Relevant bibliographies by topics / Machine Learning in Security

Contents

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

Academic literature on the topic 'Machine Learning in Security'

Author: Grafiati
Published: 7 June 2025
Last updated: 17 July 2025

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Journal articles on the topic "Machine Learning in Security"

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Dubey, Harshita. "High Security Machine Learning Algorithm for Industrial IoT." International Journal of Science and Research (IJSR) 12, no. 4 (2023): 1794–99. http://dx.doi.org/10.21275/sr23428074556.

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Singh, Dr Sachin, Piyush Rastogi, Prabal Bhatnagar, RavishKr Dubey, Salman Siddique, and Mansi Pathak. "Enhancing Blockchain Security and Efficiency with Machine Learning." International Journal of Research Publication and Reviews 6, sp5 (2025): 224–30. https://doi.org/10.55248/gengpi.6.sp525.1930.

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Majjaru, Chandrababu, and Senthil Kumar K. Dr. "Proficient Machine Learning Techniques for a Secured Cloud Environment." International Journal of Engineering and Advanced Technology (IJEAT) 11, no. 6 (2022): 74–81. https://doi.org/10.35940/ijeat.F3730.0811622.

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<strong>Abstract: </strong>Many different checks, rules, processes, and technologies work together to keep cloud-based applications and infrastructure safe and secure against cyberattacks. Data security, customer privacy, regulatory enforcement, and device and user authentication regulations are all protected by these safety measures. Insecure Access Points, DDoS Attacks, Data Breach and Data Loss are the most pressing issues in cloud security. In the cloud computing context, researchers looked at several methods for detecting intrusions. Cloud security best practises such as host &amp; middle
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Baracaldo, Nathalie, and Alina Oprea. "Machine Learning Security and Privacy." IEEE Security & Privacy 20, no. 5 (2022): 11–13. http://dx.doi.org/10.1109/msec.2022.3188190.

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Suomalainen, Jani, Arto Juhola, Shahriar Shahabuddin, Aarne Mammela, and Ijaz Ahmad. "Machine Learning Threatens 5G Security." IEEE Access 8 (2020): 190822–42. http://dx.doi.org/10.1109/access.2020.3031966.

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Gupta, Tanishq. "Machine Learning in Cyber security." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40331.

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Data science powers cyber security advancements by using machine learning to detect patterns and build intelligent systems. It addresses issues like phishing, network intrusion, and spam detection. By analyzing data, extracting features, and training models, machine learning enhances security. Regular updates and combining models improve accuracy in detecting evolving cyber threats. Keywords— Security, Machine Learning, Survey, Machine Learning, Intrusion Detection, Spam Cyber security
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Mcgraw, Gary, Richie Bonett, Harold Figueroa, and Victor Shepardson. "Security Engineering for Machine Learning." Computer 52, no. 8 (2019): 54–57. http://dx.doi.org/10.1109/mc.2019.2909955.

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Hartmann, Mark. "Machine Learning und IT-Security." Datenschutz und Datensicherheit - DuD 42, no. 4 (2018): 231–35. http://dx.doi.org/10.1007/s11623-018-0913-5.

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Barreno, Marco, Blaine Nelson, Anthony D. Joseph, and J. D. Tygar. "The security of machine learning." Machine Learning 81, no. 2 (2010): 121–48. http://dx.doi.org/10.1007/s10994-010-5188-5.

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Ahmad, Asiyah. "Improving Distance Learning Security using Machine Learning." Journal of Computer Science Application and Engineering (JOSAPEN) 1, no. 2 (2023): 39–43. https://doi.org/10.70356/josapen.v1i2.13.

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This study explores the intersection of machine learning and distance learning security, aiming to fortify online educational platforms amidst the evolving digital landscape. With technological advancements fueling the rise of distance learning, concerns regarding cybersecurity in virtual educational environments have grown significantly. The fusion of machine learning and distance learning security represents a proactive approach to bolstering safety and integrity within virtual classrooms. Leveraging sophisticated algorithms, this amalgamation seeks to preempt security breaches by identifyin
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Dissertations / Theses on the topic "Machine Learning in Security"

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Stomeo, Carlo. "Applying Machine Learning to Cyber Security." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17303/.

