Intrusion detection systems have benefited from Machine Learning (ML) to alleviate the problem of building and maintaining accurate signatures. Nevertheless, ML solutions face issues like overfitting or insufficient training data, which may necessitate retraining or adjustments to maintain long-term efficiency. From data collection to model training, all efforts are crucial for deploying a robust ML-based intrusion detector. Among these efforts, optimizing model hyperparameters, a timeconsuming task, can be automated by existing methods.

Yet, such methods require a validation set, making them unsuitable for training a detector on an attack-free dataset, as in anomaly-based intrusion detection. Additionally, setting the anomaly detectors' threshold, usually beyond hyperparameters configuration, requires knowledge of attacks. To overcome these challenges, this paper presents an automated solution to infer the hyperparameters and the threshold jointly from an attackfree training dataset. Pre-learned optimal configurations are transferred and fine-tuned across datasets.

Our method minimally impacts model accuracy detection performance (4% degradation), while dramatically reducing configuration time by a factor of 160 across the IDS2017 and IDS2018 datasets.