Relevant bibliographies by topics / AI security

Contents

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

Academic literature on the topic 'AI security'

Author: Grafiati
Published: 11 January 2025
Last updated: 31 July 2025

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Journal articles on the topic "AI security"

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Yogesh Kumar Bhardwaj. "Securing Generative AI: Navigating Data Security Challenges in the AI Era." Journal of Computer Science and Technology Studies 7, no. 4 (2025): 147–55. https://doi.org/10.32996/jcsts.2025.7.4.17.

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This article examines the evolving security landscape for Generative Artificial Intelligence (GenAI) systems as they become increasingly integrated across critical sectors including healthcare, finance, and transportation. The proliferation of these technologies creates both transformative opportunities and significant security challenges that require specialized approaches. It explores key security vulnerabilities unique to GenAI implementations, including data protection vulnerabilities, access control complexities, data anonymization gaps, model integrity risks, monitoring challenges, intel
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Poongodi, R. K. "Zero Trust AI Authentication and Blockchain Powered Secure AI." International Journal for Research in Applied Science and Engineering Technology 13, no. 2 (2025): 52–56. https://doi.org/10.22214/ijraset.2025.66800.

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The integration of Zero Trust (ZT) security models with Artificial Intelligence (AI) authentication mechanisms, along with the utilization of blockchain technology, offers a novel paradigm for securing digital interactions and data exchanges in increasingly decentralized and complex networks. Zero Trust, a security framework that assumes no implicit trust and enforces strict identity verification, is well-suited for AI-driven authentication systems that require robust, real-time, and adaptive security measures Blockchain technology further enhances this framework by providing a transparent, im
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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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Chen, Hsinchun. "AI and Security Informatics." IEEE Intelligent Systems 25, no. 5 (2010): 82–90. http://dx.doi.org/10.1109/mis.2010.116.

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Agrawal, Jatin, Samarjeet Singh Kalra, and Himanshu Gidwani. "AI in cyber security." International Journal of Communication and Information Technology 4, no. 1 (2023): 46–53. http://dx.doi.org/10.33545/2707661x.2023.v4.i1a.59.

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BS, Guru Prasad, Dr Kiran GM, and Dr Dinesha HA. "AI-Driven cyber security: Security intelligence modelling." International Journal of Multidisciplinary Research and Growth Evaluation 4, no. 6 (2023): 961–65. http://dx.doi.org/10.54660/.ijmrge.2023.4.6.961-965.

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The process of defending computer networks from cyber attacks or unintended, unauthorized access is known as cyber security. Organizations, businesses, and governments need cyber security solutions because cyber criminals pose a threat to everyone. Artificial intelligence promises to be a great solution for this. Security experts are better able to defend vulnerable networks and data from cyber attackers by combining the strengths of artificial intelligence and cyber security. This paper provides an introduction to the use of artificial intelligence in cyber security. AI-driven cyber security
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Abudalou, Mohammad Ali. "Security DevOps: Enhancing Application Delivery with Speed and Security." International Journal of Computer Science and Mobile Computing 13, no. 5 (2024): 100–104. http://dx.doi.org/10.47760/ijcsmc.2024.v13i05.009.

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"Protection DevOps: Security DevOps: Enhancing Application Delivery with Speed and Security" is a whole paper that explores the combination of synthetic intelligence (AI) technology into the protection DevOps framework. This integration goals to enhance software transport with the aid of AI-driven automation, predictive analytics, and chance intelligence. Inside the contemporary, rapid-paced virtual panorama, groups face developing pressure to deliver applications quickly while making sure sturdy safety capabilities are in proximity. The traditional method of protection frequently results in d
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Poonia, Ramesh Chandra. "Securing the Sustainable Future : Cryptography and Security in AI & IoT." Journal of Discrete Mathematical Sciences and Cryptography 27, no. 4 (2024): i—vii. http://dx.doi.org/10.47974/jdmsc-27-4-foreword.

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The objective of this special issue titled “Securing the Sustainable Future: Cryptography and Security in AI & IoT” is to discuss recent advancements of cryptography and security in AI & IoT for a secure, sustainable future. The issue focuses on the social and economic impact of sustainable computing, with sub-topics covering the applications of AI & IoT. Additionally, the conference has explored security mechanisms in the context of sustainability.
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Gudimetla, Sandeep Reddy, and Niranjan Reddy Kotha. "AI-POWERED THREAT DETECTION IN CLOUD ENVIRONMENTS." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, no. 1 (2018): 638–42. http://dx.doi.org/10.61841/turcomat.v9i1.14730.

