A malware propagation prediction model based on representation learning and graph convolutional networks

The traditional malware research is mainly based on its recognition and detection as a breakthrough point,without focusing on its propagation trends or predicting the subsequently infected nodes.The complexity of network structure,diversity of network nodes,and sparsity of data all pose difficulties in predicting propagation.This paper proposes a malware propagation prediction model based on representation learning and Graph Convolutional Networks(GCN)to address the aforementioned problems.First,to solve the problem of the inaccuracy of infection intensity calculation caused by the sparsity of node interaction behavior data in the malware propagation network,a mechanism based on a tensor to mine the infection intensity among nodes is proposed to retain the network structure information.The influence of the relationship between nodes on the infection intensity is also analyzed.Second,given the diversity and complexity of the content and structure of infected and normal nodes in the network,considering the advantages of representation learning in data feature extraction,the corresponding representation learning method is adopted for the characteristics of infection intensity among nodes.This can efficiently calculate the relationship between entities and relationships in low dimensional space to achieve the goal of low dimensional,dense,and real-valued representation learning for the characteristics of propagation spatial data.We also design a new method,Tensor2vec,to learn the potential structural features of malware propagation.Finally,considering the convolution ability of GCN for non-Euclidean data,we propose a dynamic prediction model of malware propagation based on representation learning and GCN to solve the time effectiveness problem of the malware propagation carrier.The experimental results show that the proposed model can effectively predict the behaviors of the nodes in the network and discover the influence of different characteristics of nodes on the malware propagation situation.

ICS-SVM:A user retweet prediction method for hot topics based on improved SVM

In social networks,many complex factors affect the prediction of user forwarding behavior.This paper proposes an improved SVM prediction method for user forwarding behavior of hot topics to improve prediction accuracy.Firstly,we consider that the improved Cuckoo Search algorithm can select the optimal penalty parameters and kernel function parameters to optimize the SVM and thus predict the user's forwarding behavior.Secondly,this paper considers the factors that affect the user forwarding behavior comprehensively from the user's own factors and external factors.Finally,based on the characteristics of the user's forwarding behavior changing over time,the time-slicing method is used to predict the trend of hot topics.Experiments show that the method can accurately predict the user's forwarding behavior and can sense the trend of hot topics.

Application Specified Soft-Error Failure Rate Analysis Using Sequential Equivalence Checking Techniques

Soft errors have become a critical challenge as a result of technology scaling. Existing circuit-hardening techniques are commonly associated with prohibitive overhead of performance, area, and power. However,evaluating the influence of soft errors in Flip-Flops(FFs) on the failure of circuit is a difficult verification problem.Here, we proposed a novel flip-flop soft-error failure rate analysis methodology using a formal method with respect to application behaviors. Approach and optimization techniques to implement the proposed methodology based on the given formula using Sequential Equivalence Checking(SEC) are introduced. The proposed method combines the advantage of formal technique-based approaches in completeness and the advantage of application behaviors in accuracy to differentiate vulnerability of components. As a result, the FFs in a circuit are sorted by their failure rates, and designers can use this information to perform optimal hardening of selected sequential components against soft errors. Experimental results of an implementation of a SpaceWire end node and the largest ISCAS’89 benchmark sequential circuits indicate the feasibility and potential scalability of our approach. A case study on an instruction decoder of a practical 32-bit microprocessor demonstrates the applicability of our method.

Towards functional verifying a family of SystemC TLMs

It is often the case that in the development of a system-on-a-chip(SoC)design,a family of SystemC transaction level models(TLM)is created.TLMs in the same family often share common functionalities but differ in their timing,implementation,configuration and performance in various SoC developing phases.In most cases,all the TLMs in a family must be verified for the follow-up design activities.In our previous work,we proposed to call such family TLM product line(TPL),and proposed feature-oriented(FO)design methodology for efficient TPL development.However,developers can only verify TLM in a family one by one,which causes large portion of duplicated verification overhead.Therefore,in our proposed methodology,functional verification of TPL has become a bottleneck.In this paper,we proposed a novel TPL verification method for FO designs.In our method,for the given property,we can exponentially reduce the number of TLMs to be verified by identifying mutefeature-modules(MFM),which will avoid duplicated veri-fication.The proposed method is presented in informal and formal way,and the correctness of it is proved.The theoretical analysis and experimental results on a real design show the correctness and efficiency of the proposed method.

Towards Connecting Discrete Mathematics and Software Engineering

To enhance training in software development,we argue that students of software engineering should be exposed to software development activities early in the curriculum.This entails meeting the challenge of engaging students in software development before they take the software engineering course.In this paper,we propose a method to connect courses in the software engineering curriculum by setting comprehensive development projects to students in prerequisite courses for software development.Using the Discrete Mathematics(DM)course as an example,we describe the implementation of the proposed method and teaching practices using several practical and comprehensive projects derived from topics in discrete mathematics.Detailed descriptions of the sample projects,their application,and training results are given.Results and lessons learned from applying these practices show that it is a promising way to connect courses in the software engineering curriculum.

Software testing without the oracle correctness assumption

1 Introduction When testing programs,the oracle correctness assumption(OCA)implies that there are no errors in the test oracles,such as the expected outputs are always correctly designed and written in unit testing assertions.Guo's work[1]is the first piece of work that investigated the influences on fault localization caused by errors in test oracles and proposed a method to identify potential incorrect oracles.However,no systematic discussions are availed.