Relevant bibliographies by topics / AI- and ML- driven security

Contents

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

Academic literature on the topic 'AI- and ML- driven security'

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

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Journal articles on the topic "AI- and ML- driven security"

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Bipin Gajbhiye, Anshika Aggarwal, and Shalu Jain. "Automated Security Testing in DevOps Environments Using AI and ML." International Journal for Research Publication and Seminar 15, no. 2 (2024): 259–71. http://dx.doi.org/10.36676/jrps.v15.i2.1472.

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The rapid adoption of DevOps practices has transformed the software development landscape by emphasizing continuous integration, continuous delivery (CI/CD), and agile methodologies. However, this rapid pace of development often introduces significant security challenges, as traditional security testing methods struggle to keep up with the accelerated release cycles. To address these challenges, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into automated security testing has emerged as a promising solution. This paper explores the use of AI and ML to enhance automa
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Oluwatosin Oladayo Aramide. "AI-Driven Cybersecurity in Storage Infrastructure." World Journal of Advanced Engineering Technology and Sciences 12, no. 2 (2024): 990–1001. https://doi.org/10.30574/wjaets.2024.12.2.0270.

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This paper sheds some light on how AI-powered cybersecurity can be applied to protecting storage infrastructures, namely, high-throughput NFS and S3 object stores. As data becomes more sensitive and volumes larger, conventional security is failing and perhaps the most vulnerable to this are AI/ML data. The research suggests taking into consideration the behavior-based threat identification, which reflects application to detection of ransomware, data exfiltration, insider threats, and others, prior to their evolvement. An AI can proactively identify anomalies by studying the activities and acti
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Gedam, Sahil Milind. "AI/ML-Driven Phishing Defence: Crafting a Robust Email Security Framework." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 1605–10. https://doi.org/10.61137/ijsret.vol.11.issue2.274.

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Reddy Bhimanapati, Vijay Bhasker, Shalu Jain, and Pandi Kirupa Gopalakrishna Pandian. "Security Testing for Mobile Applications Using AI and ML Algorithms." Journal of Quantum Science and Technology 1, no. 2 (2024): 44–58. http://dx.doi.org/10.36676/jqst.v1.i2.15.

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Mobile apps have revolutionized the digital world, making mobile devices essential to billions of users' everyday lives. This growth in mobile use has also increased security concerns to mobile apps, from data breaches to malicious software assaults. Traditional security testing methodologies, although useful, sometimes fail to address these attackers' sophistication and evolution. This study examines the use of AI and ML algorithms in mobile application security testing to improve vulnerability discovery, analysis, and mitigation.AI and ML algorithms use massive volumes of data and real-time
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Researcher. "INTEGRATING AI/ML INTO DEVSECOPS: STRENGTHENING SECURITY AND COMPLIANCE IN CLOUD-NATIVE APPLICATIONS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 5 (2024): 1128–48. https://doi.org/10.5281/zenodo.14043775.

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Cloud-native application security in contemporary enterprise settings can be revolutionized by incorporating Artificial Intelligence (AI) and Machine Learning (ML) into DevSecOps procedures. This thorough examination looks at how businesses may improve security testing automation, vulnerability identification, and compliance monitoring across the development lifecycle by utilizing AI/ML capabilities. Organizations report up to 76% fewer false positives, 88% higher threat detection accuracy, and 71% quicker vulnerability remediation, according to the study, which is based on substantial industr
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Pakalapati, Naveen, Jawaharbabu Jeyaraman, and Sai Mani Krishna Sistla. "Building Resilient Systems: Leveraging AI/ML within DevSecOps Frameworks." Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online) 2, no. 2 (2023): 213–30. http://dx.doi.org/10.60087/jklst.vol2.n2.p230.

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This paper explores the integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques within DevSecOps frameworks to enhance system resilience. In today's dynamic and rapidly evolving technological landscape, resilience has become a critical aspect of software development and operations. DevSecOps, an evolution of the DevOps methodology, emphasizes the importance of integrating security practices throughout the software development lifecycle. By leveraging AI/ML capabilities within DevSecOps frameworks, organizations can proactively identify and mitigate security threats, op
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Cedrick Agorbia-Atta, Imande Atalor, Rita Korkor Agyei, and Richard Nachinaba. "Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1487–97. http://dx.doi.org/10.30574/wjarr.2024.23.3.2788.

