A Clone Code Detection Method Based on Software Complex Network

2019 ◽  
Author(s):  
Haoran Guo ◽  
Jun Ai ◽  
Tao Shi
Keyword(s):  
Complex Network ◽  
Detection Method ◽  
Software Complex

The Research and Simulation on Optimization Method for Diverse Network Intrusions

2014 ◽  
Vol 556-562 ◽  
pp. 5740-5743
Author(s):  
Feng Kang ◽  
Xiao Ping Zeng ◽  
Xiao Hui Jiang
Keyword(s):  
Complex Network ◽  
Error Estimation ◽  
Detection Method ◽  
Optimization Method ◽  
Experimental Results ◽  
Classification Methods ◽  
Network Environment ◽  
Estimation Model ◽  
Network Intrusions

In current complex network environment, various kinds of attacks are mixed together forming the attack group. The diversity of the attack group leads to diverse attack signatures, which cannot be constrained by uniform conditions. Based on mixed attack signatures estimation model, the paper proposes a detection method for mixed and diverse attack groups. The paper classifies the different attacks in the mixed and diverse attack groups by use of attack constraint classification methods, builds invasion recognition particle tree, and detects mixed diverse attack groups in complex network environment according to error estimation. Experimental results show that the algorithm can effectively improve the accuracy of detection in complex network environment. atures;

Complex network community detection method by improved density peaks model

2019 ◽  
Vol 526 ◽  
pp. 121070 ◽  
Author(s):  
Zheng-Hong Deng ◽  
Hong-Hai Qiao ◽  
Ming-Yu Gao ◽  
Qun Song ◽  
Li Gao
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Detection Method ◽  
Network Community ◽  
Density Peaks

An algorithm to find critical execution paths of software based on complex network

2015 ◽  
Vol 26 (09) ◽  
pp. 1550101 ◽  
Author(s):  
Guoyan Huang ◽  
Bing Zhang ◽  
Rong Ren ◽  
Jiadong Ren
Keyword(s):  
Complex Network ◽  
Efficient Method ◽  
Software Reliability ◽  
Path Generation ◽  
Similarity Matrix ◽  
Software Complex ◽  
Sources And Sinks ◽  
Execution Path ◽  
Software Execution ◽  
Similarity Matrices

The critical execution paths play an important role in software system in terms of reducing the numbers of test date, detecting the vulnerabilities of software structure and analyzing software reliability. However, there are no efficient methods to discover them so far. Thus in this paper, a complex network-based software algorithm is put forward to find critical execution paths (FCEP) in software execution network. First, by analyzing the number of sources and sinks in FCEP, software execution network is divided into AOE subgraphs, and meanwhile, a Software Execution Network Serialization (SENS) approach is designed to generate execution path set in each AOE subgraph, which not only reduces ring structure's influence on path generation, but also guarantees the nodes' integrity in network. Second, according to a novel path similarity metric, similarity matrix is created to calculate the similarity among sets of path sequences. Third, an efficient method is taken to cluster paths through similarity matrices, and the maximum-length path in each cluster is extracted as the critical execution path. At last, a set of critical execution paths is derived. The experimental results show that the FCEP algorithm is efficient in mining critical execution path under software complex network.

scholarly journals Security Feature Measurement for Frequent Dynamic Execution Paths in Software System

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Qian Wang ◽  
Jiadong Ren ◽  
Xiaoli Yang ◽  
Yongqiang Cheng ◽  
Darryl N. Davis ◽  
...  
Keyword(s):  
Complex Network ◽  
Fault Location ◽  
Attenuation Factor ◽  
Cumulative Effect ◽  
Good Time ◽  
Software System ◽  
Software Complex ◽  
Feature Measurement ◽  
Security Feature ◽  
Dynamic Execution

The scale and complexity of software systems are constantly increasing, imposing new challenges for software fault location and daily maintenance. In this paper, the Security Feature measurement algorithm of Frequent dynamic execution Paths in Software, SFFPS, is proposed to provide a basis for improving the security and reliability of software. First, the dynamic execution of a complex software system is mapped onto a complex network model and sequence model. This, combined with the invocation and dependency relationships between function nodes, fault cumulative effect, and spread effect, can be analyzed. The function node security features of the software complex network are defined and measured according to the degree distribution and global step attenuation factor. Finally, frequent software execution paths are mined and weighted, and security metrics of the frequent paths are obtained and sorted. The experimental results show that SFFPS has good time performance and scalability, and the security features of the important paths in the software can be effectively measured. This study provides a guide for the research of defect propagation, software reliability, and software integration testing.

scholarly journals Multiobjective Brain Storm Optimization Community Detection Method Based on Novelty Search

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoying Pan ◽  
Jia Wang ◽  
Miao Wei ◽  
Hongye Li
Keyword(s):  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Detection Method ◽  
Premature Convergence ◽  
Practical Significance ◽  
Network Community ◽  
Brain Storm Optimization ◽  
Optimal Network ◽  
Novelty Search

A complex network is characterized by community structure, so it is of great theoretical and practical significance to discover hidden functions by detecting the community structure in complex networks. In this paper, a multiobjective brain storm optimization based on novelty search (MOBSO-NS) community detection method is proposed to solve the current issue of premature convergence caused by the loss of diversity in complex network community detection based on multiobjective optimization algorithm and improve the accuracy of community discovery. The proposed method designs a novel search strategy where novelty individuals are first constructed to improve the global search ability, thus avoiding falling into local optimal solutions; then, the objective space is divided into 3 clusters: elite cluster, ordinary cluster, and novel cluster, which are mapped to the decision space, and finally, the populations are disrupted and merged. In addition, the introduction of a restarting strategy is introduced to avoid stagnation by premature convergence. Experimental results show that the algorithm with good global searchability can find the Pareto optimal network community structure set with uniform distribution and high convergence and excavate the network community with higher quality.

scholarly journals Efficient community detection method based on attribution of nodes in complex network

2018 ◽  
Vol 2018 (16) ◽  
pp. 1778-1784
Author(s):  
Biao Cai ◽  
Qiang Sang ◽  
Lina Zeng ◽  
Jiang Wu
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Detection Method
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