Auswahl der wissenschaftlichen Literatur zum Thema „Cyberprotection“
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Zeitschriftenartikel zum Thema "Cyberprotection"
Chevardin, V., O. Yurchenko, O. Zaluzhnyi und Ye Peleshok. „Analysis of adversarial attacks on the machine learning models of cyberprotection systems.“ Communication, informatization and cybersecurity systems and technologies, Nr. 4 (21.12.2023): 100–109. http://dx.doi.org/10.58254/viti.4.2023.09.100.
Der volle Inhalt der QuelleNespoli, Pantaleone, Daniel Díaz-López und Félix Gómez Mármol. „Cyberprotection in IoT environments: A dynamic rule-based solution to defend smart devices“. Journal of Information Security and Applications 60 (August 2021): 102878. http://dx.doi.org/10.1016/j.jisa.2021.102878.
Der volle Inhalt der QuellePancorbo Crespo, Jaime, Luis Guerrero Gomez und Javier Gonzalo Arias. „Autonomous Shipping and Cybersecurity“. Ciencia y tecnología de buques 13, Nr. 25 (31.07.2019): 19–26. http://dx.doi.org/10.25043/19098642.185.
Der volle Inhalt der QuelleMoore, Michael Roy, Robert A. Bridges, Frank L. Combs und Adam L. Anderson. „Data-Driven Extraction of Vehicle States From CAN Bus Traffic for Cyberprotection and Safety“. IEEE Consumer Electronics Magazine 8, Nr. 6 (01.11.2019): 104–10. http://dx.doi.org/10.1109/mce.2019.2928577.
Der volle Inhalt der QuelleДрейс, Юрій, und Леонід Деркач. „БАЗОВА МНОЖИНА УЗАГАЛЬНЕНИХ КРИТЕРІЇВ ВІДНЕСЕННЯ ОБ’ЄКТІВ ДО КРИТИЧНОЇ ІНФРАСТРУКТУРИ ДЕРЖАВИ“. Ukrainian Scientific Journal of Information Security 27, Nr. 1 (30.04.2021): 13–20. http://dx.doi.org/10.18372/2225-5036.27.15807.
Der volle Inhalt der QuelleMuravskyi, Volodymyr, Vasyl Muravskyi und Oleh Shevchuk. „Classification of stakeholders (users) of accounting information for the enterprise cybersecurity purposes“. Herald of Economics, Nr. 1(99) (01.02.2021): 83. http://dx.doi.org/10.35774/visnyk2021.01.083.
Der volle Inhalt der QuelleКорченко, Олександр Григорович, Ігор Вадимович Логінов und Сергій Олександрович Скворцов. „Stationary systems of cyberattacks detection and prevention for cyberprotection and cybercounterintelli-gence (by example USA)“. Ukrainian Scientific Journal of Information Security 25, Nr. 1 (25.04.2019). http://dx.doi.org/10.18372/2225-5036.25.13664.
Der volle Inhalt der QuelleDissertationen zum Thema "Cyberprotection"
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.
Der volle Inhalt der QuelleThe list of connected devices (or IoT) is growing longer with time and so is the intense vulnerability to security of the devices against targeted attacks originating from network or physical penetration, popularly known as Cyber Physical Security (CPS) attacks. While security sensors and obfuscation techniques exist to counteract and enhance security, it is possible to fool these classical security countermeasures with sophisticated attack equipment and methodologies as shown in recent literature. Additionally, end node embedded systems design is bound by area and is required to be scalable, thus, making it difficult to adjoin complex sensing mechanism against cyberphysical attacks. The solution may lie in Artificial Intelligence (AI) security core (soft or hard) to monitor data behaviour internally from various components. Additionally the AI core can monitor the overall device behaviour, including attached sensors, to detect any outlier activity and provide a smart sensing approach to attacks. AI in hardware security domain is still not widely acceptable due to the probabilistic behaviour of the advanced deep learning techniques, there have been works showing practical implementations for the same. This work is targeted to establish a proof of concept and build trust of AI in security by detailed analysis of different Machine Learning (ML) techniques and their use cases in hardware security followed by a series of case studies to provide practical framework and guidelines to use AI in various embedded security fronts. Applications can be in PUFpredictability assessment, sensor fusion, Side Channel Attacks (SCA), Hardware Trojan detection, Control flow integrity, Adversarial AI, etc
Konferenzberichte zum Thema "Cyberprotection"
Azeez, Nureni Ayofe, und Ademolu Oluwatosin. „CyberProtector: Identifying Compromised URLs in Electronic Mails with Bayesian Classification“. In 2016 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2016. http://dx.doi.org/10.1109/csci.2016.0184.
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