The combination of blockchain and Internet of Things technology has made significant progress in smart agriculture, which provides substantial support for data sharing and data privacy protection. Nevertheless, achieving efficient interactivity and privacy protection of agricultural data remains a crucial issues. To address the above problems, we propose a blockchain-assisted federated learning-driven support vector machine (BAFL-SVM) framework to realize efficient data sharing and privacy protection. The BAFL-SVM is composed of the FedSVM-RiceCare module and the FedPrivChain module. Specifically, in FedSVM-RiceCare, we utilize federated learning and SVM to train the model, improving the accuracy of the experiment. Then, in FedPrivChain, we adopt homomorphic encryption and a secret-sharing scheme to encrypt the local model parameters and upload them. Finally, we conduct a large number of experiments on a real-world dataset of rice pests and diseases, and the experimental results show that our framework not only guarantees the secure sharing of data but also achieves a higher recognition accuracy compared with other schemes.

In the Internet of Things (IoT), a large number of devices are connected using a variety of communication technologies to ensure that they can communicate both physically and over the network. However, devices face the challenge of a single point of failure, a malicious user may forge device identity to gain access and jeopardize system security. In addition, devices collect and transmit sensitive data, and the data can be accessed or stolen by unauthorized user, leading to privacy breaches, which posed a significant risk to both the confidentiality of user information and the protection of device integrity. Therefore, in order to solve the above problems and realize the secure transmission of data, this paper proposed EBIAS, a secure and efficient blockchain-based identity authentication scheme designed for IoT devices. First, EBIAS combined the Elliptic Curve Cryptography (ECC) algorithm and the SHA-256 algorithm to achieve encrypted communication of the sensitive data. Second, EBIAS integrated blockchain to tackle the single point of failure and ensure the integrity of the sensitive data. Finally, we performed security analysis and conducted sufficient experiment. The analysis and experimental results demonstrate that EBIAS has certain improvements on security and performance compared with the previous schemes, which further proves the feasibility and effectiveness of EBIAS.

Federated Learning (FL) is a privacy-preserving machine learning paradigm that enables clients such as Internet of Things (IoT) devices, and smartphones, to train a high-performance global model jointly. However, in real-world FL deployments, carefully human-annotated labels are expensive and time-consuming. So the presence of incorrect labels (noisy labels) in the local training data of the clients is inevitable, which will cause the performance degradation of the global model. To tackle this problem, we propose a simple but effective method Local Self-Guiding (LSG) to let clients guide themselves during training in the presence of noisy labels. Specifically, LSG keeps the model from memorizing noisy labels by enhancing the confidence of model predictions. Meanwhile, it utilizes the knowledge from local historical models which haven't fit noisy patterns to extract potential ground truth labels of samples. To keep the knowledge without storing models, LSG records the exponential moving average (EMA) of model output logits at different local training epochs as self-ensemble logits on clients' devices, which will lead to negligible computation and storage overhead. Then logit-based knowledge distillation is conducted to guide the local training. Experiments on MNIST, Fashion-MNIST, CIFAR-10, ImageNet-100 with multiple noise levels, and an unbalanced noisy dataset, Clothing1M, demonstrate the resistance of LSG to noisy labels. The code of LSG is available at https://github.com/DaokuanBai/LSG-Main

In the age of big data, data has already become a "high-value commodity" with clear price. Privacy leaks can lead to personal information security violations during training in federated learning, especially in the medical industry. The data of the medical industry is characterized by large amount of data and high demand for privacy. For this reason, we designed a Decentralized Reinforced Anonymous Federated Learning Based on Blockchain (DRA-FLchain) with high privacy protection and strong anonymity. In view of the large amount of data in the medical industry, DRA-FLchain uses blockchain technology to enable a large number of clients to participate in model training, and also uses cut through technology to reduce the cost of storage space. DRA-FLchain uses the blockchain and ring signature to ensure the anonymity of the client’s identity, and also uses homomorphic encryption and mask to protect the security of the model. For anonymous Federated Learning (FL), we set up a novel reward mechanism based on game theory Reward mechanism based on ring signature (RMBRS), which can distribute rewards fairly in the anonymous FL architecture. We compared the accuracy and operation efficiency of FL, Federated Learning Based on Blockchain (FLchain) and DRA-FLchain through experiments. The experimental results show that DRA-FLchain is an effective anonymous and secure architecture. Finally, we proved that DRA-FLchain can still protect the privacy of clients well in extreme cases through case study.

