Towards a Universal Security Framework for Darknet Suppression:Conceptual Foundations and Future Prospects
Significance In recent years,anonymous networks and their underlying darknet have become vital tools for transmitting sensitive information,conducting cyberattacks,and engaging in cybercrime due to their strong concealment,high anonymity,and resistance to traceability.These char-acteristics pose serious threats to national security and social stability.This project researches a universal security theory for darknet suppression to address the challenges of darknet governance,such as difficulties in identifying concealed communication behaviors,mapping dynamic net-work topologies,and disguising trap node deployments.Progress The main content includes:1)Establishing a collaborative quantitative theoretical framework focused on darknet traffic differences and behavioral commonalities.This involves proposing heterogeneous darknet universal characteristics,differentiated element representations,uni-fied security quantification,and ecological vulnerability graph construction theories.These approaches address the challenge of quantifying darknet suppressibility,which remains complicated by diverse network structures and dynamic communication behaviors.2)Proposing a real-time lightweight traffic detection method based on solving convex optimization problems.This involves constructing a small flow sampling mod-el based on self-similarity associations and a darknet traffic identification and service classification model using Gaussian kernel functions and multimodal optimization.This method enables precise,real-time identification and classification of darknet traffic.3)Introducing a multi-net-work full-time domain connection prediction and relationship mapping method based on behavioral invariance.This approach represents cross-point connections and filters out irrelevant connections in dynamic networks to predict multi-network full-time domain connections and map rela-tionships,achieving multi-point global associations of darknet connections under local observation conditions.4)Proposing a trap node deploy-ment and tracing optimization method for darknet connections based on local observations,enabling tracking and tracing of the darknet under con-ditions of partially controllable nodes.5)Developing a real-time traffic detection and tracing demonstration system for real-world darknet scenari-os,which law enforcement agencies implement to achieve precise governance of darknet-related crimes.Conclusions and Prospects This project significantly contributes to darknet governance by developing a quantitative framework for analyzing and managing darknet traffic.The proposed real-time lightweight traffic detection method enhances law enforcement's ability to identify and clas-sify darknet activities.In addition,these methods for predicting multi-network connections and optimizing trap node deployment improve track-ing capabilities in complex environments.Future work focuses on refining these methodologies and exploring additional dimensions of darknet behavior to strengthen efforts in combating illicit online activities,generating meaningful social and economic benefits.
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