公开(公告)号：US20240244089A1

公开(公告)日：2024-07-18

申请号：US18622422

申请日：2024-03-29

Applicant: HUAWEI TECHNOLOGIES CO., LTD. ,  Politecnico di Torino

Inventor： Lorenzo DEL SORDO ,  Giulia MILAN ,  Luca VASSIO ,  Marco MELLIA ,  Idilio DRAGO ,  Zied BEN HOUIDI ,  Dario ROSSI

IPC: H04L9/40

CPC classification number: H04L63/20 ,  H04L63/1491

Abstract: The present disclosure relates generally to the field of information technology (IT) and network security, and particularly discloses a honeypot entity. The honeypot entity is configured to receive a command of a user, and determine if an assessment of the received command is required. If the assessment is required the entity is configured to retrieve a first set of command outputs associated with the command from backend systems, and populate a knowledge base with the command and the first set of command outputs. Further, the entity is configured to retrieve a second set of command outputs from the knowledge base, and select a command output of the second set in dependence of a policy. The entity is then configured to output the selected command to the user, and adapt the policy in dependence of an interaction history associated with the user and an immediate reward associated with the selected command.

公开(公告)号：US20230076178A1

公开(公告)日：2023-03-09

申请号：US18056450

申请日：2022-11-17

Applicant: Huawei Technologies Co., Ltd.

Inventor： Zied BEN HOUIDI ,  Hao SHI

IPC: G06N3/04 ,  H04L41/08

Abstract: The present disclosure relates to the field of computer networks. More specifically, a solution for machine learning-based classification of host identifiers in encrypted network traffic is provided. The classification can, in particular, include natural language processing capabilities. The present disclosure provides a network device for host identifier classification. The network device is configured to obtain a sequence of host identifiers, each host identifier corresponding to a flow of encrypted network traffic, apply an unsupervised learning technique to the sequence of host identifiers to learn a vector of a high-dimensional space for each host identifier in the sequence, obtain a labelled ground truth comprising labels corresponding to a host identifier, and apply a supervised learning technique to each vector, based on the labelled ground truth, to classify the corresponding host identifier.