Medical Image Encryption by Content-Aware DNA Computing for Secure Healthcare

Wu, Yirui; Zhang, Lilai; Wan, Shaohua

doi:10.1109/tii.2022.3194590

Cited by 111 publications

(34 citation statements)

References 28 publications

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“…According to [40], 6G-based intelligent cybersecurity will lead to new techniques; some of these are given below. Finally, several studies have focused on detection performance in mobile environments [2], which is important for enhancing cybersecurity, encrypting medical images against various threats when transmitting data via wireless broadcasting [41], and using deep-learning algorithms in segmentation tasks with various kinds of networks [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

Algarni

Thayananthan

2023

IEEE Access

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Sixth-generation (6G)-based communications have many applications and are emerging as a new system to utilize existing vehicles and communication devices in autonomous vehicles (AVs). Electric vehicles and AVs not supporting the integration of intelligent cybersecurity will become vulnerable, and their internal functions, features, and devices providing services will be damaged. This paper presents an intelligent cybersecurity model integrating intelligent features according to the emerging 6G-based technology based on evolving cyberattacks. The model's novel design was developed using the necessary algorithms to provide quick and proactive decisions with intelligent cybersecurity based on 6G (IC6G) policies when AVs face cyberattacks. In this model, network security algorithms incorporating intelligent techniques are developed using applied cryptography. Money transaction handling services implemented in an AV are considered an example to determine the security and intelligence level depending on the IC6G policies. Intelligence, complexity, and energy efficiency (EE) are assessed. Finally, we conclude that the model results are effective for intelligently detecting and preventing cyberattacks on AVs.

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Section: Literature Reviewmentioning

confidence: 99%

Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

Algarni

Thayananthan

2023

IEEE Access

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“…Wu et al [21] proposed fast object recognition and picture enhancement tasks may be completed using an edge computing and multitask-driven architecture. To encrypt medical pictures and protect patient privacy and the healthcare environment, Wu et al [22] presented a unique content-aware deoxyribonucleic acid (DNA) computer system. A two-stage DL model for effective NIDS was proposed by Khan et al [23] using stacked autoencoder with softmax for classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Grey Wolf Optimization- (IGWO-) Based Feature Selection on Multiview Features and Enhanced Multimodal-Sequential Network Intrusion Detection Approach

Yuvaraja¹,

Arunkumar²,

Kumar³

et al. 2023

Wireless Communications and Mobile Computing

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The goal of the network intrusion detection system (NIDS) is to spot malicious activity in a network. It seeks to do that by examining the behavior of the traffic network. To find abnormalities, the NIDS heavily use machine learning (ML) and data mining techniques. The performance of NIDSs is significantly impacted by feature selection. This is due to the numerous characteristics that are used in anomaly identification, which take a lot of time. The time required to analyze traffic behavior and raise the accuracy level is thus influenced by the feature selection strategy. In the current work, the researcher’s goal was to provide a feature selection model for NIDSs. IGWO (improved grey wolf optimizations) for FSs (feature selections) was proposed to address these difficulties. The three primary processes in this proposed study are preprocessing, extractions and classifications of FSs, and evaluations of results. IGWOs are used to choose a subset of input variables by minimizing features to measure the accuracy in the search space and discover the best solution. A particular structure of HPNs (hierarchical progressive networks) is controlled by the MDAEs (multimodal deep autoencoders) and ABLSTMs (attention-based long short-term memories) for enhanced multimodal-sequential IDSs, i.e., AB-LSTMs. It is possible to understand relationships between neighboring network connections automatically and efficiently integrate information from many levels of characteristics inside a network connection using the EMS-DHPN technique simultaneously. This work’s suggested hybrid IDSs called IGWO-EMS-DHPN technique were evaluated using two intrusion datasets: UNSW-NB15 and CICIDS-2017 which is compared with other existing classifiers in terms of relative accuracies, precisions, recalls, and F 1 -scores in categorizations. While several classifiers have been developed, the suggested IGWO-EMS-DHPN classifier obtains maximum accuracy.

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“…For example, chaotic sequences are generated by chaotic systems to provide pseudo-randomness for the encryption scheme, confusion and diffusion are realized by traditional Fridrich structures; [10][11][12][13] the content in medical images is discriminated to partially encrypt the visually effective part; [14] partial encryption of the part of interest is realized by combining with deep-learning encryption and decryption networks; [2] and a new content-aware DNA computing algorithm is designed to encrypt medical images and other algorithms. [15] All these schemes can protect the communication security of medical images, but in the realization of application scenarios, all users get the same visual level of decrypted images. The currently proposed schemes do not differentiate the decryption process for users with diverse security levels, which makes these users get the decrypted images with same visual levels.…”

Section: Introductionmentioning

confidence: 99%

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row–column confusion and closed-loop bi-directional diffusion

Zhang 张,

Mou 牟,

Banerjee

et al. 2024

Chinese Phys. B

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Security during remote transmission has been an important concern for researchers in recent years. In this paper, a hierarchical encryption multi-image encryption scheme for people with different security levels is designed, and a multi-image encryption (MIE) algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper. While ensuring secure communication of medical image information, people with different security levels have different levels of decryption keys, and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys. The highest security level can obtain decrypted images without watermarks, and at the same time, patient information and copyright attribution can be verified by obtaining watermark images. The experimental results show that the scheme is sufficiently secure as a MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

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Medical Image Encryption by Content-Aware DNA Computing for Secure Healthcare

Cited by 111 publications

References 28 publications

Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

Autonomous Vehicles With a 6G-Based Intelligent Cybersecurity Model

Improved Grey Wolf Optimization- (IGWO-) Based Feature Selection on Multiview Features and Enhanced Multimodal-Sequential Network Intrusion Detection Approach

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row–column confusion and closed-loop bi-directional diffusion

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