Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all participants.However,single-signature applications in transactions c...Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all participants.However,single-signature applications in transactions can lead to user identification issues due to the reuse of public keys.To address this issue,group signatures can be used,where the same group public key is used to verify signatures from group members to provide anonymity to users.However,in dynamic groups where membership may change,an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial key.This problem cannot be traced back to the partial key leaker.In this paper,we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key leaks.Exist schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing authority.This paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.展开更多
Nowadays,in almost every computer system,log files are used to keep records of occurring events.Those log files are then used for analyzing and debugging system failures.Due to this important utility,researchers have ...Nowadays,in almost every computer system,log files are used to keep records of occurring events.Those log files are then used for analyzing and debugging system failures.Due to this important utility,researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records.Research in log-based anomaly detection can be divided into two main categories:batch log-based anomaly detection and streaming log-based anomaly detection.Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies.On the other hand,streaming anomaly detection allows for immediate alert.However,current streaming approaches are mainly supervised.In this work,we propose a fully unsupervised framework which can detect anomalies in real time.We test our framework on hdfs log files and successfully detect anomalies with an F-1 score of 83%.展开更多
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(RS-2022-00167197,Development of Intelligent 5G/6G Infrastructure Technology for The Smart City)and this work was funded by BK21 FOUR(Fostering Outstanding Universities for Research)(5199990914048)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(2022R1A2B5B01002490)the Soonchunhyang University Research Fund.
文摘Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all participants.However,single-signature applications in transactions can lead to user identification issues due to the reuse of public keys.To address this issue,group signatures can be used,where the same group public key is used to verify signatures from group members to provide anonymity to users.However,in dynamic groups where membership may change,an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial key.This problem cannot be traced back to the partial key leaker.In this paper,we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key leaks.Exist schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing authority.This paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.
文摘Nowadays,in almost every computer system,log files are used to keep records of occurring events.Those log files are then used for analyzing and debugging system failures.Due to this important utility,researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records.Research in log-based anomaly detection can be divided into two main categories:batch log-based anomaly detection and streaming log-based anomaly detection.Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies.On the other hand,streaming anomaly detection allows for immediate alert.However,current streaming approaches are mainly supervised.In this work,we propose a fully unsupervised framework which can detect anomalies in real time.We test our framework on hdfs log files and successfully detect anomalies with an F-1 score of 83%.