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In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and
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Cedrick, Agorbia-Atta, Atalor Imande, Korkor Agyei Rita, and Nachinaba Richard. "Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management." World Journal of Advanced Research and Reviews 23, no. 3 (2024): 1487–97. https://doi.org/10.5281/zenodo.14945549.

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In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and
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Rutvij, Shah, Puthraya Karthik, and Paul Josson. "AI-Driven Threat Intelligence Systems: Predictive Cybersecurity Models for Adaptive IT Defense Mechanisms." Global Journal of Engineering and Technology [GJET] 4, no. 2 (2025): 13–15. https://doi.org/10.5281/zenodo.14964173.

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<em>In the rapidly evolving digital landscape, cyber threats have become increasingly sophisticated, necessitating advanced threat intelligence systems. Artificial Intelligence (AI) has emerged as a pivotal technology in cybersecurity, enabling predictive models that enhance adaptive IT defense mechanisms. This paper explores AI-driven threat intelligence systems, detailing their architecture, methodologies, and applications in mitigating cyber threats. We discuss machine learning (ML) and deep learning (DL) models in predictive cybersecurity, real-time threat detection, and automated response
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Researcher. "HUMAN-AI COLLABORATION IN FOOD MANUFACTURING: ENHANCING DATA SECURITY AND COMPLIANCE." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 703–14. https://doi.org/10.5281/zenodo.13364386.

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This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) technologies on the food manufacturing industry, focusing on the synergistic relationship between human expertise and AI-driven solutions. It examines key areas where this collaboration drives significant improvements, including supply chain security, predictive maintenance, regulatory compliance, data security, and continuous improvement processes. Integrating AI and ML technologies offers unprecedented capabilities in data analysis, predictive modeling, and process optimization, enabling
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Dissertations / Theses on the topic "AI- and ML- driven security"

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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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TOMA, ANDREA. "PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1003576.

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Recently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks. In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with m
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Shrivastwa, Ritu Ranjan. "Enhancements in Embedded Systems Security using Machine Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT051.

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La liste des appareils connectés (ou IoT) s’allonge avec le temps, de même que leur vulnérabilité face aux attaques ciblées provenant du réseau ou de l’accès physique, communément appelées attaques Cyber Physique (CPS). Alors que les capteurs visant à détecter les attaques, et les techniques d’obscurcissement existent pour contrecarrer et améliorer la sécurité, il est possible de contourner ces contre-mesures avec des équipements et des méthodologies d’attaque sophistiqués, comme le montre la littérature récente. De plus, la conception des systèmes intégrés est soumise aux contraintes de compl
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Kaplan, Caelin. "Compromis inhérents à l'apprentissage automatique préservant la confidentialité." Electronic Thesis or Diss., Université Côte d'Azur, 2024. http://www.theses.fr/2024COAZ4045.

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À mesure que les modèles d'apprentissage automatique (ML) sont de plus en plus intégrés dans un large éventail d'applications, il devient plus important que jamais de garantir la confidentialité des données des individus. Cependant, les techniques actuelles entraînent souvent une perte d'utilité et peuvent affecter des facteurs comme l'équité et l'interprétabilité. Cette thèse vise à approfondir la compréhension des compromis dans trois techniques de ML respectueuses de la vie privée : la confidentialité différentielle, les défenses empiriques, et l'apprentissage fédéré, et à proposer des méth
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Ayoubi, Solayman. "Évaluation orientée données des systèmes de détection d’intrusion dans les réseaux." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS079.

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Les systèmes de détection d'intrusions (IDS) sont des composants critiques pour sécuriser les réseaux de communication modernes, en particulier à mesure que les menaces cybernétiques deviennent plus complexes. Cependant, les méthodologies d'évaluation existantes pour les IDS basés sur l'apprentissage automatique manquent de standardisation, ce qui entraîne des évaluations incomplètes et peu fiables. Les approches d'évaluation antérieures négligent souvent les bonnes pratiques en matière d'apprentissage automatique, se concentrant principalement sur les performances dans des jeux de données spé
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Books on the topic "AI- and ML- driven security"

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Abouraia, Mahmoud. Risks and Challenges of AI-Driven Finance: Bias, Ethics, and Security. IGI Global, 2024.

