[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127693-en":3,"doc-seo-127693-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127693,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","A Fusion of Machine Learning and Cryptography for Fast Data Encryption through the Encoding of High and Moderate Plaintext Information Blocks","Within image encryption, a trade-off exists between computational complexity and secure data transmission integrity. Conventional robust image security often relies on extensive mathematics, which prevents real-time use. The proposed approach applies machine learning to improve efficiency without sacrificing security by classifying image pixel blocks into high-, moderate-, and low-information classes using SVM. Encryption is selectively performed on high and moderate blocks while low-information blocks remain unchanged, cutting computation time significantly. Performance is validated with accuracy, precision, recall, F1-score, and security metrics including correlation, PSNR, MSE, entropy, energy, and contrast, achieving about 97.4% accuracy and sub-second runtime.","A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks  \nArslan Shafique1 · Abid Mehmood2 · Moatsum Alawida2 · Mourad Elhadef2 · Mujeeb Ur Rehman3  \nReceived: 19 July 2022 / Revised: 26 February 2024 / Accepted: 13 March 2024 /  \nPublished online: 4 April 2024 © The Author(s) 2024  \nAbstract  \nWithin the domain ofimage encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by aMATLAB tool. Thesefindings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications.  \nKeyword Data security · Computational time · Machine learning · Internet of things  \nB Arslan Shafiquearslanshafi[que762@gmail.com](que762@gmail.com)  \n1 School of Biomedical Engineering, University of Glasgow, Glasgow, UK  \n2 Department of Computer Sciences, Abu Dhabi University, Abu Dhabi, United Arab Emirates  \n3 Cyber Technology Institute, School of Computer Science and Informatics, De Montfort University, Leicester, UK  \n1 Introduction  \nThe increasing use of sensitive medical, military and defense images in the Internet of Things (IoT) has resulted in a substantial surge in the volume of data transmitted through the Internet infrastructure [1, 2] . Given that the Internet is inherently insecure, there is a heightened risk of intruders attempting to compromise such sensitive data, potentially containing confidentialor classified information. Intruders may exploit vulnerabilities in unsecured communication methods, emphasizing the imperative need to safeguard this data against unauthorized access [3, 4] . Cryptography plays a pivotal role in achieving this protection, employing sophisticated mathematical algorithms to encrypt data prior to transmission [5, 6]. This becomes especially critical in the context of IoT, where securing the integrity and confidentiality of data is paramount across various sectors.  \nEncryption serves as a crucial layer of security for unencrypted data by transforming the transmitted information into a seemingly random or unintelligible form to potential adversaries [7] . Notable encryption algorithms widely used for this purpose include DES (Data Encryption Standard), AES (Advanced Encryption Standard), IDEA (International Data Encryption Algorithm), and RSA (Rivest, Shamir, and Adleman) [8–11] . While these algorithms demonstrate efficiency in terms of performance, they encounter computational challenges, particularly when dealing with large datasets. In the context of the IoT, where massive amounts of data are routinely transmitted and security is paramount, the efficiencyand computational demands of encryption algorithms become integral considerations for ensuring effective a","cbCaimk2ciQU8Lhs","https://ap.wps.com/l/cbCaimk2ciQU8Lhs","pdf",2394484,2,1,27,"English","en",105,"# Abstract\n# Introduction\n## Trade-off between security and computation in real-time encryption\n## Role of classic encryption algorithms and their limitations\n## Need to reduce chaos-based encryption time for IoT\n## Selective encryption of image blocks to optimize runtime\n## Using machine learning to accelerate encryption","[{\"question\":\"What problem does the proposed method target in image encryption?\",\"answer\":\"It targets the inherent trade-off between heavy computational complexity and secure transmission integrity, which makes real-time image encryption impractical with conventional approaches.\"},{\"question\":\"How are image pixel blocks handled to reduce computational time?\",\"answer\":\"Pixel blocks are classified into high-, moderate-, and low-information categories using an SVM, and encryption is applied only to high and moderate blocks while low-information blocks are left unencrypted.\"},{\"question\":\"How is security and performance evaluated in the research?\",\"answer\":\"Machine learning performance uses precision, recall, and F1-score, while security uses correlation, PSNR, MSE, entropy, energy, and contrast metrics, with results reported at very high accuracy and strong security indicators.\"}]","A Fusion of Machine Learning and Cryptography for Fast Data Encryption through the Encoding of High and Moderate Plaintext Information Blocks | 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problem does the proposed method target in image encryption?","Question",{"text":76,"@type":77},"It targets the inherent trade-off between heavy computational complexity and secure transmission integrity, which makes real-time image encryption impractical with conventional approaches.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are image pixel blocks handled to reduce computational time?",{"text":81,"@type":77},"Pixel blocks are classified into high-, moderate-, and low-information categories using an SVM, and encryption is applied only to high and moderate blocks while low-information blocks are left unencrypted.",{"name":83,"@type":74,"acceptedAnswer":84},"How is security and performance evaluated in the research?",{"text":85,"@type":77},"Machine learning performance uses precision, recall, and F1-score, while security uses correlation, PSNR, MSE, entropy, energy, and contrast metrics, with results reported at very high accuracy and strong security 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