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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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Impact Factor : 8.14
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| Paper Title: | Enhancing Secure Cryptography for Sustainable IoT Applications through Artificial Intelligence and Machine Learning |
| Authors Name: | Kritika Purohit , Dr. A.K. Sharma |
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IJRTI_213588
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| Published Paper Id: | IJRTI2606102 |
| Published In: | Volume 11 Issue 6, June-2026 |
| DOI: | |
| Abstract: | The Internet of Things (IoT) has revolutionized modern digital infrastructure by connecting billions of devices across domains such as healthcare, smart cities, industrial automation, and transportation. However, the rapid growth of IoT networks has also introduced significant security challenges due to the limited computational power, memory, and energy resources of many connected devices. Traditional cryptographic techniques provide essential security services, but their implementation in resource-constrained environments often results in increased computational overhead and reduced efficiency. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising technologies for enhancing IoT security by enabling intelligent threat detection, adaptive authentication, dynamic key management, and real-time security monitoring. In addition to strengthening cybersecurity, these technologies can optimize resource utilization and support sustainable IoT operations by reducing unnecessary computational and energy consumption. This paper reviews the role of AI and ML in improving cryptographic security for sustainable IoT applications. It discusses key security challenges, examines recent advancements in AI-assisted cryptographic mechanisms, and proposes the Adaptive Intelligent Cryptographic Engine (AICE), a conceptual framework that integrates lightweight cryptography with intelligent security techniques. The study highlights the potential of AI-driven cryptographic solutions to develop secure, scalable, and energy-efficient IoT ecosystems capable of addressing future cybersecurity demands. |
| Keywords: | Internet of Things (IoT), Artificial Intelligence, Machine Learning, Cryptography, IoT Security, Lightweight Cryptography, Sustainable IoT, Cybersecurity. |
| Cite Article: | "Enhancing Secure Cryptography for Sustainable IoT Applications through Artificial Intelligence and Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 6, page no.a950-a969, June-2026, Available :http://www.ijrti.org/papers/IJRTI2606102.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2606102
Registration ID:213588
Published In: Volume 11 Issue 6, June-2026
DOI (Digital Object Identifier):
Page No: a950-a969 Country: Jodhpur, Rajasthan, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2606102 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2606102 |
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ISSN: 2456-3315
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