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Abouraia, Mahmoud. Risks and Challenges of AI-Driven Finance: Bias, Ethics, and Security. IGI Global, 2024.

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Abouraia, Mahmoud. Risks and Challenges of AI-Driven Finance: Bias, Ethics, and Security. IGI Global, 2024.

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Abouraia, Mahmoud. Risks and Challenges of AI-Driven Finance: Bias, Ethics, and Security. IGI Global, 2024.

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Zhai, Xiaoming, and Joseph Krajcik, eds. Uses of Artificial Intelligence in STEM Education. Oxford University PressOxford, 2024. http://dx.doi.org/10.1093/oso/9780198882077.001.0001.

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Abstract In the age of rapid technological advancements, the integration of artificial intelligence (AI), machine learning (ML), and large language models (LLMs) in science, technology, engineering, and mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. This book, comprising twenty-six chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technologica
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Book chapters on the topic "AI- and ML- driven security"

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Kläs, Michael, and Lisa Jöckel. "A Framework for Building Uncertainty Wrappers for AI/ML-Based Data-Driven Components." In Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55583-2_23.

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Moid, Abdul, and Narendra Sharma. "Investigation on Existing Blockchain Based Architecture, AI/ML Driven for Boosting IoT Security and Privacy." In Communications in Computer and Information Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-75167-7_20.

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Çag̃layan, Mehmet Ufuk. "AI and Quality of Service Driven Attack Detection, Mitigation and Energy Optimization: A Review of Some EU Project Results." In Communications in Computer and Information Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09357-9_1.

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AbstractThis article summarizes briefly the contributions presented in this EuroCyberSecurity Workshop 2021 which is organized as part of the series of International Symposia on Computer and Information Sciences (ISCIS), with the support of the European Commission funded IoTAC Project, that was held on November and in NIce, France, and sponsored by the Institute of Teoretical and Applied Informatics of the Polish Academy of Sciences. It also summarizes some of the research contributions of several EU Projects including NEMESYS, GHOST, KONFIDO, SDK4ED and IoTAC, primarily with a cybersecurity and Machine Learning orientation. Thus subjects covered include the cybersecurity of Mobile Networks and of the Internet of Things (IoT), the design of IoT Gateways and their performance, the security of networked health systems that provide health services to individuals across the EU Member states, as well as the issues of energy consumption by ICT which are becoming increasingly important, including in the cybersecurity perspective, as we focus increasingly on climate change and the needed transition towards highly reduced emissions. Many of the techniques and results discussed in this article are based either on Machine Learning (ML) methods, or on methods for the performance modeling and optimization of networked and distributed computer systems.
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Akkari, Nadine. "Roadmap for Enabling a Sustainable Development of 6G in Saudi Arabia." In Proceedings in Technology Transfer. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-8588-9_13.

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Abstract Vision 2030 emphasizes the importance of digital transformation, innovation, and sustainability as key drivers of economic and social progress. Building a sustainable 6G is a key element of a sustainable digital infrastructure. Saudi Arabia acknowledges the transformative potential of 6G technology and its impact on evolving sectors such as telecommunications, healthcare, transportation, and smart cities. Toward this end, this paper will study the current status of 6G technology in Saudi Arabia and related factors to develop a sustainable 6G. In the light of the fact that the country is prioritizing privacy and security when designing 6G networks, challenges and limitations for 6G sustainability will be considered in terms of sustainable development goals set by Vision 2030 and the UN Sustainable Development Goals (SDGs). In this context, sustainability requirements will be addressed from two dimensions: The first one is 6G infrastructure design where sustainability requirements are analyzed in terms of energy optimization, AI/ML integration, network slicing, security and privacy, renewable energy sources, and optimal resource utilization. The second one is the economic growth driven by 6G networks and ecosystems where sustainability requirements are analyzed in terms of energy consumption, resource utilization, waste generation and quality of life. The analysis of 6G infrastructure requirements and economic growth led by various 6G-based industries promoting efficient and sustainable practices resulted in a roadmap providing future directions for a sustainable 6G in Saudi Arabia.
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Guha, Krishnendu, Jyoti Prakash Singh, and Amlan Chakrabarti. "AI/ML in Cyber-Security." In Cybersecurity for Reconfigurable Hardware Based Critical Infrastructures. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-67591-1_8.

