With the rapid development of the Internet,a large number of private protocols emerge on the network.However,some of them are constructed by attackers to avoid being analyzed,posing a threat to computer network securi...With the rapid development of the Internet,a large number of private protocols emerge on the network.However,some of them are constructed by attackers to avoid being analyzed,posing a threat to computer network security.The blockchain uses the P2P protocol to implement various functions across the network.Furthermore,the P2P protocol format of blockchain may differ from the standard format specification,which leads to sniffing tools such as Wireshark and Fiddler not being able to recognize them.Therefore,the ability to distinguish different types of unknown network protocols is vital for network security.In this paper,we propose an unsupervised clustering algorithm based on maximum frequent sequences for binary protocols,which can distinguish various unknown protocols to provide support for analyzing unknown protocol formats.We mine the maximum frequent sequences of protocolmessage sets in bytes.Andwe calculate the fuzzymembership of the protocolmessage to each maximum frequent sequence,which is based on fuzzy set theory.Then we construct the fuzzy membership vector for each protocol message.Finally,we adopt K-means++to split different types of protocol messages into several clusters and evaluate the performance by calculating homogeneity,integrity,and Fowlkes and Mallows Index(FMI).Besides,the clustering algorithms based onNeedleman–Wunsch and the fixed-length prefix are compared with the algorithm presented in this paper.Compared with these traditional clustering methods,we demonstrate a certain improvement in the clustering performance of our work.展开更多
Children and teenagers today are increasingly connected to the internet.The use by minors of social networks applications,and games that are connected to the internet offer the possibility of communication,can make th...Children and teenagers today are increasingly connected to the internet.The use by minors of social networks applications,and games that are connected to the internet offer the possibility of communication,can make them exposed to various threats.One of the most troubling threats is sexual abuse.Thus the objective of this project is to create a model for classifying messages,as normal or dangerous,according to the risk they present to the minor.In addition to integrating the developed model with a project that analyzes the behavior of minors in a social network(Facebook),and calculates the risk of the minor be a victim of sexual abuse.Finally,we use the model in the classification of messages obtained from a server of the game Minecraft,quite popular among children.展开更多
基金National Natural Science Foundation of China under Grant No.61872111Sichuan Science and Technology Program(No.2019YFSY0049)the“Project for the Development and Application of Safety Testing and Verification Platform for Industrial Robots”of the Ministry of Industry and Information Technology.
文摘With the rapid development of the Internet,a large number of private protocols emerge on the network.However,some of them are constructed by attackers to avoid being analyzed,posing a threat to computer network security.The blockchain uses the P2P protocol to implement various functions across the network.Furthermore,the P2P protocol format of blockchain may differ from the standard format specification,which leads to sniffing tools such as Wireshark and Fiddler not being able to recognize them.Therefore,the ability to distinguish different types of unknown network protocols is vital for network security.In this paper,we propose an unsupervised clustering algorithm based on maximum frequent sequences for binary protocols,which can distinguish various unknown protocols to provide support for analyzing unknown protocol formats.We mine the maximum frequent sequences of protocolmessage sets in bytes.Andwe calculate the fuzzymembership of the protocolmessage to each maximum frequent sequence,which is based on fuzzy set theory.Then we construct the fuzzy membership vector for each protocol message.Finally,we adopt K-means++to split different types of protocol messages into several clusters and evaluate the performance by calculating homogeneity,integrity,and Fowlkes and Mallows Index(FMI).Besides,the clustering algorithms based onNeedleman–Wunsch and the fixed-length prefix are compared with the algorithm presented in this paper.Compared with these traditional clustering methods,we demonstrate a certain improvement in the clustering performance of our work.
文摘Children and teenagers today are increasingly connected to the internet.The use by minors of social networks applications,and games that are connected to the internet offer the possibility of communication,can make them exposed to various threats.One of the most troubling threats is sexual abuse.Thus the objective of this project is to create a model for classifying messages,as normal or dangerous,according to the risk they present to the minor.In addition to integrating the developed model with a project that analyzes the behavior of minors in a social network(Facebook),and calculates the risk of the minor be a victim of sexual abuse.Finally,we use the model in the classification of messages obtained from a server of the game Minecraft,quite popular among children.