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This study assesses the effectiveness of artificial intelligence (AI) technologies in enhancing threat detection within cloud environments, a critical component given the escalating security challenges in cloud computing. Leveraging various AI methodologies, including machine learning models, deep learning, and anomaly detection techniques, the research aims to improve the accuracy and efficiency of security systems. These AI methods were applied to a series of simulated threat scenarios across diverse cloud platforms to evaluate their capability in real-time threat identification and mitigati
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Sohaib uz Zaman, Erum Parveen, and Syed Hasnain Alam. "AI to Enhance the Transactional Security in Digital Banking." Journal of Management & Social Science 2, no. 2 (2025): 1–22. https://doi.org/10.63075/2apb3z22.

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The integration of artificial intelligence (AI) has enhanced banking operations by providing improved customer service, security, and efficiency. For data privacy and security, Banks must implement robust data security measures to gain customer trust as well as comply with regulatory requirements. Therefore, this study examined whether AI-based security systems can augment transactional safety, secure pay systems, and strengthen customer trust in digital banks. Quantitative research design and stratified random sampling technique is used for data collection. Questionnaire is filled up with dig
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Dissertations / Theses on the topic "AI security"

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Djaidja, Taki Eddine Toufik. "Advancing the Security of 5G and Beyond Vehicular Networks through AI/DL." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCK009.

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L'émergence des réseaux de cinquième génération (5G) et des réseaux véhiculaire (V2X) a ouvert une ère de connectivité et de services associés sans précédent. Ces réseaux permettent des interactions fluides entre les véhicules, l'infrastructure, et bien plus encore, en fournissant une gamme de services à travers des tranches de réseau (slices), chacune adaptée aux besoins spécifiques de ceux-ci. Les générations futures sont même censées apporter de nouvelles avancées à ces réseaux. Cependant, ce progrès remarquable les expose à une multitude de menaces en matière de cybersécurité, dont bon nom
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Hatoum, Makram. "Digital watermarking for PDF documents and images : security, robustness and AI-based attack." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCD016.

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Le développement technologique a ses avantages et ses inconvénients. Nous pouvons facilement partager et télécharger du contenu numérique en utilisant l’Internet. En outre, les utilisateurs malveillants peuvent aussi modifier, dupliquer et diffuser illégalement tout type d'informations, comme des images et des documents. Par conséquent, nous devons protéger ces contenus et arrêter les pirates. Le but de cette thèse est de protéger les documents PDF et les images en utilisant la technique de tatouage numérique Spread Transform Dither Modulation (STDM), tout en tenant compte des exigences princi
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Radosavljevic, Bojan, and Axel Kimblad. "Etik och säkerhet när AI möter IoT." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20613.

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I dagens samhälle går den tekniska utvecklingen fort framåt. Artificiell intelligens och Internet of Things är två tekniker inom utvecklingen vars popularitet har ökat på senare tid. Dessa tekniker i integration har visat sig kunna bidra med stora verksamhetsnyttor, bland annat i form av ökad precishet vad gäller analyser, bättre kundvärde och effektivisering av ”downtime”. Med ny teknik kommer även utmaningar. I takt med att teknologierna ständigt växer uppstår frågor kring säkerhet och etik och hur detta ska hanteras. Målet med denna studien var att ta reda på hur experter värderar etiska
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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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Ranang, Martin Thorsen. "An Artificial Immune System Approach to Preserving Security in Computer Networks." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-255.

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<p>It is believed that many of the mechanisms present in the biological immune system are well suited for adoption to the field of computer intrusion detection, in the form of artificial immune systems. In this report mechanisms in the biological immune system are introduced, their parallels in artificial immune systems are presented, and how they may be applied to intrusion detection in a computer environment is discussed. An artificial immune system is designed, implemented and applied to detect intrusive behavior in real network data in a simulated network environment. The effect of costimu
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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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Musgrave, John. "Cognitive Malice Representation and Identification." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1565348664149804.

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Zhang, Yueqian. "Resource Clogging Attacks in Mobile Crowd-Sensing: AI-based Modeling, Detection and Mitigation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/40082.

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Mobile Crowdsensing (MCS) has emerged as a ubiquitous solution for data collection from embedded sensors of the smart devices to improve the sensing capacity and reduce the sensing costs in large regions. Due to the ubiquitous nature of MCS, smart devices require cyber protection against adversaries that are becoming smarter with the objective of clogging the resources and spreading misinformation in such a non-dedicated sensing environment. In an MCS setting, one of the various adversary types has the primary goal of keeping participant devices occupied by submitting fake/illegitimate sensing
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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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SIGNORI, ROBERTA. "POLIZIA PENITENZIARIA E SORVEGLIANZA DINAMICA IN CARCERE Le risposte ai cambiamenti organizzativi e l’impatto sul benessere del personale." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/158284.

