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

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Designing and Implementing an Interactive Cloud Platform for Teaching Machine Learning with Medical rapidly. New applications in economics, industry, and healthcare create new challenges for the sustainable development of our society. We describe the organization and realization of a machine learning seminar that integrates a theoretical seminar and a practical tutorial focusing on the employments

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Machine Learning and Anomaly Detection for an Automated Monitoring of Log Datae the status of a software system. However, the large number of log entries makes manual analysis of the log data difficult. In order to identify errors or disruptions of computer systems at an early stage, an automated evaluation of the log data using artificial intelligence can be useful. In this

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Enhancing Embedded IoT Systems for Intrusion Detection Using a Hybrid Modelhis issue, we propose a novel hybrid intrusion detection algorithm that combines the deep autoencoder-based intrusion detection system (DAIDS) with recurrent neural networks (RNNs). The hybrid model aims to leverage the strengths of both approaches to enhance the accuracy and robustness of intrusion

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is an evident development. To this effect, this book brings together a group of international researchers and professionals who present their views on how to create security through AI. ..This book targets pos978-3-031-57454-2978-3-031-57452-8

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https://doi.org/10.1007/978-1-4419-9356-4ient. When experts therefore work toward the ethical and sustainable implementation of their work, they align with the Sustainable Development Goals. The time of AI black boxes is over. Only explainable, trustworthy, and transparent solutions have a chance to prevail. Since this may not be the most

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Empirical examination of comprehensibilityhe often overlooked category in our description: the outliers, which can be exploited as adversarial inputs. In addition, we cover two powerful attacks aimed at breaking privacy: model stealing and membership inference. Finally, we outline the current defenses against these attacks and conclude with

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