Intrusion detection system (IDS) plays a significant role in preventing network attacks and plays a vital role in the field of national security. At present, the existing intrusion detection methods are generally based on traditional machine learning models, such as random forest and decision tree, but they rely heavily on artificial feature extraction and have relatively low accuracy. To solve the problems of feature extraction and low detection accuracy in intrusion detection, an intrusion detection model SAAE-DNN, based on stacked autoencoder (SAE), attention mechanism and deep neural network (DNN), is proposed. The SAE represents data with a latent layer, and the attention mechanism enables the network to obtain the key features of intrusion detection. The trained SAAE encoder can not only automatically extract features, but also initialize the weights of DNN potential layers to improve the detection accuracy of DNN. We evaluate the performance of SAAE-DNN in binary-classification and multi-classification on an NSL-KDD dataset. The SAAE-DNN model can detect normally and attack symmetrically, with an accuracy of 87.74% and 82.14% (binary-classification and multi-classification), which is higher than that of machine learning methods such as random forest and decision tree. The experimental results show that the model has a better performance than other comparison methods.

Due to the insidious characteristics of network intrusion behaviors, developing an efficient intrusion detection system is still a big challenge, especially in the era of big data where the number of traffic and the dimension of each traffic feature are high. Because of the shortcomings of traditional common machine learning algorithms in network intrusion detection, such as insufficient accuracy, a network intrusion detection system based on LightGBM and autoencoder (AE) is proposed. The LightGBM-AE model proposed in this paper includes three steps: data preprocessing, feature selection, and classification. The LightGBM-AE model adopts the LightGBM algorithm for feature selection, and then uses an autoencoder for training and detection. When a set of data containing network intrusion behaviors are inputted into an autoencoder, there is a large reconstruction error between the original input data and the reconstructed data obtained by the autoencoder, which provides a basis for intrusion detection. According to the reconstruction error, an appropriate threshold is set to distinguish symmetrically between normal behavior and attack behavior. The experiment is carried out on the NSL-KDD dataset and implemented using Pytorch. In addition to autoencoder, variational autoencoder (VAE) and denoising autoencoder (DAE) are also used for intrusion detection and are compared with existing machine learning algorithms such as Decision Tree, Random Forest, KNN, GBDT, and XGBoost. The evaluation is carried out through classification evaluation indexes such as accuracy, precision, recall, F1-score. The experimental results show that the method can efficiently separate the attack behavior from normal behavior according to the reconstruction error. Compared with other methods, the effectiveness and superiority of this method are verified.

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Spectral CT with monochromatic images at 50-53 keV allows significant reduction in iodine load while improving portal vein signal intensity and maintaining image quality.

ObjectiveTo investigate the efficiency of spectral computed tomography (CT) optimal monochromatic images in improving imaging quality of liver vessels.Materials and MethodsThe imaging data of 35 patients with abdominal CT angiography were retrospectively analyzed. Hepatic arteries, portal veins, and hepatic veins were reconstructed with mixed energy (quality check, QC), 70 keV and optimal monochromatic mode. Comparative parameters were analyzed including CT value, image noise (IN), contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and subjective qualitative analysis.ResultsThe optimal monochromatic value for assessment of the common hepatic artery, portal vein, and hepatic vein ranged between 49 keV and 53 keV, with a mean of 51 keV. There were statistically significant differences (p < 0.001) among the optimal monochromatic, 70 keV and QC images with regards to the hepatic vascular CT value, IN, CNR, SNR, and subjective qualitative score. CNR of the common hepatic artery in the optimal monochromatic, 70 keV and QC groups was 24.6 ± 10.9, 18.1 ± 8.3, and 11.6 ± 4.6, respectively (p < 0.001) with subjective scores of 4.7 ± 0.2, 4.0 ± 0.3, and 3.6 ± 0.4, respectively (p < 0.001). CNR of the hepatic portal vein was 6.9 ± 2.7, 4.3 ± 1.9, and 3.0 ± 2.1, respectively (p < 0.001) with subjective scores of 4.5 ± 0.3, 3.9 ± 0.4, and 3.3 ± 0.3, respectively (p < 0.001). CNR of the hepatic vein was 5.7 ± 2.3, 4.2 ± 1.9, and 2.7 ± 1.4, respectively with subjective scores of 4.3 ± 0.3, 3.8 ± 0.4, and 3.2 ± 0.3, respectively (p < 0.001).ConclusionOptimal monochromatic images can lead to improvement in the imaging parameters and optimization of the image quality of the common hepatic artery, hepatic portal vein and hepatic vein compared with conventional mixed kV and with 70 keV datasets.

Using organ dose modulation and applying the ASiR-V algorithm can more effectively reduce the radiation dose in CTPA than in 3D Smart mA modulation, while maintaining image quality.

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The present study investigated the effect of arylsulfonyl indoline-benzamide (ASIB) on neovascular glaucoma in the mice model in vivo. In the mice model of glaucoma, ASIB treatment significantly (P < 0.05) increased PDGF-B-positive cell count in the corneal tissues. ASIB treatment at 5, 10, 15 and 20 mg/kg doses raised the level of PDGF-B mRNA in the mice cornea by 2.3-, 3.8-, 5.4-and 5.5-fold, respectively. Pre-treatment of the glaucoma mice with ASIB leads to inhibition of TNF-α and IL-6 production. In the glaucoma mice, treatment with ASIB leads to a marked decrease in the level of NOD2 mRNA and protein. ASIB treatment caused a significant decrease in the glaucoma-induced up-regulation of NF-κB p65 activation. The phosphorylation of NF-κB p65 was almost completely inhibited in the glaucoma mice on treatment with 15 mg/kg dose of ASIB. ASIB exhibited inhibitory effect on glaucoma-induced inflammatory cytokine and oxidative factor damage in the mice. It caused up-regulation of PDGF expression and down-regulated NF-κB activation. Therefore, ASIB can be of therapeutic significance for neovascular glaucoma treatment. However, more studies need to be performed to fully understand the molecular mechanism of ASIB in glaucoma treatment.