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Expanding Cybersecurity with Advanced Machine Learning

Author

Listed:
  • D Sirisha
  • Anjani Dedepya S
  • A Uthpala Devi
  • K Ram Tejesh
  • M Rohith Naidu
  • K M R Yaswanth Kumar

Abstract

The increasing complexity of the cybersecurity landscape, driven by the unprecedented growth of digital connectivity and the proliferation of IoT devices, has exposed significant vulnerabilities in traditional security architectures. In the current work on “Cybersecurity Data Science: An Overview from Machine Learning Perspective”. The current work is a review work that focuses on providing a critical analysis of the contributions, exploring both the strengths and limitations of the approaches. Furthermore, advanced methodologies such as deep learning, federated learning, quantum cryptography, and blockchain offer superior efficacy in addressing the multifaceted challenges of modern cybersecurity. This work serves as a vital expansion of the original work, underscoring the necessity of evolving cybersecurity models to align with cyber threats' dynamic and increasingly sophisticated nature.

Suggested Citation

  • D Sirisha & Anjani Dedepya S & A Uthpala Devi & K Ram Tejesh & M Rohith Naidu & K M R Yaswanth Kumar, 2026. "Expanding Cybersecurity with Advanced Machine Learning," Int. J. Sci. Res. Artif. Intell. Mach. Learn, International Journal of Scientific Research in Artificial Intelligence and Machine Learning, vol. 2(2), pages 26-32, April.
    • Handle: RePEc:jbo:ijsrml:v2:y2026:i2:id:5
    Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML26224
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