Blockchain and AI for IoT Security
Building a Decentralized Framework for Autonomous Devices
Keywords:
Blockchain, Artificial Intelligence, Internet of Things, Security Framework, Data BreachesAbstract
This paper proposes a blockchain-based framework for securing AI-driven Internet of Things (IoT) devices, highlighting how the combined technologies can mitigate risks of unauthorized access, data breaches, and tampering in autonomous systems. As the proliferation of IoT devices increases, so do the vulnerabilities associated with them, making it imperative to develop robust security measures. The integration of blockchain technology and artificial intelligence (AI) offers a promising solution, leveraging decentralized architecture and intelligent data processing to enhance security protocols. This framework outlines the design and implementation of a security model that combines blockchain’s immutability with AI’s predictive capabilities. Furthermore, it discusses the challenges faced in deploying such a framework and the potential implications for the future of IoT security. The findings suggest that a decentralized approach to securing IoT devices can significantly enhance their resilience against cyber threats.
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References
Gayam, Swaroop Reddy. "Artificial Intelligence in E-Commerce: Advanced Techniques for Personalized Recommendations, Customer Segmentation, and Dynamic Pricing." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 105-150.
Chitta, Subrahmanyasarma, et al. "Decentralized Finance (DeFi): A Comprehensive Study of Protocols and Applications." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 124-145.
Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Predictive Maintenance of Banking IT Infrastructure: Advanced Techniques, Applications, and Real-World Case Studies." Journal of Deep Learning in Genomic Data Analysis 2.1 (2022): 86-122.
Putha, Sudharshan. "AI-Driven Predictive Analytics for Maintenance and Reliability Engineering in Manufacturing." Journal of AI in Healthcare and Medicine 2.1 (2022): 383-417.
Sahu, Mohit Kumar. "Machine Learning for Personalized Marketing and Customer Engagement in Retail: Techniques, Models, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 219-254.
Kasaraneni, Bhavani Prasad. "AI-Driven Policy Administration in Life Insurance: Enhancing Efficiency, Accuracy, and Customer Experience." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 407-458.
Vangoor, Vinay Kumar Reddy, et al. "Energy-Efficient Consensus Mechanisms for Sustainable Blockchain Networks." Journal of Science & Technology 1.1 (2020): 488-510.
Kondapaka, Krishna Kanth. "AI-Driven Demand Sensing and Response Strategies in Retail Supply Chains: Advanced Models, Techniques, and Real-World Applications." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 459-487.
Kasaraneni, Ramana Kumar. "AI-Enhanced Process Optimization in Manufacturing: Leveraging Data Analytics for Continuous Improvement." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 488-530.
Pattyam, Sandeep Pushyamitra. "AI-Enhanced Natural Language Processing: Techniques for Automated Text Analysis, Sentiment Detection, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 371-406.
Kuna, Siva Sarana. "The Role of Natural Language Processing in Enhancing Insurance Document Processing." Journal of Bioinformatics and Artificial Intelligence 3.1 (2023): 289-335.
George, Jabin Geevarghese. "HARNESSING GENERATIVE AI FOR ENTERPRISE APPLICATION MODERNIZATION: ENHANCING CYBERSECURITY AND DRIVING INNOVATION." INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) 15.3 (2024): 377-392.
Katari, Pranadeep, et al. "Cross-Chain Asset Transfer: Implementing Atomic Swaps for Blockchain Interoperability." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 102-123.
Karunakaran, Arun Rasika. "A Data-Driven Approach for Optimizing Omni-Channel Pricing Strategies through Machine Learning." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 588-630.
Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "SLP (Systematic Layout Planning) for Enhanced Plant Layout Efficiency." International Journal of Science and Research (IJSR) 13.6 (2024): 820-827.
Venkata, Ashok Kumar Pamidi, et al. "Implementing Privacy-Preserving Blockchain Transactions using Zero-Knowledge Proofs." Blockchain Technology and Distributed Systems 3.1 (2023): 21-42.
Namperumal, Gunaseelan, Akila Selvaraj, and Deepak Venkatachalam. "Machine Learning Models Trained on Synthetic Transaction Data: Enhancing Anti-Money Laundering (AML) Efforts in the Financial Services Industry." Journal of Artificial Intelligence Research 2.2 (2022): 183-218.
Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Debasish Paul. "AI-Driven Synthetic Data Generation for Financial Product Development: Accelerating Innovation in Banking and Fintech through Realistic Data Simulation." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 261-303.
Pradeep Manivannan, Priya Ranjan Parida, and Chandan Jnana Murthy, “Strategic Implementation and Metrics of Personalization in E-Commerce Platforms: An In-Depth Analysis”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 2, pp. 59–96, Aug. 2021
Yellepeddi, Sai Manoj, et al. "Blockchain Interoperability: Bridging Different Distributed Ledger Technologies." Blockchain Technology and Distributed Systems 2.1 (2022): 108-129.