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Sendas, Neel, and Deepali Rajale. "MLOps Security in AI/ML." In The Definitive Guide to Machine Learning Operations in AWS. Apress, 2024. https://doi.org/10.1007/979-8-8688-1076-3_4.

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Pathak, Sandeep. "Explainable AI for ML Ops." In Studies in Autonomic, Data-driven and Industrial Computing. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5689-8_12.

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Gupta, Pramod, Naresh Kumar Sehgal, and John M. Acken. "Hardware Based AI and ML." In Introduction to Machine Learning with Security. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59170-9_7.

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Ashena, Rahman, Elliot H. Kurnia, and Terrence Josiah. "Early Kick Prediction by Data-Driven AI/ML Techniques." In The Practical Handbook of Well Control. CRC Press, 2025. https://doi.org/10.1201/9781003473770-13.

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Ngoc, Tran Thi Hong, Phan Truong Khanh, and Sabyasachi Pramanik. "AI-Driven Solution Selection." In Advances in Media, Entertainment, and the Arts. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-0639-0.ch007.

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With the fast growth of aquatic data, machine learning is essential for data analysis, categorization, and prediction. Data-driven models using machine learning may effectively handle complicated nonlinear problems in water research, unlike conventional approaches. Machine learning models and findings have been used to build, monitor, simulate, evaluate, and optimize water treatment and management systems in water environment research. Machine learning may also enhance water quality, pollution control, and watershed ecosystem security. This chapter discusses how ML approaches were used to assess water quality in surface, ground, drinking, sewage, and ocean. The authors also suggest potential machine learning applications in aquatic situations.
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Conference papers on the topic "AI- and ML- driven security"

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Malik, Abeera, Faisal Bukhari, and Hafiz Muhammad Talha. "AI Driven Blockchain Reward Insights Using ML." In 2024 3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE). IEEE, 2024. https://doi.org/10.1109/etecte63967.2024.10823911.

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ur Rehman, Hidayat, Zunera Jalil, and Safa Fahim. "AI-Driven APT Detection Framework: Early Threat Identification Using ML." In 2024 21st International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2024. https://doi.org/10.1109/ibcast61650.2024.10877259.

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Karaçay, Leyli, Ahmet Cihat Baktir, Ramin Fuladi, Elham Dehghan Biyar, Ömer Faruk Tuna, and Ipek Arikan. "Secure AI/ML-Based Control in Intent-Based Management System." In 2024 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2024. http://dx.doi.org/10.1109/csr61664.2024.10679495.

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Vacariu, Andrei-Nicolae, Marian Bucos, Marius Otesteanu, and Bogdan Dragulescu. "Automated Detection of AI-Generated Text Using LLM Embedding-Driven ML Models." In 2024 International Symposium on Electronics and Telecommunications (ISETC). IEEE, 2024. https://doi.org/10.1109/isetc63109.2024.10797258.

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Shah, Babar, Muhammad Junaid, and Mohammad Habib. "Enhancing IoT Protocol Security Through AI and ML: A Comprehensive Analysis." In 2024 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2024. http://dx.doi.org/10.1109/isncc62547.2024.10758967.

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Jare, Akash, Sanket Kolte, Sonam Kadam, Vineet Babar, Pallavi Tekade, and Dipmala Salunke. "DefendNet: Harnessing AI/ML for Dynamic DNS Filtering and Network Security." In 2024 IEEE International Conference on Blockchain and Distributed Systems Security (ICBDS). IEEE, 2024. https://doi.org/10.1109/icbds61829.2024.10837330.

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V, Sumithra, Shahsidhara R, and Akansha Singh. "Automating Security in Blockchain: ML-Driven Smart Contract Vulnerability Analysis." In 2025 3rd International Conference on Disruptive Technologies (ICDT). IEEE, 2025. https://doi.org/10.1109/icdt63985.2025.10986490.

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Singh, Kamred Udham, Ankit Kumar, Neeraj Varshney, Linesh Raja, Teekam Singh, and Amit Kumar Dewangan. "AI-Driven Security Enhancements in Wireless Sensor Networks." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649103.

