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Strengthening KMS Security with Advanced Cryptography, Machine Learning, Deep Learning, and IoT Technologies

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Abstract

This paper presents an innovative approach to strengthening Key Management Systems (KMS) against the escalating landscape of cyber threats by integrating advanced cryptographic technologies, machine learning, deep learning, and the Internet of Things (IoT). As digital reliance and cyber-attacks surge, strengthening KMS security becomes paramount. Our research provides a comprehensive overview of the state-of-the-art in cloud data security, identifying key vulnerabilities in existing KMS. The paper also outlines a distinctive framework based on the combined application of advanced cryptography, machine learning, deep learning, and IoT, which represents a novel approach in the quest for robust KMS security. Our experimental results substantiate the efficacy of this unique blend of technologies, providing solid empirical evidence that such a fusion can successfully strengthen KMS against potential threats. As technologies and threat landscapes continue to evolve, our framework can serve as a benchmark for future research and practical implementations. It highlights the potential of integrated technological solutions to counter complex cybersecurity issues. Moreover, the approach we've developed can be adapted and expanded to cater to the specific needs of different sectors, such as finance, healthcare, and e-commerce, which are particularly vulnerable to cyber threats. The novelty of our work lies in the amalgamation of the four technologies and the creation of an empirically backed, robust framework, marking a significant stride in KMS security.

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Authors and Affiliations

  1. Department of Cyber Security, University of Delta, Agbor, Nigeria

    Justin Onyarin Ogala

  2. Department of Computer Science & Engineering, Mewat Engineering College (WAQF), Mewat, Nuh, Haryana, 122107, India

    Shahnawaz Ahmad

  3. Department of Electrical Engineering, Jamia Millia Islamia (A Central University), New Delhi, 110025, India

    Iman Shakeel & Shabana Mehfuz

  4. Department of Computer Science, School of Engineering Sciences & Technology (SEST) Jamia Hamdard (Deemed to be University), Hamdard Nagar, New Delhi, 110062, India

    Javed Ahmad

Authors
  1. Justin Onyarin Ogala
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  2. Shahnawaz Ahmad
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  3. Iman Shakeel
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  4. Javed Ahmad
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  5. Shabana Mehfuz
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Correspondence to Justin Onyarin Ogala.

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Ogala, J.O., Ahmad, S., Shakeel, I. et al. Strengthening KMS Security with Advanced Cryptography, Machine Learning, Deep Learning, and IoT Technologies. SN COMPUT. SCI. 4, 530 (2023). https://doi.org/10.1007/s42979-023-02073-9

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