An Efficient Framework for Secure Image Archival and Retrieval System Using Multiple Secret Share Creation Scheme

Devaraj, A. Francis Saviour; Murugaboopathi, G.; Elhoseny, Mohamed; Shankar, K.; Min, Kyungbok; Moon, Hyeonjoon; Joshi, Gyanendra Prasad

doi:10.1109/access.2020.3014346

Cited by 25 publications

(10 citation statements)

References 31 publications

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“…In this final stage, AFSA is applied to improve the efficacy of GRU model by optimal-tuning of the hyperparameters [22][23][24]. AFSA approach is a swarm intelligence technique that is developed based on the behaviour of animals [25].…”

Section: Afsa Based Hyperparameter Optimizationmentioning

confidence: 99%

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Hilal¹,

Hashim²,

Mohamed³

et al. 2023

Computers, Materials &Amp; Continua

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Cybersecurity-related solutions have become familiar since it ensures security and privacy against cyberattacks in this digital era. Malicious Uniform Resource Locators (URLs) can be embedded in email or Twitter and used to lure vulnerable internet users to implement malicious data in their systems. This may result in compromised security of the systems, scams, and other such cyberattacks. These attacks hijack huge quantities of the available data, incurring heavy financial loss. At the same time, Machine Learning (ML) and Deep Learning (DL) models paved the way for designing models that can detect malicious URLs accurately and classify them. With this motivation, the current article develops an Artificial Fish Swarm Algorithm (AFSA) with Deep Learning Enabled Malicious URL Detection and Classification (AFSADL-MURLC) model. The presented AFSADL-MURLC model intends to differentiate the malicious URLs from genuine URLs. To attain this, AFSADL-MURLC model initially carries out data preprocessing and makes use of glove-based word embedding technique. In addition, the created vector model is then passed onto Gated Recurrent Unit (GRU) classification to recognize the malicious URLs. Finally, AFSA is applied to the proposed model to enhance the efficiency of GRU model. The proposed AFSADL-MURLC technique was experimentally validated using benchmark dataset sourced from Kaggle repository. The simulation results confirmed the supremacy of the proposed AFSADL-MURLC model over recent approaches under distinct measures.

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Section: Afsa Based Hyperparameter Optimizationmentioning

confidence: 99%

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Hilal¹,

Hashim²,

Mohamed³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Each pixel, present in the input image, is regarded as the framework of n transformed techniques and is named as share. Each share includes a fixed number of sub-pixels as Red-Green-Blue (RGB) images [16]. The share towards RGB is separately denoted as R sh , G sh, and B sh and are determined as follows.…”

Section: Share Creationmentioning

confidence: 99%

Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

Almasoud¹,

Abdelmaboud²,

Alsubaei³

et al. 2022

Computers, Materials &Amp; Continua

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Cyber-Physical System (CPS) involves the combination of physical processes with computation and communication systems. The recent advancements made in cloud computing, Wireless Sensor Network (WSN), healthcare sensors, etc. tend to develop CPS as a proficient model for healthcare applications especially, home patient care. Though several techniques have been proposed earlier related to CPS structures, only a handful of studies has focused on the design of CPS models for health care sector. So, the proposal for a dedicated CPS model for healthcare sector necessitates a significant interest to ensure data privacy. To overcome the challenges, the current research paper designs a Deep Learning-based Intrusion Detection and Image Classification for Secure CPS (DLIDIC-SCPS) model for healthcare sector. The aim of the proposed DLIDIC-SCPS model is to achieve secure image transmission and image classification process for CPS in healthcare sector. Primarily, data acquisition takes place with the help of sensors and detection of intrusions is performed using Fuzzy Deep Neural Network (FDNN) technique. Besides, Multiple Share Creation (MSC) approach is used to create several shares of medical image so as to accomplish security. Also, blockchain is employed as a distributed data storage entity to create a ledger that provides access to the client. For image classification, Inception v3 with Fuzzy Wavelet Neural Network (FWNN) is utilized that diagnose the disease from the applied medical image. Finally, Salp Swarm Algorithm (SSA) is utilized to fine tune the parameters involved in WNN model, thereby boosting its classification performance. A wide range of simulations was carried out to highlight the superiority of the proposed DLIDIC-SCPS technique. The simulation outcomes confirm that DLIDIC-SCPS approach demonstrates promising results in terms of security, privacy, and image classification outcomes over recent state-of-the-art techniques.

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“…After the shares are reconstructed, the encrypted and decrypted processes utilizing EPKC method, which is applied to all color bands of the reconstructed share. All color bands of image are separated as to blocks before the encrypted and decrypted procedures [24]. The blocks are divided into 4*4 in size.…”

Section: Share Reconstructionmentioning

confidence: 99%

Blockchain Enabled Emperor Penguin Optimizer Based Encryption Technique for Secure Image Management System

Padmavathi¹,

Rajagopalan²

2021

Wireless Pers Commun

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In recent years, the electronic sharing of digital images faces a major threat to security, as the existing image transmission infrastructure is mainly based on the trust of third parties. At the same time, the available solutions are placed on the cloud based centralized data center, which is expensive, requires large storage area, and security issues regarding the transmission of data over the network. So, it is needed to develop an image management system which enables sharing and storing of digital images effectively. This paper develops novel multiple share creation schemes with block technology for secure image management (MSCCBT-SIM) systems. The MSCCBT-SIM model allows the user to create consensus with no dependencies on central authorities. It involves an MSC which involves share creation and share encryption using emperor penguin optimizer based ElGamal public key cryptosystem (EPO-EPKC). In addition, the blockchain is used as a distributed data storage mechanism to generate a ledger for permitting access to the user and prevent third party access to the encrypted shares. The application of blockchain technology and MSC techniques helps to achieve decentralization, highly reliable, inexpensive, and secure transmission and storage of digital images. In order to validate the effective performance of the MSCCBT-SIM model, a series of simulations take place and investigated the results interms of different measures. The experimental results ensured the better performance of the MSCCBT-SIM model over the state of art methods.

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An Efficient Framework for Secure Image Archival and Retrieval System Using Multiple Secret Share Creation Scheme

Cited by 25 publications

References 31 publications

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Malicious URL Classification Using Artificial Fish Swarm Optimization and Deep Learning

Deep Learning with Image Classification Based Secure CPS for Healthcare Sector

Blockchain Enabled Emperor Penguin Optimizer Based Encryption Technique for Secure Image Management System

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