Blockchain and AI: A Synergistic Approach to Enhancing Cybersecurity Protocols

Main Article Content

Armaan Malik

Abstract

Blockchain technology offers promising solutions for enhancing cybersecurity, particularly in establishing secure and immutable records. This paper examines the synergistic integration of blockchain and AI to bolster cybersecurity protocols, focusing on data integrity, secure transactions, and identity verification. By employing machine learning algorithms to analyze transaction patterns on the blockchain, we can identify anomalies and potential fraud in real time. Case studies demonstrate how this integrated approach not only strengthens security but also streamlines processes across various sectors, including finance and supply chain management, illustrating the transformative potential of combining these technologies.

Article Details

How to Cite
Blockchain and AI: A Synergistic Approach to Enhancing Cybersecurity Protocols (A. Malik , Trans.). (2022). International Journal of Creative Research In Computer Technology and Design, 4(4). https://jrctd.in/index.php/IJRCTD/article/view/73
Section
Articles

How to Cite

Blockchain and AI: A Synergistic Approach to Enhancing Cybersecurity Protocols (A. Malik , Trans.). (2022). International Journal of Creative Research In Computer Technology and Design, 4(4). https://jrctd.in/index.php/IJRCTD/article/view/73

References

Boppiniti, S. T. (2021). Real-Time Data Analytics with AI: Leveraging Stream Processing for Dynamic Decision Support. International Journal of Management Education for Sustainable Development, 4(4).

Boppiniti, S. T. (2019). Machine Learning for Predictive Analytics: Enhancing Data-Driven Decision-Making Across Industries. International Journal of Sustainable Development in Computing Science, 1(3).

Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).

Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.

Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).

Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.

Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.

Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.

Balantrapu, S. S. (2021). A Systematic Review Comparative Analysis of Machine Learning Algorithms for Malware Classification. International Scientific Journal for Research, 3(3), 1-29.

Balantrapu, S. S. (2020). AI-Driven Cybersecurity Solutions: Case Studies and Applications. International Journal of Creative Research In Computer Technology and Design, 2(2).

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 > >>