Titlebook: Machine Learning for Networking; 6th International Co éric Renault,Selma Boumerdassi,Paul Mühlethaler Conference proceedings 2024 The Edito

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978-3-031-59932-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Machine Learning for Networking978-3-031-59933-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

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,Attentive, Permutation Invariant, One-Shot Node-Conditioned Graph Generation for?Wireless Networks ice, especially when dealing with an important number of communication nodes [.]. As opposed to their omnidirectional counterpart, directional antennas allow for much more manageable interference patterns: a receiving antenna is not necessarily interfered by nearby emitting antennas as long as this

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A Deep Learning Based Automatic Outdoor Home Video Surveillance Approach,wrongdoers. Nonetheless, it’s essential to emphasize that these systems very frequently lead to false alarms, which can be quite frustrating for users. The existing automatic video surveillance methods and datasets were initially developed for monitoring public crimes, and they do not generalize wel

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Detecting Abnormal Authentication Delays In Identity And Access Management Using Machine Learning, increase in authentication delay can indicate several problems, such as performance degradation, denial-of-service attacks, or compromised accounts. This paper proposes an adaptative approach for anomaly detection in authentication delay for IAM systems. The proposed approach combines the cumulativ

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