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Il sistema Penitenziario Italiano è attualmente interessato da profondi mutamenti organizzativi che riguardano, in particolar modo, le modalità operative del personale di polizia penitenziaria. L’introduzione della sorveglianza dinamica in carcere ha rappresentato un importante cambiamento organizzativo capace di ridefinire gli spazi, i tempi e le modalità di interazione all’interno delle sezioni detentive. Quest’ ultima fa riferimento ad una modalità operativa incentrata non più sul controllo statico della persona detenuta, ma piuttosto sulla conoscenza e l’osservazione della stessa. Nella me
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Books on the topic "AI security"

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Huang, Ken, Yang Wang, Ben Goertzel, Yale Li, Sean Wright, and Jyoti Ponnapalli, eds. Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7.

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Thakkar, Hiren Kumar, Mayank Swarnkar, and Robin Singh Bhadoria, eds. Predictive Data Security using AI. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6290-5.

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Whitlock, Chris, and Frank Strickland. Winning the National Security AI Competition. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-8814-6.

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Sehgal, Naresh Kumar, Manoj Saxena, and Dhaval N. Shah. AI on the Edge with Security. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-78272-5.

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Jaswal, Gaurav, Vivek Kanhangad, and Raghavendra Ramachandra, eds. AI and Deep Learning in Biometric Security. CRC Press, 2021. http://dx.doi.org/10.1201/9781003003489.

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Hewage, Chaminda, Liqaa Nawaf, and Nishtha Kesswani, eds. AI Applications in Cyber Security and Communication Networks. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3973-8.

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Filì, Valeria. Il reddito imponibile ai fini contributivi. G. Giappichelli, 2010.

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Kulkarni, Anand J., Patrick Siarry, Apoorva S. Shastri, and Mangal Singh. AI-Based Metaheuristics for Information Security and Digital Media. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003107767.

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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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Raj, Balwinder, Brij B. Gupta, Shingo Yamaguchi, and Sandeep Singh Gill. AI for Big Data-Based Engineering Applications from Security Perspectives. CRC Press, 2023. http://dx.doi.org/10.1201/9781003230113.

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Book chapters on the topic "AI security"

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Cagle, Anton, and Ahmed Ceifelnasr Ahmed. "Security." In Architecting Enterprise AI Applications. Apress, 2024. https://doi.org/10.1007/979-8-8688-0902-6_10.

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Huang, Ken, Aditi Joshi, Sandy Dun, and Nick Hamilton. "AI Regulations." In Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7_3.

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Sharma, Dilli Prasad, Arash Habibi Lashkari, Mahdi Daghmehchi Firoozjaei, Samaneh Mahdavifar, and Pulei Xiong. "AI in Security." In Progress in IS. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91524-6_3.

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Huang, Ken, Ben Goertzel, Daniel Wu, and Anita Xie. "GenAI Model Security." In Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7_6.

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Huang, Ken, Jerry Huang, and Daniele Catteddu. "GenAI Data Security." In Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7_5.

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Huang, Ken, Grace Huang, Adam Dawson, and Daniel Wu. "GenAI Application Level Security." In Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7_7.

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Taherdoost, Hamed. "AI for cyber security and cyber security for AI." In Artificial Intelligence for Cyber Security and Industry 4.0. CRC Press, 2025. https://doi.org/10.1201/9781032657264-1.

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Huang, Ken, Fan Zhang, Yale Li, Sean Wright, Vasan Kidambi, and Vishwas Manral. "Security and Privacy Concerns in ChatGPT." In Beyond AI. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45282-6_11.

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Huang, Ken, Yang Wang, and Xiaochen Zhang. "Foundations of Generative AI." In Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7_1.

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Huang, Ken, Grace Huang, Yuyan Duan, and Ju Hyun. "Utilizing Prompt Engineering to Operationalize Cybersecurity." In Generative AI Security. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54252-7_9.

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Conference papers on the topic "AI security"

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He, Yifeng, Ethan Wang, Yuyang Rong, Zifei Cheng, and Hao Chen. "Security of AI Agents." In 2025 IEEE/ACM International Workshop on Responsible AI Engineering (RAIE). IEEE, 2025. https://doi.org/10.1109/raie66699.2025.00013.

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Jabbar, Haidar, Samir Al-Janabi, and Francis Syms. "Securing Autonomous Vehicles with Smart AI Security Agents." In 2025 International Conference on New Trends in Computing Sciences (ICTCS). IEEE, 2025. https://doi.org/10.1109/ictcs65341.2025.10989346.

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Ünal, Hasan Tolga, Arif Furkan Mendi, Özgür Umut Vurgun, Ömer Özkan, and Mehmet Akif Nacar. "AI – Supported Collective Security System." In 2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP). IEEE, 2024. http://dx.doi.org/10.1109/idap64064.2024.10711160.

