With the growing demand for digitalization, multimedia data transmission through wireless networks has become more prominent. These multimedia data include text, images, audio, and video. Therefore, a secure method is needed to modify them so that such images, even if intercepted, will not be interpreted accurately. Such encryption is proposed with a two-layer image encryption scheme involving bit-level encryption in the time-frequency domain. The top layer consists of a bit of plane slicing the image, and each plane is then scrambled using a chaotic map and encrypted with a key generated from the same chaotic map. Next, image segmentation, followed by a Lifting Wavelet Transform, is used to scramble and encrypt each segment’s low-frequency components. Then, a chaotic hybrid map is used to scramble and encrypt the final layer. Multiple analyses were performed on the algorithm, and this proposed work achieved a maximum entropy of 7.99 and near zero correlation, evidencing the resistance towards statistical attacks. Further, the keyspace of the cryptosystem is greater than 2¹²⁸, which can effectively resist a brute force attack. In addition, this algorithm requires only 2.1743 s to perform the encryption of a 256 × 256 sized 8-bit image on a host system with a Windows 10 operating system of 64-bit Intel(R) Core(TM) i5-7200U CPU at 2.5 GHz with 8 GB RAM.

Cloud services offer doctors and data scientists access to medical data from multiple locations using different devices (laptops, desktops, tablets, smartphones, etc.). Therefore, cyber threats to medical data at rest, in transit and when used by applications need to be pinpointed and prevented preemptively through a host of proven cryptographical solutions. The presented work integrates adaptive key generation, neural-based confusion and non-XOR, namely DNA diffusion, which offers a more extensive and unique key, adaptive confusion and unpredictable diffusion algorithm. Only authenticated users can store this encrypted image in cloud storage. The proposed security framework uses logistics, tent maps and adaptive key generation modules. The adaptive key is generated using a multilayer and nonlinear neural network from every input plain image. The Hopfield neural network (HNN) is a recurrent temporal network that updates learning with every plain image. We have taken Amazon Web Services (AWS) and Simple Storage Service (S3) to store encrypted images. Using benchmark evolution metrics, the ability of image encryption is validated against brute force and statistical attacks, and encryption quality analysis is also made. Thus, it is proved that the proposed scheme is well suited for hosting cloud storage for secure images.

Logistics management is very important aspect in real time applications. This paper addresses the problems in cargo loaded vehicles such as the delayed delivery of goods, overloading on vehicles, and identification of vehicle location and misplacement of goods. The proposed system uses RFID tag to identify the goods to avoid misplacement and Weight sensor is used to avoid the overloads of goods in vehicle. The GPS and GSM are used to track the vehicle and also send the details of goods to source periodically

Security in e-healthcare applications such as Telemedicine is crucial in safeguarding patients’ sensitive data during transmission. The proposed system measures the patient’s health parameters, such as body temperature and pulse rate, using LM35 and pulse sensors, respectively. The sensor data and the patient’s medical image are encrypted in the Raspberry Pi 3 B + processor using Python’s proposed text and medical image encryption scheme. The encrypted data is transmitted via the Thing Speak cloud and received by another Raspberry Pi at the receiver to decrypt the cipher data. The flask webserver can view the decrypted data by the doctor at the other end. This IoT implementation of secure Electronic Health Record (EHR) transmission employs text and medical image encryption schemes using a Combined Chaotic System (CCS). The CCS generates the chaotic key sequences to shuffle the medical image row-wise and column-wise. Then, selective shuffling between the cut-off points breaks the statistical relationship between the neighbouring pixels. Finally, the intra and inter-pixel diffusion is carried out using bit permutation and bit-wise XOR operation to create a highly random cipher image. The initial seed for inter-pixel diffusion is obtained from the hash of intra-pixel diffused images to resist chosen plain text and cipher text attacks. The efficiency of the developed medical image encryption algorithm is tested against various attack analyses. The results and the security analyses validate the effectiveness of the proposed scheme.