Multi-layer perceptron for network intrusion detection

Rosay, Arnaud; Riou, Kevin; Carlier, Florent; Leroux, Pascal

doi:10.1007/s12243-021-00852-0

Cited by 22 publications

(16 citation statements)

References 44 publications

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“…First, the factors affecting the difficulty of oral English were extracted, the extraction features were screened based on regularization, and then, the improved MLP prediction model was constructed and predicted. Finally, the difficulty of oral English was assessed based 2, are selected to screen the above features [19][20][21][22].…”

Section: Oral English Automatic Methods Based On Improved Mlpmentioning

confidence: 99%

Automatic Evaluation Method of Oral English Based on Multiplex Mode

Chi

2022

Wireless Communications and Mobile Computing

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For the problem of the low accuracy of large-scale oral English, based on oral English from the perspective of difficulty of oral English text and phonology, a multimodal-based automatic oral English assessment method is proposed by using L2 regularized (multilayer perceptron, MLP) and 9 features affecting the automated assessment of oral English as model input. Simulation results show that the proposed method can well predictively assess oral English difficulty and performs well on RMSE and R2 metrics of 0.053 and 0.905, respectively, with certain advantages over conventional elastic network-based and random forest-based prediction models.

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Section: Oral English Automatic Methods Based On Improved Mlpmentioning

confidence: 99%

Automatic Evaluation Method of Oral English Based on Multiplex Mode

Chi

2022

Wireless Communications and Mobile Computing

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“…Over the years, many data sets related to intrusion detection have been introduced for research and development, including KDDCup99 [ 45 ], UNSW-NB15 [ 46 ], NSL-KDD [ 47 ], and CIC-IDS2017 [ 48 ]. In this paper, we choose to use the UNSW-NB15, NSL-KDD, and CIC-IDS2017 data sets to evaluate the proposed model.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Network Intrusion Detection Technology Based on Convolutional Neural Network and BiGRU

Cao

Song

et al. 2022

Computational Intelligence and Neuroscience

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To solve the problem of low accuracy and high false-alarm rate of existing intrusion detection models for multiple classifications of intrusion behaviors, a network intrusion detection model incorporating convolutional neural network and bidirectional gated recurrent unit is proposed. To solve the problems of many dimensions of features and imbalance of positive and negative samples in the original traffic data, sampling processing is performed with the help of a hybrid sampling algorithm combining ADASYN and RENN, and feature selection is performed by combining random forest algorithm and Pearson correlation analysis; after that, spatial features are extracted by the convolutional neural network, and further features are extracted by incorporating average pooling and max pooling, and then BiGRU is used to extracts long-distance dependent information features to achieve comprehensive and effective feature learning. Finally, the Softmax function is used for classification. In this paper, the proposed model is evaluated on the UNSW_NB15, NSL-KDD, and CIC-IDS2017 data sets with an accuracy of 85.55%, 99.81%, and 99.70%, which is 1.25%, 0.59%, and 0.27% better than the same type model of CNN-GRU.

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“…From Ensemble ML algorithms [ 66 ], Bagging [ 67 ], Random Forest (RF) [ 68 ], Rotation Forest (RotF) [ 69 ], AdaBoostM1 [ 70 ], Voting [ 71 ], and Stacking [ 72 ] were exploited. Finally, a simple Artificial Neural Network (ANN) [ 73 ], the Multi-Layer Perceptron (MLP) [ 74 ] and K-Nearest Neighbors (kNN) [ 53 ], a distance-based classifier, were evaluated.…”

Section: Materials and Methodsmentioning

confidence: 99%

Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2

Δρίτσας

Trigka

2022

Sensors

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The coronavirus disease (COVID-19) pandemic was caused by the SARS-CoV-2 virus and began in December 2019. The virus was first reported in the Wuhan region of China. It is a new strain of coronavirus that until then had not been isolated in humans. In severe cases, pneumonia, acute respiratory distress syndrome, multiple organ failure or even death may occur. Now, the existence of vaccines, antiviral drugs and the appropriate treatment are allies in the confrontation of the disease. In the present research work, we utilized supervised Machine Learning (ML) models to determine early-stage symptoms of SARS-CoV-2 occurrence. For this purpose, we experimented with several ML models, and the results showed that the ensemble model, namely Stacking, outperformed the others, achieving an Accuracy, Precision, Recall and F-Measure equal to 90.9% and an Area Under Curve (AUC) of 96.4%.

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Multi-layer perceptron for network intrusion detection

Cited by 22 publications

References 44 publications

Automatic Evaluation Method of Oral English Based on Multiplex Mode

Automatic Evaluation Method of Oral English Based on Multiplex Mode

Network Intrusion Detection Technology Based on Convolutional Neural Network and BiGRU

Supervised Machine Learning Models to Identify Early-Stage Symptoms of SARS-CoV-2

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