Research On Darknet Multi-platform User Identity Alignment Method

Cyber hazards such as cyber attacks,illegal transactions,and cyber rumors seriously affect social stability and national security.With the strengthening of national control,its living space has gradually shifted from the clear web to the anonymous space such as the deep and darknet.The anonymization of network public hazards brings challenges to the investigation and evidence collection of law enforcement officers,and is a major problem in the field of cyberspace management and control.Detecting and identifying the identity of network users is the main content of clue discovery and investigation in network public nuisance management cases,and is the basis of network police work.Current network user identity reconnaissance mainly relies on traditional reconnaissance methods such as network addresses and network identity characteristics.Since real network addresses and effective network identity characteristics cannot be obtained in anonymous spaces,traditional reconnaissance methods are difficult to produce effects in anonymous spaces,and research is urgently needed A new approach to identity reconnaissance of network users in anonymous spaces.Aiming at the difficulty of detecting the identity of users in anonymous spaces in actual police operations and the lack of effective user alignment methods,this paper proposes a new user alignment method based on darknet text writing style,which solves the network control problems caused by anonymization.Aiming at the problem of difficult detection of anonymized user identities in cyberspace,this paper takes the darknet where cyber crimes frequently occur as a research background,and focuses on mining clues for multi-platform users in the darknet by studying the darknet user identity identification method based on text writing style.and association,darknet text sample sparseness,darknet text feature extraction,and darknet user identity alignment modeling,etc.,using the multi-category identity clue mining method based on heuristic rules,the user identity clue association method based on information extraction,and the user identity clue association method based on The user text content enhancement method of the prototype network,data augmentation is carried out from two aspects of user level and content level.At the same time,combined with the text feature extraction of self-attention-enhanced convolution and the process of user-network interaction,multi-task learning is carried out from the fusion of text writing content and user network behavior,and a darknet multi-platform user identity alignment is proposed.The new method has significantly improved the accuracy of identity detection of anonymous network users.The main innovations of the paper are as follows:(1)Aiming at the problem that it is difficult to obtain multiple identity information of the same user on multiple platforms in the darknet,a cross-platform user identity clue association method with few samples is proposed.This method mines user identity clues in darknet pages through heuristic rules;builds a supervised coreference relationship extraction model CRE(Coreference Relation Extraction)through information extraction technology to automatically correlate multiple account information of the same user across platforms;By constructing a user identity clue association method for a small number of annotations under multi-task and lowresource conditions,the model’s dependence on large-scale annotation samples is effectively reduced,and the purpose of obtaining more abundant training data for user identity alignment is achieved.Through this method,the Darknet User Identity Information Dataset DID(Darknet User Identity Information Dataset)is constructed.The method proposed in this paper shows better performance compared with a variety of classic relationship extraction models on the DID dataset.(2)Aiming at the problem that the number of darknet users’ postings is insufficient and it is difficult to determine the writing style,a user text content enhancement method based on the prototype network is proposed.By designing a Chinese text transformation strategy that can maintain semantic consistency,the method constructs the adversarial example generation model AEGP(Adversarial Example Generation with Prototypical)of the prototype network,selects the transformation fragments,and realizes the purpose of enhancing the identification data of the same writing style at the content layer.The data enhancement method of the adversarial sample method proposed in the paper is not only diversified but also has improved versatility.(3)Aiming at the short-text-oriented identity alignment method to deal with the lack of global and long-sequence information,a user identity alignment method that integrates writing style and network behavior is proposed.This method uses the self-attention mechanism to enhance the convolution,and uses the global information and long sequence information to obtain the writing style;at the same time,it introduces the meta-path information to model the user’s network behavior in the darknet,and combines multiple dimensions of text embedding,time embedding and context embedding,build a multi-task learning model FTNB(A Multi-task Learning Model that Fuses Text Features and Network Behavior)that fuses text features and network behaviors,realizes the alignment task for multi-platform users on the darknet,and improves alignment efficiency.