The exponential growth and sophistication of Internet of Things (IoT) malware behavior have resulted in new detection technologies capable of defending IoT devices against some threats. However, their success has stimulated the interest of attackers attempting to circumvent current IoT malware detectors. Among detection technologies, the detectors trained based on Uniform Resource Locator (URL) requests have become popular. To draw attention to the safety of the detectors, we propose a grey-box method to attack detectors based on URL requests without breaking malicious functions of URL requests. The key idea is to add perturbations to the tail of URLs. Specifically, this method is based on a Genetic Algorithm (GA) to find suitable perturbations and optimizes the process of adversarial attacks through a dynamic number of evolution directions and a maximum generation limit. The effectiveness of our adversarial attack is demonstrated by experimental results based on a widely used public dataset CSIC2010 and several representative detectors. As far as we know, this is the first time an adversarial attack against IoT detectors based on URL requests has been done. The method has an attack success rate of more than 92 %. Furthermore, experiment results show that the method can reduce query numbers while maintaining the attack success rate.

This paper presents the first detailed description of the innovative measurement set up of an indoor tropospheric microwave radiometer (TROWARA) that avoids water films on radome. We discuss the performance of a commercial outdoor microwave radiometer (HATPRO) for measuring tropospheric water parameters in Bern, Switzerland. The HATPRO is less than 20 meters from the TROWARA and has different instrument characteristics. Brightness temperatures measured by HATPRO are analyzed by comparing them with coincident measurements from TROWARA and radiative transfer simulations based on the ECMWF operational analysis data (denoted as RTSE). To find the source of brightness temperature bias, a gradient boosting decision tree is used to analyze the sensitivity of eight feature factors to bias. Data processing routines of the two radiometers use different algorithms to retrieve integrated water vapour (IWV) and integrated cloud liquid water (ILW), whereas the same physical algorithms based on the radiative transfer equation are applied to obtain the opacity and rain rate. Using 62 days of data with varied weather conditions, it was found that TROWARA brightness temperatures are in good agreement with RTSE. HATPRO brightness temperatures are significantly overestimated by about 5 K at 22 GHz, compared to TROWARA and RTSE. HATPRO brightness temperatures at 31 GHz agree well with TROWARA and RTSE (within about +/-1 K). The overestimated brightness temperatures in the K-band and the HATPRO retrieval algorithm lead to an overestimation of IWV and ILW by HATPRO. The opacities at 31 GHz match very well for TROWARA and HATPRO during no rain with a verified R2 of 0.96. However, liquid water floating or remaining water films on the radome of the outdoor HATPRO radiometer induce an overestimation of the rain rate. The physical reason for the overestimated 22 GHz brightness temperatures of the HATPRO is mainly the result of the combined effect of instrument calibration, the surrounding environment of the instrument, and the sun elevation angle. This can be a problem with the Generation 2 HATPRO radiometer and this problem was resolved in the Generation 5 HATPRO radiometer.

The utilization of UAV may suffer severe security problems due to the inherent characteristics of wireless air-to-ground (A2G) channels. To this end, researchers have drawn much attention on utilizing physical layer security (PLS) techniques to maintain secrecy data transmission in UAV enabled networks. Different from previous works, we jointly optimize user scheduling strategy, signal transmission power and 3D flying trajectory of the UAV to maximize the minimum system secrecy rate by considering UAV position and an eavesdropper with partial location information simultaneously. Because the formulated problem is intractable and non-convex, we in this paper develop an iteration approach to solve the problem based on the successive convex approximation (SCA) method. Finally, experimental results are further derived to validate the performance gains of our scheme.

The hyperbolic cosine function with high-order errors can be utilized to improve the accuracy of adaptive filters. However, when initial weight errors are large, the hyperbolic cosine-based adaptive filter (HCAF) may be unstable. In this paper, a novel normalization based on the logarithmic hyperbolic cosine function is proposed to achieve the stabilization for the case of large initial weight errors, which generates a logarithmic HCAF (LHCAF). Actually, the cost function of LHCAF is the logarithmic hyperbolic cosine function that is robust to large errors and smooth to small errors. The transient and steady-state analyses of LHCAF in terms of the mean-square deviation (MSD) are performed for a stationary white input with an even probability density function in a stationary zero-mean white noise. The convergence and stability of LHCAF can be therefore guaranteed as long as the filtering parameters satisfy certain conditions. The theoretical results based on the MSD are supported by the simulations. In addition, a variable scaling factor and step-size LHCAF (VSS-LHCAF) is proposed to improve the filtering accuracy of LHCAF further. The proposed LHCAF and VSS-LHCAF are superior to HCAF and other robust adaptive filters in terms of filtering accuracy and stability.

In this paper, the maximum correntropy (MC) criterion is used as the cost function in the online sequential extreme learning machine (OS-ELM) algorithm and constraint OS-ELM (COS-ELM) algorithm, generating the proposed OS-ELM based on maximum correntropy (OS-ELM-MC) and COS-ELM based on maximum correntropy (COS-ELM-MC). In comparison with OS-ELM and COS-ELM, the proposed OS-ELM-MC and COS-ELM-MC present superior performance in non-Gaussian noise environments and almost the same performance in Gaussian noise environments. As an important parameter, the hidden node number is also discussed by simulations in this paper. Simulations on the examples of Mackey-Glass (MG) chaotic time series prediction and nonlinear regression validate the efficiency of the proposed OS-ELM-MC and COS-ELM-MC.