Malware is emerging day by day.To evade detection,many malware obfuscation techniques have emerged.Dynamicmalware detectionmethods based on data flow graphs have attracted much attention since they can deal with the o...Malware is emerging day by day.To evade detection,many malware obfuscation techniques have emerged.Dynamicmalware detectionmethods based on data flow graphs have attracted much attention since they can deal with the obfuscation problem to a certain extent.Many malware classification methods based on data flow graphs have been proposed.Some of them are based on userdefined features or graph similarity of data flow graphs.Graph neural networks have also recently been used to implement malware classification recently.This paper provides an overview of current data flow graph-based malware classification methods.Their respective advantages and disadvantages are summarized as well.In addition,the future trend of the data flow graph-based malware classification method is analyzed,which is of great significance for promoting the development of malware detection technology.展开更多
In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so o...In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.展开更多
The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for data-drive...The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for data-driven cell array is proposed. Cells can be fired only when the needed data arrives, and cell array can be worked on two modes: fixed execution and reconfiguration. On reconfiguration mode, cell function and data flow direction are changed automatically at run time according to contexts. Simultaneously using an H-tree interconnection network, through pre-storing multiple application mapping contexts in reconfiguration buffer, multiple applications can execute concurrently and context switching time is the minimal. For verifying system performance, some algorithms are selected for mapping onto the proposed structure, and the amount of configuration contexts and execution time are recorded for statistical analysis. The results show that the proposed self-reconfigurable mechanism can reduce the number of contexts efficiently, and has a low computing time.展开更多
为应对电力系统碳排放计算中效率和精度不足的问题,文章提出一种基于时空图神经网络(spatiotemporal graph neural network,ST-GNN)的数据驱动方法,旨在高效计算节点碳排放因子以及支路碳流和碳流损耗。文章首先分析电力系统碳排放流计...为应对电力系统碳排放计算中效率和精度不足的问题,文章提出一种基于时空图神经网络(spatiotemporal graph neural network,ST-GNN)的数据驱动方法,旨在高效计算节点碳排放因子以及支路碳流和碳流损耗。文章首先分析电力系统碳排放流计算的复杂性及传统方法的局限性,进而设计以有功-无功(active and reactive power,PQ)节点、有功-电压(active power and voltage,PV)节点和平衡节点特征为输入的ST-GNN模型,实现碳排放因子及支路碳流的直接计算,并确定支路碳流损耗。其中PQ节点的特征有功功率和无功功率,来源于电力系统运行数据,PV节点的发电功率和电压来自发电机的运行特性,平衡节点的输入包括电压和相位角,确保系统的功率平衡。通过IEEE 9节点、IEEE 57节点和IEEE118节点系统的实验,验证了所提方法的有效性。结果表明,ST-GNN模型在碳排放因子、支路碳流和碳损耗的计算精度上显著优于线性回归、决策树、长短期记忆网络和多层感知机,特别在复杂电力网络中表现突出。该研究为电力系统碳排放监测和优化提供了精准高效的技术支持。展开更多
文摘Malware is emerging day by day.To evade detection,many malware obfuscation techniques have emerged.Dynamicmalware detectionmethods based on data flow graphs have attracted much attention since they can deal with the obfuscation problem to a certain extent.Many malware classification methods based on data flow graphs have been proposed.Some of them are based on userdefined features or graph similarity of data flow graphs.Graph neural networks have also recently been used to implement malware classification recently.This paper provides an overview of current data flow graph-based malware classification methods.Their respective advantages and disadvantages are summarized as well.In addition,the future trend of the data flow graph-based malware classification method is analyzed,which is of great significance for promoting the development of malware detection technology.
基金supported in part by the National Key R&D Program of China(2017YFB1001804)Shanghai Science and Technology Innovation Action Plan Project(16511100900)
文摘In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.
基金the National Natural Science Foundation of China (Nos. 61802304, 61834005, 61772417, 61634004, and 61602377)the Shaanxi Provincial Co-ordination Innovation Project of Science and Technology (No. 2016KTZDGY02-04-02)。
文摘The utilization of computation resources and reconfiguration time has a large impact on reconfiguration system performance. In order to promote the performance, a dynamical self-reconfigurable mechanism for data-driven cell array is proposed. Cells can be fired only when the needed data arrives, and cell array can be worked on two modes: fixed execution and reconfiguration. On reconfiguration mode, cell function and data flow direction are changed automatically at run time according to contexts. Simultaneously using an H-tree interconnection network, through pre-storing multiple application mapping contexts in reconfiguration buffer, multiple applications can execute concurrently and context switching time is the minimal. For verifying system performance, some algorithms are selected for mapping onto the proposed structure, and the amount of configuration contexts and execution time are recorded for statistical analysis. The results show that the proposed self-reconfigurable mechanism can reduce the number of contexts efficiently, and has a low computing time.
文摘为应对电力系统碳排放计算中效率和精度不足的问题,文章提出一种基于时空图神经网络(spatiotemporal graph neural network,ST-GNN)的数据驱动方法,旨在高效计算节点碳排放因子以及支路碳流和碳流损耗。文章首先分析电力系统碳排放流计算的复杂性及传统方法的局限性,进而设计以有功-无功(active and reactive power,PQ)节点、有功-电压(active power and voltage,PV)节点和平衡节点特征为输入的ST-GNN模型,实现碳排放因子及支路碳流的直接计算,并确定支路碳流损耗。其中PQ节点的特征有功功率和无功功率,来源于电力系统运行数据,PV节点的发电功率和电压来自发电机的运行特性,平衡节点的输入包括电压和相位角,确保系统的功率平衡。通过IEEE 9节点、IEEE 57节点和IEEE118节点系统的实验,验证了所提方法的有效性。结果表明,ST-GNN模型在碳排放因子、支路碳流和碳损耗的计算精度上显著优于线性回归、决策树、长短期记忆网络和多层感知机,特别在复杂电力网络中表现突出。该研究为电力系统碳排放监测和优化提供了精准高效的技术支持。