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Intrusion Detection Systems (IDS) nowadays are a very important part of a system. In the last years many methods have been proposed to implement this kind of security measure against cyber attacks, including Machine Learning and Data Mining based. In this work we discuss in details the family of anomaly based IDSs, which are able to detect never seen attacks, paying particular attention to adherence to the FAIR principles. This principles include the Accessibility and the Reusability of software. Moreover, as the purpose of this work is the assessment of what is going on in the state of the ar
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Jan, Steve T. K. "Robustifying Machine Learning based Security Applications." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99862.

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In recent years, machine learning (ML) has been explored and employed in many fields. However, there are growing concerns about the robustness of machine learning models. These concerns are further amplified in security-critical applications — attackers can manipulate the inputs (i.e., adversarial examples) to cause machine learning models to make a mistake, and it's very challenging to obtain a large amount of attackers' data. These make applying machine learning in security-critical applications difficult. In this dissertation, we present several approaches to robustifying three machine lea
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DEMETRIO, LUCA. "Formalizing evasion attacks against machine learning security detectors." Doctoral thesis, Università degli studi di Genova, 2021. http://hdl.handle.net/11567/1035018.

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Recent work has shown that adversarial examples can bypass machine learning-based threat detectors relying on static analysis by applying minimal perturbations. To preserve malicious functionality, previous attacks either apply trivial manipulations (e.g. padding), potentially limiting their effectiveness, or require running computationally-demanding validation steps to discard adversarial variants that do not correctly execute in sandbox environments. While machine learning systems for detecting SQL injections have been proposed in the literature, no attacks have been tested against the pro
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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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Tian, Ke. "Learning-based Cyber Security Analysis and Binary Customization for Security." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/85013.

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This thesis presents machine-learning based malware detection and post-detection rewriting techniques for mobile and web security problems. In mobile malware detection, we focus on detecting repackaged mobile malware. We design and demonstrate an Android repackaged malware detection technique based on code heterogeneity analysis. In post-detection rewriting, we aim at enhancing app security with bytecode rewriting. We describe how flow- and sink-based risk prioritization improves the rewriting scalability. We build an interface prototype with natural language processing, in order to customize
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Pozdniakov, K. "A machine learning approach for smart computer security audit." Thesis, City, University of London, 2017. http://openaccess.city.ac.uk/19971/.

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This thesis presents a novel application of machine learning technology to automate network security audit and penetration testing processes in particular. A model-free reinforcement learning approach is presented. It is characterized by the absence of the environmental model. The model is derived autonomously by the audit system while acting in the tested computer network. The penetration testing process is specified as a Markov decision process (MDP) without definition of reward and transition functions for every state/action pair. The presented approach includes application of traditional a
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Grosse, Kathrin [Verfasser]. "Why is Machine Learning Security so hard? / Kathrin Grosse." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1237268818/34.

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Sorio, Enrico. "Machine Learning Techniques for Document Processing and Web Security." Doctoral thesis, Università degli studi di Trieste, 2013. http://hdl.handle.net/10077/8533.

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2011/2012<br>The task of extracting structured information from documents that are unstructured or whose structure is unknown is of uttermost importance in many application domains, e.g., office automation, knowledge management, machine-to-machine interactions. In practice, this information extraction task can be automated only to a very limited extent or subject to strong assumptions and constraints on the execution environment. In this thesis work I will present several novel application of machine learning techniques aimed at extending the scope and opportunities for automation of inform
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Davis, Jonathan J. "Machine learning and feature engineering for computer network security." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106914/1/Jonathan_Davis_Thesis.pdf.

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This thesis studies the application of machine learning to the field of Cyber security. Machine learning algorithms promise to enhance Cyber security by identifying malicious activity based only on provided examples. However, a major difficulty is the unsuitability of raw Cyber security data as input. In an attempt to address this problem, this thesis presents a framework for automatically constructing relevant features suitable for machine learning directly from network traffic. We then test the effectiveness of the framework by applying it to three Cyber security problems: HTTP tunnel detect
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Lounici, Sofiane. "Watermarking machine learning models." Electronic Thesis or Diss., Sorbonne université, 2022. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2022SORUS282.pdf.

