Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the be...Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots:a higher number of connected components weakens their collaborative capabilities,thereby reducing their proportion within the overall network.However,current social bot detection methods still make limited use of topological features.Furthermore,both graph neural network(GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations,and existing studies lack an effective fusion of multi-scale information.To address these issues,this paper proposes a topology-aware multi-scale social bot detection method,which jointly learns local and global representations through a co-training mechanism.At the local level,topological features are effectively embedded into node representations,enhancing expressiveness while alleviating the over-smoothing problem in GNNs.At the global level,a clustering attention mechanism is introduced to learn global node representations,mitigating the over-globalization problem.Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches.Our code is publicly available at https://anonymous.4open.science/r/TopoMSG-2C41/(accessed on 27 October 2025).展开更多
The high costs incurred due to attacks and the increasing number of different devices in the Internet of Things(IoT)highlight the necessity of the early detection of botnets(i.e.,a network of infected devices)to gain ...The high costs incurred due to attacks and the increasing number of different devices in the Internet of Things(IoT)highlight the necessity of the early detection of botnets(i.e.,a network of infected devices)to gain an advantage against attacks.However,early botnet detection is challenging because of continuous malware mutations,the adoption of sophisticated obfuscation techniques,and the massive volume of data.The literature addresses botnet detection by modeling the behavior of malware spread,the classification of malicious traffic,and the analysis of traffic anomalies.This article details ANTE,a system for ANTicipating botnEt signals based on machine learning algorithms.The system adapts itself to different scenarios and detects different types of botnets.It autonomously selects the most appropriate Machine Learning(ML)pipeline for each botnet and improves the classification before an attack effectively begins.The system evaluation follows trace-driven experiments and compares ANTE results to other relevant results from the literature over four representative datasets:ISOT HTTP Botnet,CTU-13,CICDDoS2019,and BoT-IoT.Results show an average detection accuracy of 99.06%and an average bot detection precision of 100%.展开更多
The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In ...The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In this paper,we introduce a unified framework for defining and classifying malicious social bots along three dimensions:behavior,interaction,and operation.We then present a comprehensive review of social bot detection methods,tracing their evolution from traditional machine learning techniques to deep learning architectures and graph neural networks,with particular emphasis on recent advances in group-level detection.We also explore the emerging paradigm of Large Language Model(LLM)based bot detection.This paper reviews the current state of research,identifies key challenges,and outlines future directions.It provides a cohesive foundation for building more robust detection frameworks to counter the evolving threats posed by malicious social bots.展开更多
基金supported by“the Fundamental Research Funds for the Central Universities”(Grant No.CUCAI2511).
文摘Social bots are automated programs designed to spread rumors and misinformation,posing significant threats to online security.Existing research shows that the structure of a social network significantly affects the behavioral patterns of social bots:a higher number of connected components weakens their collaborative capabilities,thereby reducing their proportion within the overall network.However,current social bot detection methods still make limited use of topological features.Furthermore,both graph neural network(GNN)-based methods that rely on local features and those that leverage global features suffer from their own limitations,and existing studies lack an effective fusion of multi-scale information.To address these issues,this paper proposes a topology-aware multi-scale social bot detection method,which jointly learns local and global representations through a co-training mechanism.At the local level,topological features are effectively embedded into node representations,enhancing expressiveness while alleviating the over-smoothing problem in GNNs.At the global level,a clustering attention mechanism is introduced to learn global node representations,mitigating the over-globalization problem.Experimental results demonstrate that our method effectively overcomes the limitations of single-scale approaches.Our code is publicly available at https://anonymous.4open.science/r/TopoMSG-2C41/(accessed on 27 October 2025).
基金This work was supported by National Council for Scientific and Technological Development(CNPq/Brazil)grants#309129/2017-6 and#432204/2018-0,by Sao Paulo Research Foundation(FAPESP)+2 种基金grant#2018/23098-0,by the Coordination for the Improvement of Higher Education Personnel CAPES/Brazilgrants#88887.501287/2020-00 and#88887.509309/2020–00by the National Teaching and Research Network(RNP)by the GT-Periscope project.
文摘The high costs incurred due to attacks and the increasing number of different devices in the Internet of Things(IoT)highlight the necessity of the early detection of botnets(i.e.,a network of infected devices)to gain an advantage against attacks.However,early botnet detection is challenging because of continuous malware mutations,the adoption of sophisticated obfuscation techniques,and the massive volume of data.The literature addresses botnet detection by modeling the behavior of malware spread,the classification of malicious traffic,and the analysis of traffic anomalies.This article details ANTE,a system for ANTicipating botnEt signals based on machine learning algorithms.The system adapts itself to different scenarios and detects different types of botnets.It autonomously selects the most appropriate Machine Learning(ML)pipeline for each botnet and improves the classification before an attack effectively begins.The system evaluation follows trace-driven experiments and compares ANTE results to other relevant results from the literature over four representative datasets:ISOT HTTP Botnet,CTU-13,CICDDoS2019,and BoT-IoT.Results show an average detection accuracy of 99.06%and an average bot detection precision of 100%.
基金supported by the National Natural Science Foundation of China(No.62302213)Key Laboratory of Social Computing and Cognitive Intelligence(Dalian University of Technology),Ministry of Education,China.
文摘The rise of online social platforms has enhanced connectivity and access to information.Still,it has also enabled the proliferation of malicious social bots that threaten platform security and disrupt social order.In this paper,we introduce a unified framework for defining and classifying malicious social bots along three dimensions:behavior,interaction,and operation.We then present a comprehensive review of social bot detection methods,tracing their evolution from traditional machine learning techniques to deep learning architectures and graph neural networks,with particular emphasis on recent advances in group-level detection.We also explore the emerging paradigm of Large Language Model(LLM)based bot detection.This paper reviews the current state of research,identifies key challenges,and outlines future directions.It provides a cohesive foundation for building more robust detection frameworks to counter the evolving threats posed by malicious social bots.
