[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122334-en":3,"doc-seo-122334-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122334,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning in Chaos-Based Encryption - Theory, Implementations, and Applications","Chaos-based encryption supports secure communication through complexity and unpredictability, yet efficient, low-power, and attack-resistant implementations remain difficult. Machine learning has emerged as a key tool to improve security and efficiency while maximizing emerging computing platforms. This paper studies ML techniques for ML-assisted secure chaotic communication for wearable devices, focusing on chaos-based encryption. It analyzes ML foundations for signal synchronization, noise reduction, encryption, and evaluates methods and metrics, while also surveying ML-enabled defenses, attacks, and real-world application opportunities in IoT, cloud, and wireless networks.","Received 13 October 2023, accepted 30 October 2023, date of publication 8 November 2023, date of current version 13 November 2023. Digital Object Identifier 10.1109/ACCESS.2023.3331320  \nMachine Learning in Chaos-Based Encryption: Theory, Implementations, and Applications  \nJINHA HWANG 1, GAURI KALE2, PERSIS PREMKUMAR PATEL 1,  \nRAHUL VISHWAKARMA1,(Graduate Student Member, IEEE), MEHRDAD ALIASGARI 1, AVA HEDAYATIPOUR2,(Member, IEEE), AMIN REZAEI 1,(Member, IEEE),  \nAND HOSSEIN SAYADI1,(Member, IEEE)  \n1Department of Computer Engineering and Computer Science, California State University Long Beach, Long Beach, CA 90840, USA  \n2Department of Electrical Engineering, California State University Long Beach, Long Beach, CA 90840, USA Corresponding author: Ava Hedayatipour ([Ava.Hedayatipour@csulb.edu](Ava.Hedayatipour@csulb.edu))  \nThis work was supported by the National Science Foundation under Grant 2131156 .  \nABSTRACT Chaos-based encryption is a promising approach to secure communication due to its complexity and unpredictability. However, various challenges lie in the design and implementation of efficient, low-power, attack-resistant chaos-based encryption schemes with high encryption and decryption rates. In addition, Machine learning (ML) has emerged as a promising tool for enhancing the growing security and efficiency concerns and maximizing the potential of emerging computing platforms across diverse domains. With the rapid advancements in technology and the increasing complexity of computing systems, ML offers a unique approach to addressing security challenges and optimizing performance. This paper presents a comprehensive study on the application of ML techniques to secure chaotic communication for wearable devices, with an emphasis on chaos-based encryption. The theoretical foundations of ML for secure chaotic communication are discussed, including the use of ML algorithms for signal synchronization, noise reduction, and encryption. Various ML algorithms, such as deep neural networks, support vector machines, decision trees, and ensemble learning methods, are explored for designing chaos-based encryption algorithms. This paper places a greater emphasis on methodological aspects, metrics, and performance evaluation of machine learning algorithms. In addition, the paper presents an in-depth investigation into stateof-the-art ML-assisted defense and attacks on chaos-based encryption schemes, covering their theoretical foundations and practical implementations. Furthermore, a review of the potential advantages and limitations associated with the utilization of ML techniques in secure communication systems and encryption is provided. The study extends to exploring the diverse range of applications that can benefit from ML-assisted encryption, such as secure communication in the Internet of Things (IoTs), cloud computing, and wireless networks. Overall, we provide insights into the applications of ML for secure chaotic communication in wearable devices, its challenges, and opportunities, offering a foundation for further research and development and facilitating advancements in the field of secure chaotic communication.  \nINDEX TERMS Quantum computing, quantum-safe, chaos, chaotic map, encryption, side channel attacks, machine learning, artificial intelligence, hardware security.  \nI. INTRODUCTION  \nThe amount of data created over the next three years will be greater than the data created in the past 30 years combined [1] . According to a study by the International Data  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Alberto Cano .  \nCorporation (IDC), the global datasphere, which includes all the data created, captured, and replicated, is expected to grow from 33 ZettaBytes (ZB) in 2018 to 175 ZB by 2025 (Fig. 1) . This represents a Compound Annual Growth Rate (CAGR) of 61% . The growth in data is being driven by the data generated from IoT sensors, wearable devices [2]","cbCaih2M0P2pQlYn","https://ap.wps.com/l/cbCaih2M0P2pQlYn","pdf",3386497,1,19,"English","en",105,"# Introduction\n## Data growth and healthcare/wearables security needs\n## Background and motivation for encryption\n# Chaos-based encryption and ML-assisted secure communication\n## ML foundations: synchronization and noise reduction\n## ML algorithms for encryption design\n# Defense and attacks using ML\n## State-of-the-art ML-assisted defense\n## Practical implementations and evaluation\n# Applications, challenges, and opportunities\n## IoT, cloud computing, and wireless networks","[{\"question\":\"What problem does the paper address in chaos-based encryption?\",\"answer\":\"It addresses the challenge of designing efficient, low-power, attack-resistant chaos-based encryption schemes with high encryption and decryption rates.\"},{\"question\":\"How does the paper use machine learning in secure chaotic communication for wearable devices?\",\"answer\":\"It discusses ML for signal synchronization, noise reduction, and encryption, and explores multiple ML algorithms to design chaos-based encryption methods.\"},{\"question\":\"Which kinds of ML-related security threats and protections does the paper cover?\",\"answer\":\"It investigates state-of-the-art ML-assisted defenses and attacks on chaos-based encryption schemes, including their theoretical foundations and practical implementations.\"}]","Machine Learning in Chaos-Based Encryption - 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