Iot Based Alzheimer’s Disease Diagnosis Model for Providing Security Using Light Weight Hybrid Cryptography

Security in the Internet of things (IoT) is a broad yet active research area that focuses on securing the sensitive data being circulated in the network. The data involved in the IoT network comes from various organizations, hospitals, etc., that require a higher range of security from attacks and breaches. The common solution for security attacks is using traditional cryptographic algorithms that can protect the content through encryption and decryption operations. The existing solutions are suffering from major drawbacks, including computational complexities, time and space complexities, slower encryption, etc. Therefore, to overcome such drawbacks, this paper introduces an efficient light weight cryptographic mechanism to secure the images of Alzheimer’s disease (AD) being transmitted in the network. The mechanism involves major stages such as edge detection, key generation, encryption, and decryption. In the case of edge detection, the edge maps are detected using the Prewitt edge detection technique. Then the hybrid elliptic curve cryptography (HECC) algorithm is proposed to encrypt and secure the images being transmitted in the network. For encryption, the HECC algorithm combines blowfish with the elliptic curve algorithm to attain a higher range of security. Another significant advantage of the proposed method is selecting the ideal private key, which is achieved using the enhanced seagull optimization (ESO) algorithm. The proposed work has been tested in the Python tool, and the performance is evaluated with the Alzheimer’s dataset, and the outcomes proved its efficacy over the compared methods

SECURE VIDEO CODED SYSTEM MODEL

In this paper, overall system model, shown in Figure (1), of video compression-encryption-transmitter/decompression-dencryption-receiver was designed and implemented. The modified video codec system has used and in addition to compression/decompression, theencryption/decryption video signal by using chaotic neural network (CNN) algorithm was done. Both of quantized vector data and motion vector data have been encrypted by CNN. The compressed and encrypted video data stream has been sent to receiver by using orthogonal frequency division multiplexing (OFDM) modulation technique. The system model was designed according to video signal sample size of 176 × 144 (QCIFstandard format) with rate of 30 frames per second. Overall system model integrates and operates successfully with acceptable performance results

A review and open issues of multifarious image steganography techniques in spatial domain

Nowadays, information hiding is becoming a helpful technique and fetch more attention due fast growth of using internet, it is applied for sending secret information by using different techniques. Steganography is one of major important technique in information hiding. Steganography is science of concealing the secure information within a carrier object to provide the secure communication though the internet, so that no one can recognize and detect it’s except the sender & receiver. In steganography, many various carrier formats can be used such as an image, video, protocol, audio. The digital image is most popular used as a carrier file due its frequency on internet. There are many techniques variable for image steganography, each has own strong and weak points. In this study, we conducted a review of image steganography in spatial domain to explore the term image steganography by reviewing, collecting, synthesizing and analyze the challenges of different studies which related to this area published from 2014 to 2017. The aims of this review is provides an overview of image steganography and comparison between approved studies are discussed according to the pixel selection, payload capacity and embedding algorithm to open important research issues in the future works and obtain a robust method

Authenticated public key elliptic curve based on deep convolutional neural network for cybersecurity image encryption application

The demand for cybersecurity is growing to safeguard information flow and enhance data privacy. This essay suggests a novel authenticated public key elliptic curve based on a deep convolutional neural network (APK-EC-DCNN) for cybersecurity image encryption application. The public key elliptic curve discrete logarithmic problem (EC-DLP) is used for elliptic curve Diffie–Hellman key exchange (EC-DHKE) in order to generate a shared session key, which is used as the chaotic system’s beginning conditions and control parameters. In addition, the authenticity and confidentiality can be archived based on ECC to share the (Formula presented.) parameters between two parties by using the EC-DHKE algorithm. Moreover, the 3D Quantum Chaotic Logistic Map (3D QCLM) has an extremely chaotic behavior of the bifurcation diagram and high Lyapunov exponent, which can be used in high-level security. In addition, in order to achieve the authentication property, the secure hash function uses the output sequence of the DCNN and the output sequence of the 3D QCLM in the proposed authenticated expansion diffusion matrix (AEDM). Finally, partial frequency domain encryption (PFDE) technique is achieved by using the discrete wavelet transform in order to satisfy the robustness and fast encryption process. Simulation results and security analysis demonstrate that the proposed encryption algorithm achieved the performance of the state-of-the-art techniques in terms of quality, security, and robustness against noise- and signal-processing attacks

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N $\times$ M $\times$ 3 to P $\times$ Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N $\times$ M $\times$ 3. The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. encryption

Distinguishing Lightweight Block Ciphers in Encrypted Images

Modern day lightweight block ciphers provide powerful encryption methods for securing IoT communication data. Tiny digital devices exchange private data which the individual users might not be willing to get disclosed. On the other hand, the adversaries try their level best to capture this private data. The first step towards this is to identify the encryption scheme. This work is an effort to construct a distinguisher to identify the cipher used in encrypting the traffic data. We try to establish a deep learning based method to identify the encryption scheme used from a set of three lightweight block ciphers viz. LBlock, PRESENT and SPECK. We make use of images from MNIST and fashion MNIST data sets for establishing the cryptographic distinguisher. Our results show that the overall classification accuracy depends firstly on the type of key used in encryption and secondly on how frequently the pixel values change in original input image

A Simple and Robust Gray Image Encryption Scheme Using Chaotic Logistic Map and Artificial Neural Network

A robust gray image encryption scheme using chaotic logistic map and artificial neural network (ANN) is introduced. In the proposed method, an external secret key is used to derive the initial conditions for the logistic chaotic maps which are employed to generate weights and biases matrices of the multilayer perceptron (MLP). During the learning process with the backpropagation algorithm, ANN determines the weight matrix of the connections. The plain image is divided into four subimages which are used for the first diffusion stage. The subimages obtained previously are divided into the square subimage blocks. In the next stage, different initial conditions are employed to generate a key stream which will be used for permutation and diffusion of the subimage blocks. Some security analyses such as entropy analysis, statistical analysis, and key sensitivity analysis are given to demonstrate the key space of the proposed algorithm which is large enough to make brute force attacks infeasible. Computing validation using experimental data with several gray images has been carried out with detailed numerical analysis, in order to validate the high security of the proposed encryption scheme