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Jewanth, Sirigiri, Chittela Tarun Reddy, Ravi Rastogi, Billipalli Hemanth Reddy, and Nakka Bhanu Prakash Reddy. "AI-Driven Anomaly Detection in Cloud Network Security." In 2024 International Conference on Advances in Computing, Communication and Materials (ICACCM). IEEE, 2024. https://doi.org/10.1109/icaccm61117.2024.11059043.

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Elnawawy, Mohammed, Mohammadreza Hallajiyan, Gargi Mitra, Shahrear Iqbal, and Karthik Pattabiraman. "Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems." In 2024 IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE, 2024. http://dx.doi.org/10.1109/chase60773.2024.00019.

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Reports on the topic "AI- and ML- driven security"

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Pasupuleti, Murali Krishna. Quantum Intelligence: Machine Learning Algorithms for Secure Quantum Networks. National Education Services, 2025. https://doi.org/10.62311/nesx/rr925.

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Abstract: As quantum computing and quantum communication technologies advance, securing quantum networks against emerging cyber threats has become a critical challenge. Traditional cryptographic methods are vulnerable to quantum attacks, necessitating the development of AI-driven security solutions. This research explores the integration of machine learning (ML) algorithms with quantum cryptographic frameworks to enhance Quantum Key Distribution (QKD), post-quantum cryptography (PQC), and real-time threat detection. AI-powered quantum security mechanisms, including neural network-based quantum
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Lewis, Daniel, and Josh Oxby. Energy security and AI. Parliamentary Office of Science and Technology, 2024. https://doi.org/10.58248/pn735.

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This POSTnote summarises the current and emerging applications of AI and ML in the energy system, barriers to wider implementation, the challenges likely to be encountered, and policy considerations proposed by sector stakeholders.
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Zhu, Qing, William Riley, and James Randerson. Improve wildfire predictability driven by extreme water cycle with interpretable physically-guided ML/AI. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769720.

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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JOSI, Editor. Artificial Intelligence and Machine Learning: Transforming Industrial Optimization. Industrial Engineering Department, Faculty of Engineering, Universitas Andalas, 2025. https://doi.org/10.25077/03032025.

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The integration of artificial intelligence (AI) and machine learning (ML) into industrial systems is rapidly reshaping the way industries operate, optimize, and innovate. As industries grow more complex and data-driven, the ability to harness AI and ML technologies offers unprecedented efficiency, precision, and adaptability. In the pursuit of optimization, these technologies are no longer just experimental tools but have become essential drivers of transformation.
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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,
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Pasupuleti, Murali Krishna. AI and Quantum-Nano Frontiers: Innovations in Health, Sustainability, Energy, and Security. National Education Services, 2025. https://doi.org/10.62311/nesx/rr525.

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Abstract: This research report explores transformative advancements at the intersection of Artificial Intelligence (AI), Quantum Computing, and Nanotechnology, focusing on breakthrough innovations in health, sustainability, energy, and global security. By integrating quantum algorithms, AI-driven analytics, and advanced nanomaterials, this report highlights revolutionary solutions in precision medicine, predictive diagnostics, sustainable energy storage, universal water purification, and cybersecurity. Real-world case studies and emerging technologies such as graphene-based nanomaterials, quan
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Maerz, Seraphine. Using AI for Text Analysis: Advanced Applications. Instats Inc., 2024. https://doi.org/10.61700/nqumt3vu2zq3k1816.

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This one-day workshop provides an advanced exploration of AI-driven text analysis, focusing on the application and fine-tuning of open-source large language models to enhance research across various academic disciplines. Participants will gain practical skills in setting up local environments and employing sophisticated AI methodologies, empowering them to conduct independent and impactful text analysis while ensuring data security and privacy.
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Pasupuleti, Murali Krishna. Augmented Human Intelligence: Converging Generative AI, Quantum Computing, and XR for Enhanced Human-Machine Synergy. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv525.

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Abstract: Augmented Human Intelligence (AHI) represents a paradigm shift in human-AI collaboration, leveraging Generative AI, Quantum Computing, and Extended Reality (XR) to enhance cognitive capabilities, decision-making, and immersive interactions. Generative AI enables real-time knowledge augmentation, automated creativity, and adaptive learning, while Quantum Computing accelerates AI optimization, pattern recognition, and complex problem-solving. XR technologies provide intuitive, immersive environments for AI-driven collaboration, bridging the gap between digital and physical experiences.
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