Quantum Machine Learning and Cybersecurity

Memoria Investigaciones en Ingenieria, 2024

This article corresponds to an extensive review of Quantum Computers. We chose to consider topics relevant to quantum computing, such as machine learning, and the deepening of other issues related to cybersecurity. We introduce the reader to the basic concepts of quantum computing so that they can easily understand the terms mentioned in this review. We analyze different state of the art articles, and we give a summary of the contributions made. Finally, we conclude with the analysis of the bibliography, the research centers, the current state of the art, surprising results and conclusions.

ijetrm journal, 2023

Quantum Artificial Intelligence (Quantum AI) is poised to revolutionize FinTech, Cybersecurity, and Healthcare by enhancing data encryption, security resilience, and computational efficiency. Quantum algorithms such as Shor's Algorithm and Grover's Algorithm provide an exponential speed-up in solving cryptographic and security challenges. This research explores the integration of Quantum AI in fraud detection, quantum-secure encryption protocols, and medical AI decision-making. Our proposed Quantum AI security model demonstrates a 98% reduction in cyber vulnerabilities while improving AI-driven predictive analytics by 85%. The findings indicate that Quantum AI is a transformative force in securing the next era of digital finance, cybersecurity, and personalized healthcare. Different business sectors undergoing digital changes have increased the frequency of sophisticated digital security threats, which include cyber risks, financial misconduct, and health data breaches. Standard encryption methods and heuristic AI techniques fail to produce adequate security frameworks that address the modern evolving security threats. Financial transaction anomaly detection and quick pattern recognition processes are possible through the combination of Quantum AI technology and quantum computing probabilistic functions. Quantum-enhanced AI models that execute financial data processing achieve better analytics results, creating better assessment technologies while decreasing false fraud warnings. Post-quantum encryption strengthens security frameworks by allowing the cybersecurity sector to use Quantum AI to protect against future quantum attacks. The capabilities for computation in healthcare facilities strengthen substantially because of Quantum AI. Fast genomic research becomes possible through quantum-enhanced AI modeling, which leads to more effective individual medical solutions and better disease identification through quantum neural networks. Medical professionals receive fast options for creating new medicines and obtaining enhanced image processing abilities, generating accelerated diagnostic products for healthcare delivery systems. The use of quantum AI in practical applications remains limited because it requires more sophisticated hardware, improved quantum computational power, and standardized system interfaces. Quantum technology deployment requires technical alliances between multiple subjects with regulatory frameworks and substantial investment structures for success. The advancement of Quantum AI will revolutionize digital security because it revolutionizes FinTech services and cybersecurity and healthcare systems.

IEEE INFOCOM Workshops: Quantum Networked Applications and Protocols (QuNAP), 2025

Network security is a theme facing a continuous transformation, due to the diversity of users and devices that populate the Internet. On the technology side, quantum computing represents a reality in progress, offering new solutions and applications. Among these, Quantum Machine Learning (QML) is a good candidate to be employed in network security, thanks to benefits like computation speed-up and efficient treatment of big volumes of data. In this paper we analyze the effectiveness of two classical QML approaches (named AMPE and ANGE) in Attack Classification (AC) and Misuse Detection (MD) scenarios, comparing with two DL approaches (named 1D-CNN and HYBRID). Two popular and publicly available IOT securityaware datasets, i.e., IOT-NIDD and EDGE-IIOT, are considered for experimental evaluation. Moreover, we further examine the algorithms by performing a cross-evaluation, to test robustness of such models in network contexts they were not explicitly trained for. The experimental campaign we conduct shows how QML can represent a valid choice for the deployment in IOT network intrusion detection systems.

arXiv (Cornell University), 2021

In this paper, we examine the state art of quantum computing and analyze its potential effects in scientific computing and cybersecurity. Additionally, a non-technical description of the mechanics of the listed form of computing is provided to educate the reader for better understanding of the arguments provided. The purpose of this study is not only to increase awareness in this nescient technology, but also serve as a general reference guide for any individual wishing to study other applications of quantum computing in areas that include finance, biochemistry , and data science. Lastly, an educated argument is provided in the discussion section that addresses the implications this form of computing will have in the main areas examined.

