The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.Thes...The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.These algorithms are often trained on the transaction behavior and,in some cases,trained on the vulnerabilities that exist in the system.In our approach,we study the feasibility of using the Domain Name(DN)associated with the account in the blockchain and identify whether an account should be tagged malicious or not.Here,we leverage the temporal aspects attached to the DN.Our approach achieves 89.53%balanced-accuracy in detecting malicious blockchain DNs.While our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over time.Nonetheless,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.展开更多
Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors.It is popularly used in knowledge graph completion(or link prediction...Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors.It is popularly used in knowledge graph completion(or link prediction)tasks.Translation-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)well.Besides,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet facts.Therefore,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship embedding.Meanwhile,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge graphs.We conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and FB15K.Our model outperforms baseline models on multiple evaluation metrics according to the experimental results.展开更多
A kind of classification on temporal relations of propositions is presented.By introducing temporal approaching relation, a new temporal logic based ontime-point and time-interval is proposed, which can describe uncer...A kind of classification on temporal relations of propositions is presented.By introducing temporal approaching relation, a new temporal logic based ontime-point and time-interval is proposed, which can describe uncertain temporalrelations. Finally some properties of temporal proposition under.uncertainrelations are proposed.展开更多
基金partially funded by the National Blockchain Project(grant number NCSC/CS/2017518)at Indian Institute of Technology KanpurIndia sponsored by the National Cyber Security Coordinator's office of the Government of India and partially by the C3i Center funding from the Science and Engineering Research Board of the Government of India(grant number SERB/CS/2016466).
文摘The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of dollars.Many machine learning algorithms are applied to detect such illegal behavior.These algorithms are often trained on the transaction behavior and,in some cases,trained on the vulnerabilities that exist in the system.In our approach,we study the feasibility of using the Domain Name(DN)associated with the account in the blockchain and identify whether an account should be tagged malicious or not.Here,we leverage the temporal aspects attached to the DN.Our approach achieves 89.53%balanced-accuracy in detecting malicious blockchain DNs.While our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over time.Nonetheless,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.
基金supported partly by National Natural Science Foundation of China(Nos.62372119 and 62166003)the Project of Guangxi Science and Technology(Nos.GuiKeAB23026040 and GuiKeAD20159041)+3 种基金the Innovation Project of Guangxi Graduate Education(No.YCSW2023188)Key Lab of Education Blockchain and Intelligent Technology,Ministry of Education,Guangxi Normal University,Guilin,China,Intelligent Processing and the Research Fund of Guangxi Key Lab of Multi-source Information Mining&Security(Nos.20-A-01-01 and MIMS21-M01)Open Research Fund of Guangxi Key Lab of Humanmachine Interaction and Intelligent Decision(No.GXHIID2206)the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and the Guangxi“Bagui”Teams for Innovation and Research,China.
文摘Knowledge representation learning(KRL)aims to encode entities and relationships in various knowledge graphs into low-dimensional continuous vectors.It is popularly used in knowledge graph completion(or link prediction)tasks.Translation-based knowledge representation learning methods perform well in knowledge graph completion(KGC).However,the translation principles adopted by these methods are too strict and cannot model complex entities and relationships(i.e.,N-1,1-N,and N-N)well.Besides,these traditional translation principles are primarily used in static knowledge graphs and overlook the temporal properties of triplet facts.Therefore,we propose a temporal knowledge graph embedding model based on variable translation(TKGE-VT).The model proposes a new variable translation principle,which enables flexible transformation between entities and relationship embedding.Meanwhile,this paper considers the temporal properties of both entities and relationships and applies the proposed principle of variable translation to temporal knowledge graphs.We conduct link prediction and triplet classification experiments on four benchmark datasets:WN11,WN18,FB13,and FB15K.Our model outperforms baseline models on multiple evaluation metrics according to the experimental results.
文摘A kind of classification on temporal relations of propositions is presented.By introducing temporal approaching relation, a new temporal logic based ontime-point and time-interval is proposed, which can describe uncertain temporalrelations. Finally some properties of temporal proposition under.uncertainrelations are proposed.
