Accuracy Of Detection Model Research Articles

A novel data-driven integrated detection method for network intrusion classification based on multi-feature imbalanced data

The multi-feature and imbalanced nature of network data has always been a challenge to be overcome in the field of network intrusion detection. The redundant features in data could reduce the overall quality of network data and the accuracy of detection models, because imbalance could lead to a decrease in the detection rate for minority classes. To improve the detection accuracy for imbalanced intrusion data, we develop a data-driven integrated detection method, which utilizes Recursive Feature Elimination (RFE) for feature selection, and screens out features that are conducive to model recognition for improving the overall quality of data analysis. In this work, we also apply the Adaptive Synthetic Sampling (ADASYN) method to generate the input data close to the original dataset, which aims to eliminate the data imbalance in the studied intrusion detection model. Besides, a novel VGG-ResNet classification algorithm is also proposed via integrating the convolutional block with the output feature map size of 128 from the Visual Geometry Group 16 (VGG16) of the deep learning algorithm and the residual block with output feature map size of 256 from the Residual Network 18 (ResNet18). Based on the numerical results conducted on the well-known NSL-KDD dataset and UNSW-NB15 dataset, it illustrates that our method can achieve the accuracy rates of 86.31% and 82.56% in those two test datasets, respectively. Moreover, it can be found that the present algorithm can achieve a better accuracy and performance in the experiments of comparing our method with several existing algorithms proposed in the recent three years.

TSI-based hierarchical clustering method and regular-hypersphere model for product quality detection

In modern industry, the production process is always accompanied by a large amount of high-dimensional data, which is always too sparse to provide sufficient information for quality detection. Moreover, traditional quality detection methods show the ambiguity and uncertainty in the clustering process. To overcome these difficulties, an improved hierarchical clustering method and a hypersphere model are proposed respectively in this paper. First, the weights of the minimum spanning tree in each cluster are used to calculate the intra-cluster dispersion and the inter-cluster expansion. Next, to enhance the effectiveness of the clustering process, a threshold-based scheduling index is designed to determine whether the merging process for different clusters continues or not, by which the optimal clustering results can be obtained eventually. Then, the obtained clustering results can be used to calculate the centroid of each cluster, thereby to train the judgment boundary to form a hypersphere. Finally, the hypersphere can be used to detect the product quality. The simulation and experimental results show that, the clustering accuracy is close to 100%, and the accuracy of detection model is superior to 91.83%. Those results are competitive with existing state-of-the-art detection methods in terms of clustering effectiveness and the real-time performance.

A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure.

The worldwide outbreak of the new coronavirus disease (COVID-19) has been declared a pandemic by the World Health Organization (WHO). It has a devastating impact on daily life, public health, and global economy. Due to the highly infectiousness, it is urgent to early screening of suspected cases quickly and accurately. Chest X-ray medical image, as a diagnostic basis for COVID-19, arouses attention from medical engineering. However, due to small lesion difference and lack of training data, the accuracy of detection model is insufficient. In this work, a transfer learning strategy is introduced to hierarchical structure to enhance high-level features of deep convolutional neural networks. The proposed framework consisting of asymmetric pretrained DCNNs with attention networks integrates various information into a wider architecture to learn more discriminative and complementary features. Furthermore, a novel cross-entropy loss function with a penalty term weakens misclassification. Extensive experiments are implemented on the COVID-19 dataset. Compared with the state-of-the-arts, the effectiveness and high performance of the proposed method are demonstrated.

An Integrated Intrusion Detection System by Combining SVM with AdaBoost

In the Internet, computers and network equipments are threatened by malicious intrusion, which seriously affects the security of the network. Intrusion behavior has the characteristics of fast upgrade, strong concealment and randomness, so that traditional methods of intrusion detection system (IDS) are difficult to prevent the attacks effectively. In this paper, an integrated network intrusion detection algorithm by combining support vector machine (SVM) with AdaBoost was presented. The SVM is used to construct base classifiers, and the AdaBoost is used for training these learning modules and generating the final intrusion detection model by iterating to update the weight of samples and detection model, until the number of iterations or the accuracy of detection model achieves target setting. The effectiveness of the proposed IDS is evaluated using DARPA99 datasets. Accuracy, a criterion, is used to evaluate the detection performance of the proposed IDS. Experimental results show that it achieves better performance when compared with two state-of-the-art IDS.