Red Teaming Generative AI/NLP, the BB84 quantum cryptography protocol
and the NIST-approved Quantum-Resistant Cryptographic Algorithms
- URL: http://arxiv.org/abs/2310.04425v1
- Date: Sun, 17 Sep 2023 00:59:14 GMT
- Title: Red Teaming Generative AI/NLP, the BB84 quantum cryptography protocol
and the NIST-approved Quantum-Resistant Cryptographic Algorithms
- Authors: Petar Radanliev, David De Roure, Omar Santos
- Abstract summary: This research delves into the cybersecurity implications of AI/Natural Language Processing (NLP) models and quantum cryptographic protocols.
Utilising Python and C++ as primary computational tools, the study employs a "red teaming" approach, simulating potential cyber-attacks.
The study's overarching goal is to ensure that as the digital world transitions to quantum-enhanced operations, it remains resilient against AI-driven cyber threats.
- Score: 2.3020018305241337
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the contemporary digital age, Quantum Computing and Artificial
Intelligence (AI) convergence is reshaping the cyber landscape, introducing
unprecedented opportunities and potential vulnerabilities.This research,
conducted over five years, delves into the cybersecurity implications of this
convergence, with a particular focus on AI/Natural Language Processing (NLP)
models and quantum cryptographic protocols, notably the BB84 method and
specific NIST-approved algorithms. Utilising Python and C++ as primary
computational tools, the study employs a "red teaming" approach, simulating
potential cyber-attacks to assess the robustness of quantum security measures.
Preliminary research over 12 months laid the groundwork, which this study seeks
to expand upon, aiming to translate theoretical insights into actionable,
real-world cybersecurity solutions. Located at the University of Oxford's
technology precinct, the research benefits from state-of-the-art infrastructure
and a rich collaborative environment. The study's overarching goal is to ensure
that as the digital world transitions to quantum-enhanced operations, it
remains resilient against AI-driven cyber threats. The research aims to foster
a safer, quantum-ready digital future through iterative testing, feedback
integration, and continuous improvement. The findings are intended for broad
dissemination, ensuring that the knowledge benefits academia and the global
community, emphasising the responsible and secure harnessing of quantum
technology.
Related papers
- Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems [42.184783937646806]
We investigate the potential impact of quantum computing and machine learning (QML) on cybersecurity applications of traditional ML.
First, we explore the potential advantages of quantum computing in machine learning problems specifically related to cybersecurity.
Then, we describe a methodology to quantify the future impact of fault-tolerant QML algorithms on real-world problems.
arXiv Detail & Related papers (2025-02-16T15:49:25Z) - Quantum-driven Zero Trust Framework with Dynamic Anomaly Detection in 7G Technology: A Neural Network Approach [0.0]
We propose the Quantum Neural Network-Enhanced Zero Trust Framework (QNN-ZTF) for enhanced security.
We integrate Zero Trust Architecture, Intrusion Detection Systems, and Quantum Neural Networks (QNNs) for enhanced security.
We show improved cyber threat mitigation, demonstrating the framework's effectiveness in reducing false positives and response times.
arXiv Detail & Related papers (2025-02-11T18:59:32Z) - SeQUeNCe GUI: An Extensible User Interface for Discrete Event Quantum Network Simulations [55.2480439325792]
SeQUeNCe is an open source simulator of quantum network communication.
We implement a graphical user interface which maintains the core principles of SeQUeNCe.
arXiv Detail & Related papers (2025-01-15T19:36:09Z) - Defending crosstalk-mediated quantum attacks using dynamical decoupling [0.276240219662896]
In the past few years, the field of quantum computing is reaching new heights with significant advancements in algorithm development.
Companies and research labs are actively working to build fault-tolerant quantum computers.
arXiv Detail & Related papers (2024-09-22T21:22:05Z) - A Security Assessment tool for Quantum Threat Analysis [34.94301200620856]
The rapid advancement of quantum computing poses a significant threat to many current security algorithms used for secure communication, digital authentication, and information encryption.
A sufficiently powerful quantum computer could potentially exploit vulnerabilities in these algorithms, rendering data in insecure transit.
This work developed a quantum assessment tool for organizations, providing tailored recommendations for transitioning their security protocols into a post-quantum world.
arXiv Detail & Related papers (2024-07-18T13:58:34Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - Assessing the Benefits and Risks of Quantum Computers [0.7224497621488283]
We review what is currently known on the potential uses and risks of quantum computers.
We identify 2 large-scale trends -- new approximate methods and the commercial exploration of business-relevant quantum applications.
We conclude there is a credible expectation that quantum computers will be capable of performing computations which are economically-impactful.
arXiv Detail & Related papers (2024-01-29T17:21:31Z) - Generative AI-enabled Quantum Computing Networks and Intelligent
Resource Allocation [80.78352800340032]
Quantum computing networks execute large-scale generative AI computation tasks and advanced quantum algorithms.
efficient resource allocation in quantum computing networks is a critical challenge due to qubit variability and network complexity.
We introduce state-of-the-art reinforcement learning (RL) algorithms, from generative learning to quantum machine learning for optimal quantum resource allocation.
arXiv Detail & Related papers (2024-01-13T17:16:38Z) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.