Image encryption is an effective way to protect image data. However, existing image encryption algorithms are still unable to strike a good balance between security and efficiency. To overcome the shortcomings of these algorithms, an image encryption algorithm based on plane-level image filtering and discrete logarithmic transformation (IEA-IF-DLT) is proposed. By utilizing the hash value more rationally, our proposed IEA-IF-DLT avoids the overhead caused by repeated generations of chaotic sequences and further improves the encryption efficiency through plane-level and three-dimensional (3D) encryption operations. Aiming at the problem that common modular addition and XOR operations are subject to differential attacks, IEA-IF-DLT additionally includes discrete logarithmic transformation to boost security. In IEA-IF-DLT, the plain image is first transformed into a 3D image, and then three rounds of plane-level permutation, plane-level pixel filtering, and 3D chaotic image superposition are performed. Next, after a discrete logarithmic transformation, a random pixel swapping is conducted to obtain the cipher image. To demonstrate the superiority of IEA-IF-DLT, we compared it with some state-of-the-art algorithms. The test and analysis results show that IEA-IF-DLT not only has better security performance, but also exhibits significant efficiency advantages.

With the development of the Internet of Things (IoT), the traffic composition in the network has changed greatly. The traffic analysis is the basis for the further tasks in IoT network, such as intrusion detection, abnormal behavior analysis and attack detection. This paper adopts support vector regression (SVR) to predict traffic data in the wireless sensor networks and IoT network. First, the traffic data is represented as the time series form. Then, the sequence of traffic data is processed by logarithmic function to eliminate the fluctuation of the traffic data. Lastly, the processed traffic sequence data is used to learn a SVR model. The learnt SVR model is used to predict the traffic in the future. The experiments on telemedicine, smart agriculture, vending and automatic driving show that the mean square error of proposed traffic prediction method can achieve less than 0.150.