Relevant bibliographies by topics / AI-enabled security

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI-enabled security'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "AI-enabled security"

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Patel, Ohm. "AI Enabled Security for Ethereum Blockchain Transactions." Journal of Artificial Intelligence & Cloud Computing 1, no. 4 (2022): 1–9. http://dx.doi.org/10.47363/jaicc/2022(1)372.

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Fang, Binxing, Jinqiao Shi, Zhongru Wang, and Weiqiang Yu. "AI-Enabled Cyberspace Attacks: Security Risks and Countermeasures." Chinese Journal of Engineering Science 23, no. 3 (2021): 60. http://dx.doi.org/10.15302/j-sscae-2021.03.002.

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Chiranjeevi, G., R. Abhishek Reddy, R. Shyam, Swati Sah, and Rejwan Bin Sulaiman. "Docker Based Decentralized Vulnerability Assessment with Port Scanning Powered by Artificial Intelligence." FMDB Transactions on Sustainable Intelligent Networks 1, no. 4 (2024): 220–41. https://doi.org/10.69888/ftsin.2024.000290.

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Decentralized solutions, widely adopted across industries like banking, health- care, and logistics, face persistent security concerns from potential threats. This study introduces a novel decentralized vulnerability assessment using GPT-3, an artificial intelligence (AI) technology. Employing Dockerized containers for disinfecting environments and creating unique connections to the AI API service enhances system responsiveness. AI algorithms, specifically GPT-3, conduct comprehensive network scans to identify security flaws. Findings are securely distributed to network nodes, fortifying the s
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Han-Mei Liu. "AI-Enabled Adaptive Cybersecurity Response Using Reinforcement Learning." Frontiers in Artificial Intelligence Research 2, no. 1 (2025): 1–12. https://doi.org/10.71465/gwa30h81.

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Cyber threats are evolving in complexity and frequency, rendering traditional cybersecurity response mechanisms insufficient. Conventional rule-based and supervised machine learning (ML) models struggle to adapt to novel attack patterns, leaving security systems vulnerable to emerging threats. Reinforcement learning (RL) offers a promising approach to adaptive cybersecurity by enabling systems to learn optimal defense strategies through continuous interaction with adversarial environments. This study explores an RL-based cybersecurity response framework that dynamically adjusts mitigation stra
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Ayaz, Ferheen, Zhengguo Sheng, Daxin Tian, Maziar Nekovee, and Nagham Saeed. "Blockchain-Empowered AI for 6G-Enabled Internet of Vehicles." Electronics 11, no. 20 (2022): 3339. http://dx.doi.org/10.3390/electronics11203339.

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The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving
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Rishitha, Kokku. "Revolutionizing DevOps Security: AI and ML-Enabled Automated Testing Approaches." International Journal of Current Science Research and Review 07, no. 10 (2024): 7947–51. https://doi.org/10.5281/zenodo.13972658.

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Abstract : In modern DevOps environments, the integration of security practices poses significant challenges due to the fast-paced nature of Continuous Integration/Continuous Deployment (CI/CD) pipelines. Traditional security testing methods are usually too slow and reactive to address vulnerabilities effectively in such dynamic settings. To overcome these challenges, organizations are increasingly adopting automated security testing solutions that leverage Artificial Intelligence (AI) and Machine Learning (ML). This paper discusses AI and ML capabilities in automating security testing during
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Rishitha, Kokku. "Revolutionizing DevOps Security: AI and ML-Enabled Automated Testing Approaches." International Journal of Current Science Research and Review 07, no. 11 (2024): 8140–44. https://doi.org/10.5281/zenodo.14027398.