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La protection de la propriété intellectuelle des modèles d’apprentissage automatique apparaît de plus en plus nécessaire, au vu des investissements et de leur impact sur la société. Dans cette thèse, nous proposons d’étudier le tatouage de modèles d’apprentissage automatique. Nous fournissons un état de l’art sur les techniques de tatouage actuelles, puis nous le complétons en considérant le tatouage de modèles au-delà des tâches de classification d’images. Nous définissons ensuite les attaques de contrefaçon contre le tatouage pour les plateformes d’hébergement de modèles, et nous présentons
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Books on the topic "Machine Learning in Security"

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Chen, Xiaofeng, Willy Susilo, and Elisa Bertino, eds. Cyber Security Meets Machine Learning. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6726-5.

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Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7.

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Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62460-6.

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Chen, Xiaofeng, Hongyang Yan, Qiben Yan, and Xiangliang Zhang, eds. Machine Learning for Cyber Security. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7.

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Chen, Xiaofeng, Xinyi Huang, and Jun Zhang, eds. Machine Learning for Cyber Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30619-9.

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Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20102-8.

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Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20096-0.

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Xu, Yuan, Hongyang Yan, Huang Teng, Jun Cai, and Jin Li, eds. Machine Learning for Cyber Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20099-1.

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Kim, Dan Dongseong, and Chao Chen, eds. Machine Learning for Cyber Security. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2458-1.

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Xiang, Yang, and Jian Shen, eds. Machine Learning for Cyber Security. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-4566-4.

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Book chapters on the topic "Machine Learning in Security"

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Salane, Douglas E. "Machine Learning." In Encyclopedia of Security and Emergency Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69891-5_14-1.

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Salane, Douglas E. "Machine Learning." In Encyclopedia of Security and Emergency Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-69891-5_14-2.

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Salane, Douglas E. "Machine Learning." In Encyclopedia of Security and Emergency Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-69891-5_14-3.

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Salane, Douglas E. "Machine Learning." In Encyclopedia of Security and Emergency Management. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-70488-3_14.

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Hernández-Castro, Carlos Javier, Zhuoran Liu, Alex Serban, Ilias Tsingenopoulos, and Wouter Joosen. "Adversarial Machine Learning." In Security and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98795-4_12.

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Möller, Dietmar P. F. "Machine Learning and Deep Learning." In Advances in Information Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26845-8_8.

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Kuznetsov, Phillip, Riley Edmunds, Ted Xiao, et al. "Adversarial Machine Learning." In Artificial Intelligence Safety and Security. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781351251389-17.

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Gupta, Pramod, Naresh Kumar Sehgal, and John M. Acken. "Machine Learning Concepts." In Introduction to Machine Learning with Security. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59170-9_1.

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Gupta, Pramod, Naresh Kumar Sehgal, and John M. Acken. "Machine Learning Operations." In Introduction to Machine Learning with Security. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59170-9_13.

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Gupta, Pramod, Naresh Kumar Sehgal, and John M. Acken. "Machine Learning Algorithms." In Introduction to Machine Learning with Security. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-59170-9_2.

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Conference papers on the topic "Machine Learning in Security"

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Attila, Nagy, Ács Szilvia, Horváth-Kiss Anikó, Fregan Beatrix, and Rajnai Zoltán. "Machine Learning Used in Cyber Security." In 2024 IEEE 18th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2024. http://dx.doi.org/10.1109/saci60582.2024.10619896.

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Patel, Anish, Mansi Bhavsar, and Kaushik Roy. "Enchanced CAV Security Using Machine Learning." In 2024 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE, 2024. http://dx.doi.org/10.1109/icabcd62167.2024.10645287.

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M, Saumya Y., Vinay P, Charis Pinto, Natasha Elizabeth Correia, Melanie Crystal Miranda, and Joyline Rencita Dsouza. "SmartDefend - IoT Security Using Machine Learning." In 2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2024. http://dx.doi.org/10.1109/discover62353.2024.10750744.

