Privacy-preserving of medical information (such as medical records and images) is an essential right for patients to ensure security against undesired access parties. This right is typically protected by law through firm regulations set by healthcare authorities. However, sensitive-private data usually requires the application of further security and privacy mechanisms such as encipherment (encryption) techniques. ’Medical images’ is one such example of highly demanding security and privacy standards. This is due to the quality and nature of the information carried among these images, which are usually sensitive-private information with few features and tonal variety. Hence, several state-of-the-art encryption mechanisms for medical images have been proposed and developed; however, only a few were efficient and promising. This paper presents a hybrid crypto-algorithm, MID-Crypt, to secure the medical image communicated between medical laboratories and doctors’ accounts. MID-Crypt is designed to efficiently hide medical image features and provide high-security standards. Specifically, MID-Crypt uses a mix of Elliptic-curve Diffie–Hellman (ECDH) for image masking and Advanced Encryption Standard (AES) with updatable keys for image encryption. Besides, a key management module is used to organize the public and private keys, the patient’s digital signature provides authenticity, and integrity is guaranteed by using the Merkle tree. Also, we evaluated our proposed algorithm in terms of several performance indicators including, peak signal-to-noise ratio (PSNR) analysis, correlation analysis, entropy analysis, histogram analysis, and timing analysis. Consequently, our empirical results revealed the superiority of MID-Crypt scoring the best performance values for PSNR, correlation, entropy, and encryption overhead. Finally, we compared the security measures for the MID-Crypt algorithm with other studies, the comparison revealed the distinguishable security against several common attacks such as side-channel attacks (SCA), differential attacks, man-in-the-middle attacks (MITM), and algebraic attacks.

Recently, medical image encryption has gained special attention due to the nature and sensitivity of medical data and the lack of effective image encryption using innovative encryption techniques. Several encryption schemes have been recommended and developed in an attempt to improve medical image encryption. The majority of these studies rely on conventional encryption techniques. However, such improvements have come with increased computational complexity and slower processing for encryption and decryption processes. Alternatively, the engagement of intelligent models such as deep learning along with encryption schemes exhibited more effective outcomes, especially when used with digital images. This paper aims to reduce and change the transferred data between interested parties and overcome the problem of building negative conclusions from encrypted medical images. In order to do so, the target was to transfer from the domain of encrypting an image to encrypting features of an image, which are extracted as float number values. Therefore, we propose a deep learning-based image encryption scheme using the autoencoder (AE) technique and the advanced encryption standard (AES). Specifically, the proposed encryption scheme is supposed to encrypt the digest of the medical image prepared by the encoder from the autoencoder model on the encryption side. On the decryption side, the analogous decoder from the auto-decoder is used after decrypting the carried data. The autoencoder was used to enhance the quality of corrupted medical images with different types of noise. In addition, we investigated the scores of structure similarity (SSIM) and mean square error (MSE) for the proposed model by applying four different types of noise: salt and pepper, speckle, Poisson, and Gaussian. It has been noticed that for all types of noise added, the decoder reduced this noise in the resulting images. Finally, the performance evaluation demonstrated that our proposed system improved the encryption/decryption overhead by 50–75% over other existing models.

Data security is critical, particularly in medical imaging, yet remains challenging. Many research efforts have focused on enhancing medical image security, particularly during network transmission. Ensuring confidentiality and authenticity is a key priority for researchers. However, traditional encryption methods are unsuitable for IoT environments due to data size limitations. Lightweight encryption algorithms that preserve confidentiality, integrity, and authenticity can address these limitations. This paper proposes an efficient, lightweight method to encrypt and authenticate medical images in healthcare systems. The approach splits images into diagonal and non-diagonal blocks, and then processes them in two phases: (1) non-diagonal blocks are permuted using inter-block differences and XORed with diagonal blocks for substitution; (2) diagonal blocks are encrypted via AES and enhanced CBC mode with a tag mechanism for integrity. Security tests (histograms, correlation, entropy, NPCR, UACI) verify the scheme’s robustness. The results show that the model outperforms existing techniques in efficacy and attack resistance, making it viable for medical IoT and smart surveillance.

Due to the increased attacks on different applications, data security has become crucial. Many modes can be used to operate the advanced encryption standard (AES), some of which provide integrity, and some outperform other modes in security and simplicity. In this paper, the chain block cipher (CBC) mode has been modified to provide more security to the encrypted data by making it robust against the bit-flipping attack and adding an integrity approach using the keyedhash function. In addition, using the keyd-hash function increases the number of keys needed in CBC-AES to two keys, and this can make the proposed model more secure against bruteforce attacks and Grover’s quantum search algorithm.

A Distributed Denial of Service (DDoS) attack is a type of cybercrime that renders a target service unavailable by overwhelming it with traffic from several sources (attack nodes). In this paper, we focus on DDoS attacks on a computer network by spreading bots throughout the network. A mathematical differential equation model is proposed to represent the dynamism of nodes at different compartments of the model. The model considers two levels of security, with the assumption that the recovered nodes do not return to the same security level. In previous models, the recovered nodes are returned to be suspect on the same security level, which is an unrealistic assumption. Moreover, it is assumed that the attacker can use the infected target nodes to attack again. With such epidemic-like assumptions of infection, different cases are presented and discussed, and the stability of the model is analyzed as well; reversing the symmetry transformation of attacking nodes population is also proven. The proposed model has many parameters in order to precisely describe the infection movement and propagation. Numerical simulation methods are used to solve the developed system of equations using MATLAB, with the intention of finding the best counteraction to control DDoS spread throughout a network.