In this paper, a novel double-image compression-encryption algorithm is proposed by combining Rossler hyperchaos and compressive sensing. In the proposed scheme, the sparse matrixes obtained by performing two-dimensional discrete wavelet transform on two plain images are first scrambled and compressed by index confusion and compressive sensing. Next, the compressed matrixes are linearly quantized, and then variable step length Josephus confusion and bitwise exclusive-OR operation are performed on them. The final noise-like cipher image is generated by hiding half of the encrypted image into the alpha channel of the other half of encrypted image. In order to realize “One cipher image corresponds to one key”, a novel key generation mechanism is designed. Finally, the simulation analyses indicate that the proposed double-image encryption scheme has high transmission efficiency. Meanwhile, the average local information entropy of the cipher image is about 0.902 and the decryption quality is as high as 30 dB.

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.