Titlebook: Artificial Intelligence for Security; Enhancing Protection Tuomo Sipola,Janne Alatalo,Tero Kokkonen Book 2024 The Editor(s) (if applicable)

Engulf

SPALL

Artificial Intelligence and Differential Privacy: Review of Protection Estimate Modelseoretical, and relational proof of privacy, which makes it important to understand the actual behavior of the DP-based protection models. For this purpose, we will review what kind of frameworks or models are available to estimate how well an implemented differential privacy model works. Special att

Mast-Cell

innovation

放逐某人

Who Guards the Guardians? On Robustness of Deep Neural Networksther to mislead and change the model’s behavior or to leak information about the training data and potentially about the model in use. These attacks can be readily mapped within the Confidentiality, Integrity, and Availability triad components. We lay out the potential threat models and include the

中國紀(jì)念碑

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嘮叨

On the Cybersecurity of Logistics in the Age of Artificial Intelligencely involved in national critical infrastructures (CI): transportation is directly identified as one of the CI sectors, and many other CI sectors cannot adequately function without properly working logistics. To optimize business processes and automate operational technology, different machine learni

Conflagration

Fulminate

On Protection of the Next-Generation Mobile Networks Against Adversarial Examplesgent machine learning (ML)-driven network components to adversarial effects. Due to the shared nature of wireless mediums, these components may be susceptible to sophisticated attacks that can manipulate the training and inference processes of the AI/ML models over the air. In our research, we focus