AD-NEv++ : The multi-architecture neuroevolution-based multivariate anomaly detection framework

arXiv:2404.07968v1 Announce Type: cross
Abstract: Anomaly detection tools and methods enable key analytical capabilities in modern cyberphysical and sensor-based systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time-consuming process. Neuroevolution could be an effective and efficient solution to this problem, as a fully automated search method for learning optimal neural networks, supporting both gradient and non-gradient fine tuning. However, existing frameworks incorporating neuroevolution lack of support …

abstract anomaly anomaly detection architecture architectures arxiv capabilities cs.ai cs.lg cs.ne dataset deep learning detection detection tools development framework key model optimization modern multivariate neuroevolution optimization process sensor solution systems tools type