Journal of Analytical Science and Technology , 2024

The technological advancements made in recent times, particularly in Artificial Intelligence (AI) and Quantum Computing, have brought about significant changes in technology. These advancements have profoundly impacted quantum cryptography, a field where AI methodologies hold tremendous potential to enhance the efficiency and robustness of cryptographic systems. However, the emergence of quantum computers has created a new challenge for existing security algorithms, commonly called the 'quantum threat'. Despite these challenges, there are promising avenues for integrating neural network-based AI in cryptography, which has significant implications for future digital security paradigms. This summary highlights the key themes in the intersection of AI and quantum cryptography, including the potential benefits of AI-driven cryptography, the challenges that need to be addressed, and the prospects of this interdisciplinary research area.

ArXiv, 2022

Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises-quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.

The advent of quantum computing poses unprecedented challenges to the security of classical cryptographic systems, driving the need for innovative approaches in cybersecurity. This chapter introduces the concept of quantum-aware cybersecurity, highlighting the pivotal role of large language models (LLMs) in addressing emerging threats. By leveraging LLMs' advanced capabilities in data analysis, threat detection, and adaptive learning, organizations can enhance resilience against quantum-era vulnerabilities. The chapter emphasizes the integration of LLMs with quantum-resistant cryptographic techniques, fostering a secure foundation for the next-generation digital ecosystem. This exploration underscores the urgency of adopting quantum-aware strategies to safeguard critical infrastructures and data integrity.

2024

The advent of quantum computing represents a transformative leap in computational power, offering the potential to solve complex problems beyond the reach of classical computers. However, this power also presents significant cybersecurity risks, as quantum algorithms can potentially break widely used encryption methods, jeopardizing data privacy and secure communications. This paper presents a comprehensive review of emerging quantum-related cybersecurity threats and explores defense mechanisms designed to mitigate these risks. We examine both theoretical and real-world implications of quantum computing on cryptographic systems, highlighting recent developments in post-quantum cryptography, Quantum Key Distribution (QKD), and hybrid classical-quantum security solutions. This review aims to provide a foundational understanding of the threats quantum computing poses to cybersecurity and discusses current and future defenses essential for safeguarding digital infrastructure.

International Research Journal of Advanced Engineering and Science, Volume 9, Issue 4, pp. 291-315, 2024

The advent of quantum computing presents a transformative challenge to contemporary cryptographic systems, threatening the security of widely used encryption algorithms such as RSA, ECC, and Diffie-Hellman. Leveraging principles like superposition and entanglement, quantum computers can efficiently solve complex mathematical problems that underpin classical cryptographic methods. This paper explores the profound implications of quantum computing on cybersecurity, highlighting quantum algorithms such as Shor's and Grover's, which demonstrate the vulnerabilities of current encryption systems. It reviews advancements in post-quantum cryptography, including lattice-based, code-based, and multivariate approaches, and discusses methodologies for integrating quantum-resistant algorithms into existing infrastructures. Case studies on quantum-safe blockchain solutions and quantumsecure communication networks illustrate practical applications and emerging technologies. By examining the challenges and proposing a framework for transitioning to quantum-safe systems, this study underscores the urgency of adopting proactive strategies to secure digital communications in the quantum era.

Future Internet

The first week of August 2022 saw the world’s cryptographers grapple with the second shocker of the year. Another one of the four post-quantum cryptography (PQC) algorithms selected by the NIST (National Institute of Standards and Technology) in a rigorous 5-year process was cracked by a team from Belgium. They took just 62 min and a standard laptop to break the PQC algorithm to win a USD 50,000 bounty from Microsoft. The first shocker came 6 months earlier, when another of the NIST finalists (Rainbow) was taken down. Unfortunately, both failed PQC algorithms are commercially available to consumers. With 80 of the 82 PQC candidates failing the NIST standardization process, the future of the remaining two PQC algorithms is, at best, questionable, placing the rigorous 5-year NIST exercise to build a quantum-safe encryption standard in jeopardy. Meanwhile, there is no respite from the quantum threat that looms large. It is time we take a step back and review the etiology of the problem...