QUANTUM-INSPIRED GENERATIVE ADVERSARIAL NETWORK FOR INTELLIGENT INTRUSION DETECTION

Authors

  • M. SWAPNA,SESHAIAH REPUDI,Dr. G. GURUKESAVA DAS Author

DOI:

https://doi.org/10.48047/yv70xs27

Keywords:

Quantum Generative Adversarial Network, Intrusion Detection System, Network Security, NSL-KDD Dataset, Deep Learning, Cybersecurity.

Abstract

As complex cyberattacks grow in sophistication, network intrusion detection has become a key part of today's cyber security infrastructures. The traditional IDS methods have difficulty in detecting complex attacks or attacks which never occurred before because of the high dimensionality of network traffic data and changing network traffic patterns. 

Downloads

Download data is not yet available.

Downloads

Published

10.07.2026

Issue

Vol. 16 No. 2 (2026): volume 16 issue 2(2026)

Section

Articles

License

You are free to:

  1. Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
  2. Adapt — remix, transform, and build upon the material for any purpose, even commercially.
  3. The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  1. Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
  2. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Notices:

You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .

No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.

How to Cite

QUANTUM-INSPIRED GENERATIVE ADVERSARIAL NETWORK FOR INTELLIGENT INTRUSION DETECTION. (2026). International Journal of Information and Electronics Engineering, 16(2), 569-575. https://doi.org/10.48047/yv70xs27