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Abstract : In modern DevOps environments, the integration of security practices poses significant challenges due to the fast-paced nature of Continuous Integration/Continuous Deployment (CI/CD) pipelines. Traditional security testing methods are usually too slow and reactive to address vulnerabilities effectively in such dynamic settings. To overcome these challenges, organizations are increasingly adopting automated security testing solutions that leverage Artificial Intelligence (AI) and Machine Learning (ML). This paper discusses AI and ML capabilities in automating security testing during
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Researcher. "BLOCKCHAIN-ENABLED AI GOVERNANCE FOR SCALABLE CLOUD SECURITY AUTOMATION." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 947–59. https://doi.org/10.5281/zenodo.13962366.

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This article presents an innovative blockchain-enabled AI governance framework for cloud security that addresses critical challenges in scalability, real-time adaptability, and regulatory compliance. The proposed solution integrates blockchain's immutable ledger with AI-driven automation to create a decentralized, transparent, and adaptive security infrastructure. This framework demonstrates significant improvements over traditional systems, including a 10x increase in transaction handling capacity, a 25% reduction in threat response time, and a 99.5% increase in regulatory compliance. Ex
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Gupta, Hari, and Vikhyat Gupta. "Data Privacy and Security in AI-Enabled Platforms: The Role of the Chief Infosec Officer." Stallion Journal for Multidisciplinary Associated Research Studies 3, no. 5 (2024): 191–214. https://doi.org/10.55544/sjmars.3.5.15.

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In the age of digital transformation, artificial intelligence (AI)-enabled platforms have become central to a wide array of industries, driving innovation, efficiency, and personalization. However, the integration of AI technologies introduces significant challenges related to data privacy and security, as sensitive information is increasingly processed and analyzed by autonomous systems. The Chief Information Security Officer (CISO) plays a crucial role in navigating these challenges, ensuring that the privacy of user data is maintained while safeguarding against potential cyber threats. This
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Kashyap, Gaurav. "AI in Blockchain-Enabled Healthcare Systems." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem17947.

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The integration of Artificial Intelligence (AI) and Blockchain technology offers transformative potential for the healthcare industry. Blockchain provides a secure, immutable, and transparent infrastructure for managing healthcare data, while AI enhances decision-making, predictive analytics, and personalized treatment. This research paper explores the synergies between AI and Blockchain in healthcare systems, focusing on how these technologies can improve patient care, streamline operations, enhance data security, and address key challenges like interoperability and trust. Through case studie
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Dissertations / Theses on the topic "AI-enabled security"

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KRAYANI, ALI. "Learning Self-Awareness Models for Physical Layer Security in Cognitive and AI-enabled Radios." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1074612.

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Cognitive Radio (CR) is a paradigm shift in wireless communications to resolve the spectrum scarcity issue with the ability to self-organize, self-plan and self-regulate. On the other hand, wireless devices that can learn from their environment can also be taught things by malicious elements of their environment, and hence, malicious attacks are a great concern in the CR, especially for physical layer security. This thesis introduces a data-driven Self-Awareness (SA) module in CR that can support the system to establish secure networks against various attacks from malicious users. Such users c
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SYED, MUHAMMAD FARRUKH SHAHID. "Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1048543.

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Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every field of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT surfers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Artificial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attac
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Lautenschläger, Jana Katharina. "The effect of knowledge about artificial intelligence (Ai) on openness towards ai-enabled products and services: examining whether customer beliefs about the efficiency, convenience, privacy protection and data security of ai-enabled products and services mediate this effect." Master's thesis, 2020. http://hdl.handle.net/10362/104102.

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Artificial Intelligence(“AI”)is a rapidly evolving technology and iscurrently the most promisingmarket opportunity in the worldeconomy. The study examines the relationship of customers knowledge aboutAI with theiropenness to interact with AI-enabled products/services. The author analyzes whether customer beliefs about efficiency, convenience, privacy protection and data security act as a mediator of this relationship. Analyzing primary data (n=331)throughregression models, the study suggests that thesignificantrelationship between knowledge and opennessispartially med
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Books on the topic "AI-enabled security"

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Karimipour, Hadis, and Farnaz Derakhshan, eds. AI-Enabled Threat Detection and Security Analysis for Industrial IoT. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76613-9.

