Botnets pose a significant threat to Internet of Things (IoT) environments due to the limited computational resources of IoT devices, making traditional detection methods difficult to implement. These constraints not only hinder effective real-time detection but also leave networks vulnerable to large-scale DDoS and botnet attacks, posing a critical threat to modern connected systems. Aiming to design a lightweight botnet detection method for IoT networks, we propose a novel cloud–edge–node framework that decouples the computationally intensive training phase from the real-time detection phase. In our framework, the node layer comprises resource-constrained IoT devices that collect raw network data, the edge layer hosts lightweight detection modules for rapid analysis, and the cloud layer performs heavy-duty model training and incremental updates. Additionally, we propose a two-step feature selection process, in which the first step uses the cumulative density function (CDF) to rank features based on their distribution characteristics, and the second step applies Gini importance to further refine the feature set. This process effectively reduces computational overhead while retaining highly discriminative features for lightweight botnet detection. Experimental results on a public IoT dataset reveal that our method reduces detection time by up to 52% and energy consumption by up to 71% while maintaining high detection accuracy. These significant improvements not only validate the efficiency of our approach but also underline its potential to transform IoT security by enabling scalable, low-cost, and real-time botnet detection in diverse practical scenarios.

The Internet of Things (IoT) is increasingly providing industrial production objects to connect with the physical world and has been widely used in various fields. Although it has brought great industrial convenience, there are also potential security threats due to the vulnerabilities and malicious nodes in IoT. To correctly identify the traffic of malicious nodes in IoT and reduce the damage caused by malicious attacks on IoT devices, this paper proposes an autoencoder-based IoT malicious node detection method. The contributions of this paper are as follows: firstly, the high complexity multi-featured traffic data are processed and dimensionally reduced through the autoencoder to obtain the low-dimensional feature data. Then, the Bayesian Gaussian mixture model is adopted to cluster the data in a low-dimensional space to detect anomalies. Furthermore, the method of variational inference is used to estimate the parameters in the Bayesian Gaussian mixture model. To evaluate our model’s effectiveness, we used a public dataset for our experiments. As a result, in the experiment, the proposed method achieves a high accuracy rate of 99% distinguishing normal and abnormal traffic with three-dimension data reduced by the autoencoder, and it establishes our model’s better detection performance compared with previous K-means and Gaussian Mixture Model (GMM) solutions.

Background: The MRI imaging traits of pediatric posterior cranial fossa neoplasms overlap highly, leading to difficulties in preoperative diagnosis. Their treatment strategies differ significantly, and traditional deep learning models have limitations in multi - sequence MRI fusion and clinical interpretability, so new solutions are urgently needed. Objectives: This study aims to develop a 2.5D multi - sequence MRI deep learning framework (ResSwinT) that integrates Residual Network and Swin Transformer, to achieve automatic classification of three main Pediatric posterior fossa tumors—Pilocytic astrocytoma (PA), Medulloblastoma (MB), and Ependymoma (EP), and enhance the interpretability of the model through the SHAP method, so as to provide a more reliable auxiliary decision-making basis for clinical practice. Methods: This study retrospectively collected 309 pediatric patients confirmed by pathology, including 109 PA, 130 MB and 70 EP. The MRI data of these patients included five sequences: T1WI, T1C, T2WI,FLAIR, and ADC. After preprocessing steps such as N4 bias field correction, resampling, sequence registration, and intensity normalization, samples were constructed using a 2.5D image construction strategy, and the ResSwinT model is designed. Its performance was compared with seven deep learning models such as Residual Network 18 and VGG16, and SHAP analysis was used to analyze trait contributions. Results: The proposed ResSwinT model outperforms existing commonly used deep learning models in all classification tasks, particularly showing outstanding performance in terms of area under the curve(AUC) and overall accuracy(ACC). For the PA vs Non-PA task: ACC 89.5%, AUC 0.975; for the MB vs Non-MB task: ACC 93.7%, AUC 0.978; for the EP vs Non-EP task: Acc 87.5%, AUC 0.937. SHapley Additive exPlanations(SHAP) analysis shows that the model pays high attention to the gross tumor volume and its surrounding structures, and its decision-making basis is highly consistent with key imaging biomarkers, verifying the interpretability and clinical relevance of the model. Conclusions: ResSwinT achieves high-precision classification of pediatric posterior fossa tumor through 2.5D multi-sequence fusion and cross-attention mechanism. SHAP attribution analysis reveals the biological basis of the model’s decision-making, providing clinicians with an interpretable AI-assisted diagnostic tool, and is expected to optimize individualized treatment strategies.

Due to the absence of tailored designs that address challenges such as variations in scale, disparities in illumination, and instances of occlusion, the implementation of current person re-identification techniques remains challenging in practical applications. An Efficient Multi-Branch Attention Network over OSNet (EMANet) is proposed. The structure is composed of three parts, the global branch, relational branch, and global contrastive pooling branch, and corresponding features are obtained from different branches. With the attention mechanism, which focuses on important features, DAS attention evaluates the significance of learned features, awarding higher ratings to those that are deemed crucial and lower ratings to those that are considered distracting. This approach leads to an enhancement in identification accuracy by emphasizing important features while discounting the influence of distracting ones. Identity loss and adaptive sparse pairwise loss are used to efficiently facilitate the information interaction. In experiments on the Market-1501 mainstream dataset, EMANet exhibited high identification accuracies of 96.1% and 89.8% for Rank-1 and mAP, respectively. The results indicate the superiority and effectiveness of the proposed model.

This work focused on the application value of magnetic resonance imaging (MRI) image segmentation algorithm based on fully convolutional DenseNet neural network (FCDNN) in glioma diagnosis. In this work, based on the fully convolutional DenseNet algorithm, a new MRI image automatic semantic segmentation method cerebral gliomas semantic segmentation network (CGSSNet) was established and was applied to glioma MRI image segmentation by using the BraTS public dataset as research data. Under the same conditions, compare the differences of dice similarity coefficient (DSC), sensitivity, and Hausdroff distance (HD) between this algorithm and other algorithms in MRI image processing. The results showed that the CGSSNet network segmentation algorithm significantly improved the segmentation accuracy of glioma MRI images. In addition, its DSC, sensitivity, and HD values for glioma MRI images were 0.937, 0.811, and 1.201, respectively. Under different iteration times, the DSC, sensitivity, and HD values of the CGSSNet network segmentation algorithm are significantly better than other algorithms. It showed that the CGSSNet model based on the DenseNet can improve the segmentation accuracy of glioma MRI images, and has potential application value in clinical practice.