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Sarma, Nilotpola, E. Bhawani Eswar Reddy, and Chandan Karfa. "Security Concerns of Machine Learning Hardware." In 2024 IEEE 33rd Asian Test Symposium (ATS). IEEE, 2024. https://doi.org/10.1109/ats64447.2024.10915390.

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Bagade, Jayashree V., Sukhvinder Singh Dari, Rekha Dhivrani, Santosh Chowhan, G. Ravivarman, and V. Janakiraman. "Automation Through Machine Learning: Reinvigorating Cyber Security." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724667.

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Vigenesh, M., M. Shalini, P. Mandar, Inzimam Ul Hassan, Sukhvinder Singh Dari, and S. Annamalai. "Applying Machine Learning to Strengthening Cyber Security." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10723995.

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Amanulla, Syed Faisal, Vignesh T, Shaik Khadeer Basha, and Tiparani Manikanta Sai Pavan. "Advancements in Biometric Security: Machine Learning Approaches." In 2024 International Conference on IoT, Communication and Automation Technology (ICICAT). IEEE, 2024. https://doi.org/10.1109/icicat62666.2024.10922895.

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Tomasov, Adrian, Petr Dejdar, Petr Munster, and Tomas Horvath. "Utilizing a State of Polarization Change Detector and Machine Learning for Enhanced Security in Fiber-Optic Networks." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jtu2a.217.

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The paper presents a novel method for securing fiber-optic infrastructures using a state of polarization analyzer combined with machine learning algorithms. The proposed system detects vibrations indicative of security breaches, achieving an F1-score above 95.65 %.
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Wagner, David. "Security and Machine Learning." In CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM, 2017. http://dx.doi.org/10.1145/3133956.3134108.

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Wiranda, Nuruddin, and Fal Sadikin. "Machine Learning for Security and Security for Machine Learning: A Literature Review." In 2021 4th International Conference on Information and Communications Technology (ICOIACT). IEEE, 2021. http://dx.doi.org/10.1109/icoiact53268.2021.9563985.

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Reports on the topic "Machine Learning in Security"

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Barreno, Marco, Blaine A. Nelson, Anthony D. Joseph, and Doug Tygar. The Security of Machine Learning. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada519143.

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Verzi, Stephen, Raga Krishnakumar, Drew Levin, Daniel Krofcheck, and Kelly Williams. Data Science and Machine Learning for Genome Security. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1855003.

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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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Skryzalin, Jacek, Kenneth Goss, and Benjamin Jackson. Securing machine learning models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1661020.

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Singhal, Anoop. Modeling and Security Analysis of Attacks on Machine Learning Systems. National Institute of Standards and Technology, 2025. https://doi.org/10.6028/nist.sp.800-237.

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Singhal, Anoop. Modeling and Security Analysis of Attacks on Machine Learning Systems. National Institute of Standards and Technology, 2025. https://doi.org/10.6028/nist.sp.800-237.ipd.

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Ritchey, Ralph P., Garrett S. Payer, and Richard E. Harang. Compilation of a Network Security/Machine Learning Toolchain for Android ARM Platforms. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada609411.

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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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Lohn, Andrew. Poison in the Well: Securing the Shared Resources of Machine Learning. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/2020ca013.

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Modern machine learning often relies on open-source datasets, pretrained models, and machine learning libraries from across the internet, but are those resources safe to use? Previously successful digital supply chain attacks against cyber infrastructure suggest the answer may be no. This report introduces policymakers to these emerging threats and provides recommendations for how to secure the machine learning supply chain.
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Buchanan, Ben. A National Security Research Agenda for Cybersecurity and Artificial Intelligence. Center for Security and Emerging Technology, 2020. http://dx.doi.org/10.51593/2020ca001.

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Machine learning advances are transforming cyber strategy and operations. This necessitates studying national security issues at the intersection of AI and cybersecurity, including offensive and defensive cyber operations, the cybersecurity of AI systems, and the effect of new technologies on global stability.
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