Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-8· 0 citations· 28 references
Abstract
Federated Learning (FL) enables collaborative model learning without the need to share raw data, but its communication links are vulnerable to interception, replay, and man-in-the-middle (MITM) attacks. The existing key exchange methods rely on computational hardness assumptions, which can be broken by post-quantum attackers. In this paper, a secure federated learning framework improved by Quantum Key Distribution (QKD) is proposed, which integrates BB84-like quantum key generation and authenticated encryption on a per-round basis, as well as Quantum Bit Error Rate (QBER)-assisted intrusion detection. A new cryptographic key is produced in each federated round, making it immune to replay attacks and allowing for detection of tampering. Theoretical calculations show that intercept-resend attacks lead to a minimum expected QBER of 25%, making it easier to detect statistically. Experimental results on the MNIST dataset show near-perfect detection rates for MITM and replay attacks, with QBER values increasing from about 1% (serving as a benign scenario) to about 26% in an attack scenario. Communication overhead is kept below 10%, with negligible computational latency compared to local training. The experiments show that the use of QKD-based key refresh improves FL communication security while still ensuring model convergence.
In this paper, we recommend an Onion Routing framework powered by federated learning and augmented with E91-based quantum key distribution (QKD) to protect next-generation communication systems like 5G-supported satellite and spaceborne IoT networks. Conventional encryption techniques protect message content but are still susceptible to traffic analysis and developing quantum attacks, necessitating layered, robust protection. In the suggested solution, locally on resourcelimited nodes, lightweight intrusion detection models are trained, whereas just onion-encrypted updates are shared for global aggregation, while keeping privacy intact and bandwidth usage minimum. Onion Routing offers multi-layer anonymity against adversarial eavesdropping, and QKD gives quantum-resilient key distribution immune to cryptanalytic attacks. Experimental testing on the X-IIoTID dataset indicates that the framework records a global accuracy of 98.03% with a loss of 0.0567, which confirms its effectiveness in identifying distributed denial-of-service (DDoS) attacks. Through decentralized intelligence, anonymity, and quantum-level security, this research sets the stage for a scalable and future-proof communication model for vital spaceborne applications.
Samiksha Gharmalkar, Bhavya Vora, Lakshin Pathak et al.· 2026 IEEE International Work...· 0 citations
The rapid development of quantum computing poses a significant threat to classical cryptographic systems that rely on computational hardness assumptions. In order to tackle this difficulty, this paper suggests a hybrid quantum-inspired secure communication system which merges Quantum Key Distribution (QKD) based on the BB84 protocol and classical encryption, steganography and blockchain-based auditing. The suggested system facilitates safe transmission of all data modalities such as text, images, audio and video. To monitor quantum Bit Error Rate (QBER) to identify attempts of eavesdropping and simulate the adversarial interference, the system is trained on a GAN-like attack model. The system also enforces two-factor authentication over Time-based One-Time Passwords (TOTP) and the logs are stored in an immutable blockchain system through a minimal blockchain implementation. Security analytics dashboard monitor is a real-time operation that overviews the security measurements of the system. As in experimental findings, the framework proposed proves to be practical, scale-able and quantum resilient and befitting the contemporary secure communication set-up.
C. Prathima, Irala Suneetha, Boggula Naga Raja Mohan Reddy et al.· International Conference Com...· 0 citations
With the increasing sophistication of financial frauds, there is now a need for more advanced, secure, and scalable detection mechanisms. A fraud detection framework has been proposed that uses Federated Deep Learning (FDL) and Quantum Key Distribution (QKD) for non-IID financial data while carrying out secure communication. Using FL algorithms-FedAvg, FedAdagrad, FedAMP, and FedDyn-on partitioned client data, we demonstrate that FedDyn outperforms the other algorithms with an accuracy of 97.74%. Furthermore, we use Continuous-Variable QKD to encrypt the model updates to secure client-server communication, achieving a secure key ratio of above 98% and key rates of more than 250,000 bits/sec. Lastly, we implemented an elaborate suite of evaluations consisting of client-wise metrics, ROC curves, and t-SNE plots to validate the efficacy of our model implementation in terms of both performance and privacy preservation. Through our results, we address the brought-up importance of distributed intelligence powered by quantum encryption against advanced financial frauds.
Param Desai, Mohammad S. Obaidat, Mahek Desai et al.· International Conference on...· 0 citations
The necessity of a secure key exchange protocol arises from the critical need to establish encrypted communication over an untrusted network. Over time, a multitude of key exchange mechanisms have been developed to counteract adversarial threats. The Diffie-Hellman key exchange protocol (DHKE) is one of the most widely used protocols for symmetric key sharing. However, this protocol exhibits certain inherent limitations that attackers may exploit. It lacks authentication mechanism and is susceptible to Man-in-the-Middle (MITM) attacks and quantum attacks. To mitigate these vulnerabilities, we have designed a hybrid key exchange protocol combining DHKE with Learning With Errors (LWE), a lattice-based post-quantum primitive. This proposed protocol provides authentication via a Public Key Infrastructure (PKI) together with CRYSTALS-Dilithium digital signature, resilience against MITM attacks, and robustness against classical and quantum threats. We have done a security analysis using the Dolev-Yao threat model, extended to quantum-equipped attackers, and showed that the protocol achieves mutual authentication, session key secrecy, and forward secrecy under the hardness of LWE and DHKE. Lastly, we provided detailed parameter recommendations based on NIST standards and shed light on side-channel attacks.
A. K. M. Fakhrul Hossain· SUST Journal of Science and...· 0 citations
This paper proposes a quantum k-nearest neighbor (QkNN)-based multiclass attack detection framework for CVQKD systems that establishes a direct connection between attack detection and secret key generation, enabling a more realistic security evaluation for practical CVQKD systems.
The increasing needs in data sharing in the fields of finance, governance, and artificial intelligence pose a major threat to privacy, particularly in quantum computing. In this paper, a hybrid privacy-preserving system incorporating simulated BB84 Quantum Key Distribution (QKD) to generate secure keys, reversible pseudonymization with encrypted mapping vaults, automatic key rotation, and re-identification risk analysis by machine learning are introduced. Also, optional differential privacy layer allows irreversible anonymization in the cases of analysis. The proposed system will enable two modes, that is, recovery of secure data and the ability to publish data in privacy modes. The experimental findings indicate that authorized users have 100% recovery accuracy, re-identification risk is low and is close to random guessing and data utility is acceptable given the privacy restrictions. FastAPI and Streamlit are used to implement the framework, which is appropriate in the real-world deployment in clouds.
Srividhya Ganesan, G. Vijayasekaran, Sujith R· 2026 4th International Confe...· 0 citations