Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhib...Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.展开更多
Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from attacks.In...Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from attacks.In this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be developed.Earlier studies have developed different approaches for the detection of spam bots in OSN.But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be explored.With this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBDHDL.The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs.The technique has different stages of operations such as pre-processing,classification,and parameter optimization.Besides,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification process.In order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this domain.The experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.展开更多
Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a st...Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.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
Intelligent blockchain is an emerging field that integrates Artificial Intelligence(AI)techniques with blockchain networks,with a particular emphasis on improving the performance of blockchain,especially in cryptocurr...Intelligent blockchain is an emerging field that integrates Artificial Intelligence(AI)techniques with blockchain networks,with a particular emphasis on improving the performance of blockchain,especially in cryptocurrencies exchanges.Meanwhile,arbitrage bots are widely deployed and increasing in intelligent blockchain.These bots exploit the characteristics of cryptocurrencies exchanges to engage in frontrunning,generating substantial profits at the expense of ordinary users.In this paper,we address this issue by proposing a more efficient asynchronous Byzantine ordered consensus protocol,which can be used to prevent arbitrage bots from changing the order of the transactions for profits in intelligent blockchain-based cryptocurrencies.Specifically,we present two signal asynchronous common subset protocols,the more optimal one with only constant time complexity.We implement both our protocol and the optimal existing solution Chronos with Go language in the same environment.The experiment results indicate that our protocols achieve a threefold improvement over Chronos in consensus latency and nearly a tenfold increase in throughput.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.61773091 and 62476045)the LiaoNing Revitalization Talents Program(Grant No.XLYC1807106)the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Liaoning(Grant No.LR2016070).
文摘Complex network modeling characterizes system relationships and structures,while network visualization enables intuitive analysis and interpretation of these patterns.However,existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales,particularly failing to provide advanced visual representations of specific nodes and edges,community affiliation attribution,and global scalability.These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation.To address these limitations,we propose SFFSlib,a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization.Notably,we have enhanced the visualization of pivotal details at different scales across diverse network scenarios.The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms.The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales,offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under Grant Number(RGP 1/53/42).www.kku.edu.sa.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-Track Path of Research Funding Program。
文摘Cybersecurity encompasses various elements such as strategies,policies,processes,and techniques to accomplish availability,confidentiality,and integrity of resource processing,network,software,and data from attacks.In this scenario,the rising popularity of Online Social Networks(OSN)is under threat from spammers for which effective spam bot detection approaches should be developed.Earlier studies have developed different approaches for the detection of spam bots in OSN.But those techniques primarily concentrated on hand-crafted features to capture the features of malicious users while the application of Deep Learning(DL)models needs to be explored.With this motivation,the current research article proposes a Spam Bot Detection technique using Hybrid DL model abbreviated as SBDHDL.The proposed SBD-HDL technique focuses on the detection of spam bots that exist in OSNs.The technique has different stages of operations such as pre-processing,classification,and parameter optimization.Besides,SBD-HDL technique hybridizes Graph Convolutional Network(GCN)with Recurrent Neural Network(RNN)model for spam bot classification process.In order to enhance the detection performance of GCN-RNN model,hyperparameters are tuned using Lion Optimization Algorithm(LOA).Both hybridization of GCN-RNN and LOA-based hyperparameter tuning process make the current work,a first-of-its-kind in this domain.The experimental validation of the proposed SBD-HDL technique,conducted upon benchmark dataset,established the supremacy of the technique since it was validated under different measures.
文摘Autonomous navigation of mobile robots is a challenging task that requires them to travel from their initial position to their destination without collision in an environment.Reinforcement Learning methods enable a state action function in mobile robots suited to their environment.During trial-and-error interaction with its surroundings,it helps a robot tofind an ideal behavior on its own.The Deep Q Network(DQN)algorithm is used in TurtleBot 3(TB3)to achieve the goal by successfully avoiding the obstacles.But it requires a large number of training iterations.This research mainly focuses on a mobility robot’s best path prediction utilizing DQN and the Artificial Potential Field(APF)algorithms.First,a TB3 Waffle Pi DQN is built and trained to reach the goal.Then the APF shortest path algorithm is incorporated into the DQN algorithm.The proposed planning approach is compared with the standard DQN method in a virtual environment based on the Robot Operation System(ROS).The results from the simulation show that the combination is effective for DQN and APF gives a better optimal path and takes less time when compared to the conventional DQN algo-rithm.The performance improvement rate of the proposed DQN+APF in comparison with DQN in terms of the number of successful targets is attained by 88%.The performance of the proposed DQN+APF in comparison with DQN in terms of average time is achieved by 0.331 s.The performance of the proposed DQN+APF in comparison with DQN average rewards in which the positive goal is attained by 85%and the negative goal is attained by-90%.
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 Key R&D Program of China under Grant(2022YFB2702702)in part by the National Natural Science Foundation of China under Grants(62372020,72031001)+1 种基金in part by the Beijing Natural Science Foundation under Grants(L222050)in part by the Fundamental Research Funds for the Central Universities under Grant(YWF-23-L-1032).
文摘Intelligent blockchain is an emerging field that integrates Artificial Intelligence(AI)techniques with blockchain networks,with a particular emphasis on improving the performance of blockchain,especially in cryptocurrencies exchanges.Meanwhile,arbitrage bots are widely deployed and increasing in intelligent blockchain.These bots exploit the characteristics of cryptocurrencies exchanges to engage in frontrunning,generating substantial profits at the expense of ordinary users.In this paper,we address this issue by proposing a more efficient asynchronous Byzantine ordered consensus protocol,which can be used to prevent arbitrage bots from changing the order of the transactions for profits in intelligent blockchain-based cryptocurrencies.Specifically,we present two signal asynchronous common subset protocols,the more optimal one with only constant time complexity.We implement both our protocol and the optimal existing solution Chronos with Go language in the same environment.The experiment results indicate that our protocols achieve a threefold improvement over Chronos in consensus latency and nearly a tenfold increase in throughput.