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van Oers, Alexander M., and Jorik T. Venema. "Anti-AI camouflage." In Artificial Intelligence for Security and Defence Applications II, edited by Henri Bouma, Yitzhak Yitzhaky, Radhakrishna Prabhu, and Hugo J. Kuijf. SPIE, 2024. http://dx.doi.org/10.1117/12.3031144.

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Kaplan, Hasan. "Systematic Testing of Security-Related Vulnerabilities in LLM-Based Applications." In 2025 IEEE/ACM 4th International Conference on AI Engineering – Software Engineering for AI (CAIN). IEEE, 2025. https://doi.org/10.1109/cain66642.2025.00043.

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Telrandhe, Paras, and Shivani Makhijani. "AI for Healthcare Data Security and Privacy." In 2024 2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIEI). IEEE, 2024. https://doi.org/10.1109/idicaiei61867.2024.10842836.

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Diyora, Vishal, and Nilesh Savani. "Blockchain or AI: Web Applications Security Mitigations." In 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT). IEEE, 2024. http://dx.doi.org/10.1109/ic2sdt62152.2024.10696861.

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Ambati, Sri Haritha, Norah Ridley, Enrico Branca, and Natalia Stakhanova. "Navigating (in)Security of AI-Generated Code." In 2024 IEEE International Conference on Cyber Security and Resilience (CSR). IEEE, 2024. http://dx.doi.org/10.1109/csr61664.2024.10679468.

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P, Sivakumar, Premkumar T, Ani Sowntharya A, Navin S, and Pavithra T. "Mastering Beaglebone AI & AI-64 on its Multifaceted Applications." In 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA). IEEE, 2024. https://doi.org/10.1109/icscsa64454.2024.00124.

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Van Ede, Thijs, Trix Mulder, and Evgeni Moyakine. "Interdisciplinary Research Project ‘AI Shield’." In 2024 8th Cyber Security in Networking Conference (CSNet). IEEE, 2024. https://doi.org/10.1109/csnet64211.2024.10851746.

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Reports on the topic "AI security"

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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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Christie, Lorna. AI in policing and security. Parliamentary Office of Science and Technology, 2021. http://dx.doi.org/10.58248/hs27.

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Sherman, Dylan, and Simon Brawley. AI, disinformation and cyber security. Parliamentary Office of Science and Technology, 2025. https://doi.org/10.58248/hs104.

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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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Bennet, Karen, Gopi Krishnan Rajbahadur, Arthit Suriyawongkul, and Kate Stewart. Implementing AI Bill of Materials (AI BOM) with SPDX 3.0: A Comprehensive Guide to Creating AI and Dataset Bill of Materials. The Linux Foundation, 2024. https://doi.org/10.70828/rned4427.

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A Software Bill of Materials (SBOM) is becoming an increasingly important tool in regulatory and technical spaces to introduce more transparency and security into a project's software supply chain. Artificial intelligence (AI) projects face unique challenges beyond the security of their software, and thus require a more expansive approach to a bill of materials. In this report, we introduce the concept of an AI-BOM, expanding on the SBOM to include the documentation of algorithms, data collection methods, frameworks and libraries, licensing information, and standard compliance.
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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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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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Kim, Kyungmee, and Boulanin Vincent. Artificial Intelligence for Climate Security: Possibilities and Challenges. Stockholm International Peace Research Institute, 2023. http://dx.doi.org/10.55163/qdse8934.

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Recent advances in artificial intelligence (AI)—largely based on machine learning—offer possibilities for addressing climate-related security risks. AI can, for example, make disaster early-warning systems and long-term climate hazard modelling more efficient, reducing the risk that the impacts of climate change will lead to insecurity and conflict. This SIPRI Policy Report outlines the opportunities that AI presents for managing climate-related security risks. It gives examples of the use of AI in the field and delves into the problems—notably methodological and ethical—associated with the us
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Murdick, Dewey, Daniel Chou, Ryan Fedasiuk, and Emily Weinstein. The Public AI Research Portfolio of China’s Security Forces. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200057.

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New analytic tools are used in this data brief to explore the public artificial intelligence (AI) research portfolio of China’s security forces. The methods contextualize Chinese-language scholarly papers that claim a direct working affiliation with components of the Ministry of Public Security, People's Armed Police Force, and People’s Liberation Army. The authors review potential uses of computer vision, robotics, natural language processing and general AI research.
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Murdick, Dewey, James Dunham, and Jennifer Melot. AI Definitions Affect Policymaking. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/20200004.

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The task of artificial intelligence policymaking is complex and challenging, made all the more difficult by such a rapidly evolving technology. In order to address the security and economic implications of AI, policymakers must be able to viably define, categorize and assess AI research and technology. In this issue brief, CSET puts forward a functional definition of AI, based on three core principles, that significantly outperforms methods developed over the last decade.
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