Abstract: A base station is disclosed. The base station includes several radio devices and a core network device. The core network device is connected to the several radio devices and is configured to determine a first user location data corresponding to a user device according to a user uplink transmission data and several user location data, and to determine whether to allow the user device to perform a data uplink transmission operation or not according to the first user location data. The user uplink transmission data is uplink transmitted by the user device, and the several user location data are establish according to the several radio devices and several user locations. The several user location data include the first user location data.

Abstract: A base station is disclosed. The base station includes several radio devices and a core network device. The core network device is connected to the several radio devices and is configured to determine a first user location data corresponding to a user device according to a user uplink transmission data and several user location data, and to determine whether to allow the user device to perform a data uplink transmission operation or not according to the first user location data. The user uplink transmission data is uplink transmitted by the user device, and the several user location data are establish according to the several radio devices and several user locations. The several user location data include the first user location data.

Abstract: A malicious software recognition apparatus and method are provided. The malicious software recognition apparatus stores a training dataset, which includes a plurality of network flow datasets. Each network flow dataset corresponds to one of a plurality of software categories, and the software categories include a plurality of malicious software categories. The malicious software recognition apparatus tests a malicious software recognition model and learns that a plurality of recognition accuracies of a subset of the malicious software categories are low, determines that an overlap degree of the network flow datasets corresponding to the subset is high, updates the software categories by combining the malicious software categories corresponding to the subset, updates the training dataset by integrating the network flow datasets corresponding to the subset, trains the malicious software recognition model according to the updated training dataset.

Abstract: A malicious software recognition apparatus and method are provided. The malicious software recognition apparatus stores a training dataset, which includes a plurality of network flow datasets. Each network flow dataset corresponds to one of a plurality of software categories, and the software categories include a plurality of malicious software categories. The malicious software recognition apparatus tests a malicious software recognition model and learns that a plurality of recognition accuracies of a subset of the malicious software categories are low, determines that an overlap degree of the network flow datasets corresponding to the subset is high, updates the software categories by combining the malicious software categories corresponding to the subset, updates the training dataset by integrating the network flow datasets corresponding to the subset, trains the malicious software recognition model according to the updated training dataset.