [370], detection of mechanical parts (nuts, bolts, gaskets, etc.) by machine vision [371], asphalt road crack detection [372], steel commondity selection using bidirectional encoder representations from transformers (BERT) [373], short-term traffic flow prediction using LSTM-XGBoost combination model [374], emotion analysis based on multi-channel CNN in social networks [375].…”

Section: Cmes 2023mentioning

confidence: 99%

Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

Vu‐Quoc¹,

Humer²

2023

Computer Modeling in Engineering &Amp; Sciences

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“…Valuable emotional information is extracted. Second, a classification method based on machine learning [20] is used to classify the extracted Weibo information, including the classification of subjective and objective information and the sentiment classification of subjective information, as well as the summary of core ideas. Finally, the machineprocessed sentiment analysis results are presented to the user, categorized by positive, negative, and neutral.…”

Section: User Portrait Based On Weibo Data With Text Featurementioning

confidence: 99%

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

Dong¹,

Liang²,

Yu³

et al. 2023

Computers, Materials &Amp; Continua

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With the frequent occurrence of telecommunications and network fraud crimes in recent years, new frauds have emerged one after another which has caused huge losses to the people. However, due to the lack of an effective preventive mechanism, the police are often in a passive position. Using technologies such as web crawlers, feature engineering, deep learning, and artificial intelligence, this paper proposes a user portrait fraud warning scheme based on Weibo public data. First, we perform preliminary screening and cleaning based on the keyword "defrauded" to obtain valid fraudulent user Identity Documents (IDs). The basic information and account information of these users is user-labeled to achieve the purpose of distinguishing the types of fraud. Secondly, through feature engineering technologies such as avatar recognition, Artificial Intelligence (AI) sentiment analysis, data screening, and follower blogger type analysis, these pictures and texts will be abstracted into user preferences and personality characteristics which integrate multidimensional information to build user portraits. Third, deep neural network training is performed on the cube. 80% percent of the data is predicted based on the N-way K-shot problem and used to train the model, and the remaining 20% is used for model accuracy evaluation. Experiments have shown that Few-short learning has higher accuracy compared with Long Short Term Memory (LSTM), Recurrent Neural Networks (RNN) and Convolutional Neural Network (CNN). On this basis, this paper develops a WeChat small program for early warning of telecommunications network fraud based on user portraits. When the user enters some personal information on the front end, the back-end database can perform correlation analysis by itself, so as to match the most likely fraud types and give relevant early warning information. The fraud warning model is highly scaleable. The data of other Applications (APPs) can be extended to further improve the efficiency of anti-fraud which has extremely high public welfare value.

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A Step-Based Deep Learning Approach for Network Intrusion Detection

Cited by 8 publications

References 47 publications

RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

An Early Warning Model of Telecommunication Network Fraud Based on User Portrait

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