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Karimipour, Hadis, and Farnaz Derakhshan. AI-Enabled Threat Detection and Security Analysis for Industrial IoT. Springer International Publishing AG, 2022.

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Karimipour, Hadis, and Farnaz Derakhshan. AI-Enabled Threat Detection and Security Analysis for Industrial IoT. Springer International Publishing AG, 2021.

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Lee, Cheng-Chi. Handbook of Security and Privacy of AI-Enabled Healthcare Systems and Internet of Medical Things. Taylor & Francis Group, 2023.

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Lee, Cheng-Chi. Handbook of Security and Privacy of AI-Enabled Healthcare Systems and Internet of Medical Things. Taylor & Francis Group, 2023.

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Lee, Cheng-Chi. Handbook of Security and Privacy of AI-Enabled Healthcare Systems and Internet of Medical Things. CRC Press LLC, 2023.

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Lee, Cheng-Chi. Handbook of Security and Privacy of AI-Enabled Healthcare Systems and Internet of Medical Things. Taylor & Francis Group, 2023.

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Book chapters on the topic "AI-enabled security"

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Strieder, Emanuele, Johanna Baehr, and Matthias Hiller. "AI-Enabled Hardware Security." In AI-Enabled Electronic Circuit and System Design. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-71436-8_9.

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Gupta, Sunil, Monit Kapoor, and Sanjoy Kumar Debnath. "Future Directions for AI-Enabled Healthcare Security." In Artificial Intelligence-Enabled Security for Healthcare Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82810-2_9.

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Sachdeva, Ritu, and Shilpa Bhatia. "Security and Privacy in Edge AI." In Edge Computational Intelligence for AI-Enabled IoT Systems. CRC Press, 2024. http://dx.doi.org/10.1201/9781032650722-6.

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Gupta, Sunil, Monit Kapoor, and Sanjoy Kumar Debnath. "Best Practices for Implementing AI-Enabled Healthcare Security." In Artificial Intelligence-Enabled Security for Healthcare Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82810-2_7.

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Gupta, Sunil, Monit Kapoor, and Sanjoy Kumar Debnath. "Challenges and Risks of AI-Enabled Healthcare Security." In Artificial Intelligence-Enabled Security for Healthcare Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82810-2_6.

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Sayadi, Hossein, and Zhangying He. "Adapt and Defend: Reinforcement Learning for Hardware-Assisted Security." In AI-Enabled Electronic Circuit and System Design. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-71436-8_12.

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Rouzbahani, Hossein Mohammadi, Zahra Faraji, Mohammad Amiri-Zarandi, and Hadis Karimipour. "AI-Enabled Security Monitoring in Smart Cyber Physical Grids." In Security of Cyber-Physical Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-45541-5_8.

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Gupta, Sunil, Monit Kapoor, and Sanjoy Kumar Debnath. "Impact of AI-Enabled Healthcare Security on Patient Outcomes." In Artificial Intelligence-Enabled Security for Healthcare Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-82810-2_8.

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Dasari, Venkata Lakshmi, Ragini Mokkapati, Kotha Lavanya, and Gudditi Chetan. "Protecting AI-Enabled Industrial Engineering in Cloud and Edge Environments." In Industrial Internet of Things Security. CRC Press, 2024. http://dx.doi.org/10.1201/9781003466284-1.

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Yogi, Manas Kumar, and A. S. N. Chakravarthy. "Novel Security Mechanism for AI Enabled Wastewater Treatment Systems." In Springer Water. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-67237-8_12.

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Conference papers on the topic "AI-enabled security"

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Harguess, Josh, and Chris M. Ward. "Offensive security for AI systems: concepts, practices, and applications." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3055959.

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Kapusta, Ariel, David Jin, Peter Teague, et al. "A framework for the assurance of AI-enabled systems." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3056719.

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Verma, Dinesh C., and Radha Ratnaparkhi. "Guardrails for safe implementations of AI-based services." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3051891.

