The application of the Internet of Things (IoT) in medical field has brought unprecedented convenience to human beings. However, attackers can use device configuration vulnerabilities to hijack devices, control services, steal medical data, or make devices operate illegally. These restrictions have led to huge security risks for IoT, and have challenged the management of critical infrastructure services. Based on these problems, this paper proposes an anomaly detection system for detecting illegal behavior (DIB) in medical IoT environment. The DIB system can analyze data packets transmitted by medical IoT devices, learn operation rules by itself, and remind management personnel that the device is in an abnormal operation state to ensure the safety of control service. We further propose a model which is based on rough set (RS) theory and fuzzy core vector machine (FCVM) to improve the accuracy of DIB classification anomalies. Experimental results show that the R-FCVM is effective.

The number of money laundering crimes for Ethereum and the amount involved have grown exponentially in recent years. However, previous studies related to anomaly detection for Ethereum usually consider multiple types of financial crimes as a whole, ignoring the apparent differences between money laundering and other malicious activities and lacking a more granular detection targeting money laundering. In this paper, for the first time, we propose an improved graph embedding algorithm specifically for money laundering detection called GTN2vec. By mining Ethereum transaction records, the algorithm comprehensively considers the behavioral patterns of money launderers and structural information of transaction networks and can automatically extract features of money laundering addresses. Specifically, we fuse the gas price and timestamp from the transaction records into a new weight and set appropriate return and exploration parameters to modulate the sampling tendency of random walk to characterize the money laundering nodes. We construct the dataset using real Ethereum data and evaluate the effectiveness of GTN2vec on the dataset by various classifiers such as random forest. The experimental results show that GTN2vec can accurately and effectively extract money laundering account features and significantly outperform other advanced graph embedding methods.

In the intelligent manufacturing process of solar photovoltaic (PV) cells, the automatic defect detection system using the Industrial Internet of Things (IIoT) smart cameras and sensors cooperated in IIoT has become a promising solution. Many works have been devoted to defect detection of PV cells in a data-driven way. However, because of the subjectivity and fuzziness of human annotation, the data contains a high quantity of noise and unpredictable uncertainties, which creates great difficulties in automatic defect detection. To address this problem, we propose a novel architecture named fuzzy convolution, which integrates fuzzy logic and convolution operations at microscopic level. Combining the proposed fuzzy convolution with the regular convolution, we build a network called Hybrid Fuzzy Convolutional Neural Network (HFCNN). Compared with convolutional neural networks (CNNs), HFCNN can address the uncertainties of PV cell data to improve the accuracy with fewer parameters, making it possible to apply our method in smart cameras. Experimental results on a public dataset show the superiority of our proposed method compared with CNNs.