A computer-implemented method comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform opera-tions comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer-implemented method comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform opera-tions comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer-implemented method comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform opera-tions comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer-implemented method comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer program product comprising one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by a processor to cause the processor to perform operations comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.

A computer system comprising a processor and one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable by the processor to cause the processor to perform opera-tions comprising: training, using a first plurality of real data packets transmitted over a communications network, a generative adversarial network (GAN) to generate a first plurality of generated data packets corresponding to the first plurality of real data packets, the training resulting in a trained GAN; generating, using the trained GAN, a second plurality of generated data packets from a second plurality of real data packets transmitted over the communications net-work; generating, using a probability distribution of the second plurality of real data packets and a probability distri-bution of the second plurality of generated data pack-ets, a plurality of sampling indices, each sampling index in the plurality of sampling indices comprising a packet number to be sampled for inspection; and inspecting, using a packet inspector, a third plurality of data packets transmitted over the communications net-work, each inspected data packet having an index in the plurality of sampling indices.