In this paper, a noise-resistant image encryption scheme is proposed. We have used a cubic-logistic map, Discrete Wavelet Transform (DWT), and bit-plane extraction method to encrypt the medical images at the bit-level rather than pixel-level. The proposed work is divided into three sections; In the first and the last section, the image is encrypted in the spatial domain. While the middle section of the proposed algorithm is devoted to the frequency domain encryption in which DWT is incorporated. As the frequency domain encryption section is a sandwich between the two spatial domain encryption sections, we called it a ”sandwich encryption.” The proposed algorithm is lossless because it can decrypt the exact pixel values of an image. Along with this, we have also gauge the proposed scheme's performance using statistical analysis such as entropy, correlation, and contrast. The entropy values of the cipher images generated from the proposed encryption scheme are more remarkable than 7.99, while correlation values are very close to zero. Furthermore, the number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed encryption scheme is higher than 99.4% and 33, respectively. We have also tested the proposed algorithm by performing attacks such as cropping and noise attacks on enciphered images, and we found that the proposed algorithm can decrypt the plaintext image with little loss of information, but the content of the original image is visible.

The Internet of Things (IoT) is enhancing our lives in a variety of structures, which consists of smarter cities, agribusiness, and e‐healthcare, among others. Even though the Internet of Things has many features with the consumer Internet of Things, the open nature of smart devices and their worldwide connection make IoT networks vulnerable to a variety of assaults. Several approaches focused on attack detection in Internet of Things devices, which has the longest calculation times and the lowest accuracy issues. It is proposed in this paper that an attack detection framework for Internet of Things devices, based on the DWU‐ODBN method, be developed to alleviate the existing problems. At the end of the process, the proposed method is used to identify the source of the assault. It comprises steps such as preprocessing, feature extraction, feature selection, and classification to identify the source of the attack. A random oversampler is used to preprocess the input data by dealing with NaN values, categorical features, missing values, and unbalanced datasets before being used to deal with the imbalanced dataset. When the data has been preprocessed, it is then sent to the MAD Median‐KS test method, which is used to extract features from the dataset. To categorize the data into attack and nonattack categories, the features are classified using the dual weight updation‐based optimal deep belief network (DWU‐ODBN) classification technique, which is explained in more detail below. According to the results of the experimental assessment, the proposed approach outperforms existing methods in terms of detecting intrusions and assaults. The proposed work achieves 77 seconds to achieve the attack detection with an accuracy rate of 98.1%.

In this article, we present an encryption technique that can encrypt any digital data. The proposed scheme basically depends on a substitution-permutation network. Two separate bijective maps are used in the proposed algorithm. Firstly, the substitution boxes are used to perform the substitution process. This technique uses elliptic curves (ECs) to create several substitution boxes with good cryptographic properties. The generated substitution boxes are utilized to replace the arrange data that produces the most uncertainty in the plain image data. Further, we used the permutation process to generate strong randomness in the proposed technique. For the evaluation of the stability of the proposed algorithm; nonlinearity, linear approximation probability, bit independence criterion, strict avalanche criterion and differential approximation probability were performed on the substitution boxes. Differential attacks (Number of Changing Pixel Rate (NPCR), Uniform Average Change Intensity (UACI)) and statistical tests (Entropy, Correlation) were performed on the encrypted images to check their resistance against different attacks. The execution and image analysis reveals that the proposed scheme attains good encryption results while requiring minimal computing ability and has efficient potential in real-time image encryption applications.