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Aiyagari, Sanjay, Elisa Bertino, Jan Bieniek, et al. "Security design for NLIP: a universal protocol for AI-enabled systems." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3055287.

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Bastian, Nathaniel D. "Operational AI red-teaming: gaps and opportunities in the DoD." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3068030.

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Verma, Dinesh C., Pawan Chowdhary, David Beymer, Swanand R. Kadhe, Jon Lenchner, and Shiqiang Wang. "Patterns for combining large language models with knowledge bases to improve assurance, performance, and reliability of AI solutions." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3054792.

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Manzonelli, Nico A., Sean M. Coffey, and Nathaniel D. Bastian. "How private are your chat adapters? Evaluating the privacy of LoRA fine-tuned large language models with membership inference attacks." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3053265.

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Cybenko, George, Paul S. Lintilhac, Joshua M. Ackerman, and Nathaniel D. Bastian. "Quantifying adversarial risk of multimodal foundation models for military applications." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3052788.

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Jagatheesaperumal, Senthil Kumar, Dhanupriyan Pothiraja, Satheesh Kumaresan, Mohamed Rahouti, Hamza Drid, and Khaled Hamouid. "Authentic key agreement scheme for blockchain-based smart grid applications." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3054791.

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Ward, Chris M., and Josh Harguess. "Adversarial threat vectors and risk mitigation for retrieval-augmented generation systems." In Assurance and Security for AI-enabled Systems 2025, edited by Joshua D. Harguess, Nathaniel D. Bastian, and Teresa L. Pace. SPIE, 2025. https://doi.org/10.1117/12.3055931.

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Reports on the topic "AI-enabled security"

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Gehlhaus, Diana. Staying Ahead: Strengthening Tomorrow's U.S. AI and AI-Enabled Workforce. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20210075.

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This research agenda provides a roadmap for the next phase of CSET’s line of research on the U.S. AI workforce. Our goal is to assist policymakers and other stakeholders in the national security community to create policies that will ensure the United States maintains its competitive advantage in AI talent. We welcome comments, feedback and input on this vision at cset@georgetown.edu.
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Hoffman, Wyatt. "Making AI Work for Cyber Defense: The Accuracy-Robustness Tradeoff ". Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/2021ca007.

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Artificial intelligence will play an increasingly important role in cyber defense, but vulnerabilities in AI systems call into question their reliability in the face of evolving offensive campaigns. Because securing AI systems can require trade-offs based on the types of threats, defenders are often caught in a constant balancing act. This report explores the challenges in AI security and their implications for deploying AI-enabled cyber defenses at scale.
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Williams, Anthony. Open Source Congress 2024: Shaping the Future of Collaboration in AI, Security, and Digital Public Goods. The Linux Foundation, 2024. https://doi.org/10.70828/mton6557.

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The 2024 Open Source Congress in Beijing, hosted by the OpenAtom Foundation, united leaders to address critical challenges in open source software (OSS). Building on the inaugural 2023 Geneva event, discussions centered on open source AI, cybersecurity, decentralized infrastructure, and global collaboration. Participants highlighted the necessity of enhanced OSS security, citing vulnerabilities in software supply chains and the rise of AI-enabled threats. Proposals included adopting Software Bill of Materials (SBOM) and pooling resources for monitoring and response. Additionally, participants
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Pasupuleti, Murali Krishna. Quantum Semiconductors for Scalable and Fault-Tolerant Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rr825.

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Abstract: Quantum semiconductors are revolutionizing computing by enabling scalable, fault-tolerant quantum processors that overcome the limitations of classical computing. As quantum technologies advance, superconducting qubits, silicon spin qubits, topological qubits, and hybrid quantum-classical architectures are emerging as key solutions for achieving high-fidelity quantum operations and long-term coherence. This research explores the materials, device engineering, and fabrication challenges associated with quantum semiconductors, focusing on quantum error correction, cryogenic control sys
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