Most existing secure biometric authentication schemes are server-centric, and users must fully trust the server to store, process, and manage their biometric data. As a result, users’ biometric data could be leaked by outside attackers or the service provider itself. This paper first constructs the EDZKP protocol based on the inner product, which proves whether the secret value is the Euclidean distance of the secret vectors. Then, combined with the Cuproof protocol, we propose a novel user-centric biometric authentication scheme called BAZKP. In this scheme, all the biometric data remain encrypted during authentication phase, so the server will never see them directly. Meanwhile, the server can determine whether the Euclidean distance of two secret vectors is within a pre-defined threshold by calculation. Security analysis shows BAZKP satisfies completeness, soundness, and zero-knowledge. Based on BAZKP, we propose a privacy-preserving biometric authentication system, and its evaluation demonstrates that it provides reliable and secure authentication.

Since the services on the Internet are becoming increasingly abundant, all walks of life are inextricably linked with the Internet. Simultaneously, the Internet’s WEB attacks have never stopped. Relative to other common WEB attacks, WEB DDoS (distributed denial of service) will cause serious damage to the availability of the target network or system resources in a short period of time. At present, most researches are centered around machine learning-related DDoS attack detection algorithms. According to previous studies, unsupervised methods generally have a high false positive rate, while supervisory methods cannot handle large amount of network traffic data, and the performance is often limited by noise and irrelevant data. Therefore, this paper proposes a semisupervised learning detection model combining spectral clustering and random forest to detect the DDoS attack of the WEB application layer and compares it with other existing detection schemes to verify the semisupervised learning model proposed in this paper. While ensuring a low false positive rate, there is a certain improvement in the detection rate, which is more suitable for the WEB application layer DDoS attack detection.

It’s a challenging task to recognize facial expression in video sequences due to the gap between the hand-crafted features and the subjective emotions. To bridge the gap, this paper proposes a novel method of video-based facial expression recognition using deep temporal–spatial networks. The proposed method firstly employs multimodal deep convolutional neural networks (CNN), including the spatial CNN network and the temporal CNN network, to extract high-level spatial and temporal features in video sequences, respectively. The temporal–spatial CNN networks are fine-tuned on target video facial expression data from a pre-trained CNN model. Specially, the spatial network is used to learn deep spatial features from the static expression images in a video. Likewise, the temporal network is adopted to learn deep temporal features from the produced optical flow images between multiple frames in a video. Then the extracted spatial and temporal features are combined in a fusion network to conduct video-based facial expression classification tasks. Extensive experiments on two public video-based facial expression datasets, i.e. the BAUM-1s and RML database, demonstrate the promising performance of the proposed method, outperforming the-state-of-the-arts.

Software-defined networking (SDN) improves network flexibility by separating the control plane and data plane, but centralized control architecture also increases the risk of distributed denial of service (DDoS) attacks. Traditional detection methods often face performance bottlenecks when dealing with large-scale data. Deep learning, with its powerful automatic feature extraction capabilities, can significantly improve detection accuracy. However, single models typically focus on a specific dimension of network traffic features, neglecting the comprehensive processing of spatial and temporal features. In addition, most existing deep learning methods rely on centralized training, leading to issues such as data transmission delays and privacy breaches. To address these issues, this paper proposes a hybrid model named 1DCNN-TransBiLSTM. The model consists of a spatial feature extraction module based on a 1DCNN and a temporal feature extraction module based on Transformer and BiLSTM. By processing spatial and temporal features in parallel, the model effectively enhances the ability to identify complex attack patterns. Additionally, federated learning is employed to enable multiple SDN controllers to collaborate in training without sharing raw data, and multi-key homomorphic encryption is used to protect the privacy of model gradients. The experimental results show that the model achieves an accuracy of 99.925% and an F1 score of 99.962% on the CICDDoS2019 dataset, outperforming existing machine learning and deep learning methods, demonstrating outstanding performance in DDoS detection in SDN environments.

In this work, an improved Apriori algorithm is proposed. The main goal is to improve the processing efficiency of the algorithm, and the idea and process of the Apriori algorithm are optimized. The proposed method is compared with the classical association rule algorithm to verify its effectiveness. Traditional Chinese medicine plays a certain role in the prevention and treatment of COVID-19. In order to deeply mine the association rules between Chinese herbal medicines for the prevention and treatment of COVID-19, this improved Apriori algorithm is applied from the retrieved published scientific literature and the guidelines for the prevention and treatment of COVID-19 published all over China. Based on the representation of traditional Chinese medicine data in binary form, the potential core traditional Chinese medicine combinations in the treatment of COVID-19 are identified. The results of association rules of Chinese herbal medicine data obtained from the real database provide an important reference for the analysis of COVID-19 combined treatment of Chinese herbal medicine.

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.