This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates senso...This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.展开更多
Spammer detection is to identify and block malicious activities performing users.Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity ...Spammer detection is to identify and block malicious activities performing users.Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces.Previous research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled datasets.However,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time detection.For example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time environment.To fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate results.REMS is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and analysis.Secondly,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine users.The diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical locations.The REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph structures.Dataset and REMS’s codes are available on GitHub(www.github.com/mhadnanali/REMS).展开更多
文摘This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems,focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots.Each follower robot has unknown dynamics and control inputs,which expose it to the risks of both sensor and actuator attacks.The leader robot,described by a secondorder,time-varying nonlinear model,transmits its position,velocity,and acceleration information to follower robots through a wireless connection.To handle the complex setup and communication among robots in the network,we design a robust hybrid distributed adaptive control strategy combining the effect of sensor and actuator attack,which ensures asymptotic consensus,extending beyond conventional bounded consensus results.The proposed framework employs fuzzy logic systems(FLSs)as proactive controllers to estimate unknown nonlinear behaviors,while also effectively managing sensor and actuator attacks,ensuring stable consensus among all agents.To counter the impact of the combined signal attack on follower dynamics,a specialized robust control mechanism is designed,sustaining system stability and performance under adversarial conditions.The efficiency of this control strategy is demonstrated through simulations conducted across two different directed communication topologies,underscoring the protocol’s adaptability,resilience,and effectiveness in maintaining global consensus under complex attack scenarios.
基金supported by the Guangzhou Government Project(Grant No.62216235)the National Natural Science Foundation of China(Grant Nos.61573328,622260-1).
文摘Spammer detection is to identify and block malicious activities performing users.Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces.Previous research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled datasets.However,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time detection.For example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time environment.To fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate results.REMS is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and analysis.Secondly,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine users.The diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical locations.The REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph structures.Dataset and REMS’s codes are available on GitHub(www.github.com/mhadnanali/REMS).
