A Novel Method for Modeling Complex Network of Software System Security

2012 ◽  
Author(s):  
Li Hailin ◽  
Wang Yadi ◽  
Han Jihong
Keyword(s):  
Complex Network ◽  
System Security ◽  
Software System ◽  
Novel Method

A Discovery Method of the Dirty Data Transmission Path Based on Complex Network

2014 ◽  
Vol 651-653 ◽  
pp. 1741-1747
Author(s):  
Xiao Lin Zhao ◽  
Gang Hao ◽  
Chang Zhen Hu ◽  
Zhi Qiang Li
Keyword(s):  
Complex Network ◽  
Data Transmission ◽  
Software System ◽  
Power Law Distribution ◽  
Scale Free ◽  
Transmission Path ◽  
Binary File ◽  
Weighted Directed Graph ◽  
Dirty Data ◽  
Discovery Method

With the increasing scale of software system, the interaction between software elements becomes more and more complex, which lead to the increased dirty data in running software system. This may reduce the system performance and cause system collapse. In this paper, we proposed a discovery method of the dirty data transmission path based on complex network. Firstly, the binary file is decompiled and the function call graph is drawn by using the source code. Then the software structure is described as a weighted directed graph based on the knowledge of complex network. In addition, the dirty data node is marked by using the power-law distribution characteristics of the scale-free network construction of complex network chart. Finally, we found the dirty data transmission path during software running process. The experimental results show the transmission path of dirty data is accurate, which confirmed the feasibility of the method.

Differential Evolution Dynamic Analysis in the Form of Complex Networks

2016 ◽  
pp. 285-318 ◽  
Author(s):  
Lenka Skanderova ◽  
Ivan Zelinka
Keyword(s):  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Complex Networks ◽  
Dynamic Analysis ◽  
Evolutionary Algorithm ◽  
Complex Network ◽  
Novel Method

In this work, we investigate the dynamics of Differential Evolution (DE) using complex networks. In this pursuit, we would like to clarify the term complex network and analyze its properties briefly. This chapter presents a novel method for analysis of the dynamics of evolutionary algorithms in the form of complex networks. We discuss the analogy between individuals in populations in an arbitrary evolutionary algorithm and vertices of a complex network as well as between edges in a complex network and communication between individuals in a population. We also discuss the dynamics of the analysis.

scholarly journals A Novel Method for the Enhancement of Wi-Fi Security Software System

2019 ◽  
Vol 8 (6) ◽  
pp. 905-909
Keyword(s):  
User Interface ◽  
Real Time ◽  
Fog Computing ◽  
Experimental Results ◽  
Wireless Access ◽  
Software System ◽  
Security Software ◽  
Novel Method ◽  
Current Systems ◽  
System Analyzing

This paper proposes a novel method for enhancing current Wi-Fi security software system analyzing user’s wireless access behavior. The system secures the user from security hazards during the pre-connection, connection, and afterconnection phases. The system can analyze and plot the Wi-Fi environment. The methods of fog computing and sending fake traffic are employed to protect PSK from sniffing. In the post connection phase, it identifies De-auth attack in real time and footmarks the attacker. The software functionalities are implemented and all the malicious entities are displayed on the User Interface (UI). The experimental results have shown that the system has better performance when compared with current systems. The system can be used for the security of Wi-Fi users

An Empirical Study on the Network Model and the Online Knowledge Production Structure

2022 ◽  
pp. 599-611
Author(s):  
Quan Chen ◽  
Jiangtao Wang ◽  
Ruiqiu Ou ◽  
Sang-Bing Tsai
Keyword(s):  
Complex Network ◽  
Network Model ◽  
Knowledge Production ◽  
Mass Production ◽  
Network Size ◽  
Software System ◽  
Production Structure ◽  
R Software ◽  
Knowledge Propagation ◽  
New Perspective

Mass production has attracted much attention as a new approach to knowledge production. The R software system is a typical product of mass production. For its unique architecture, the R software system accurately recorded the natural process of knowledge propagation and inheritance. Thus, this article established a dynamic complex network model based on the derivative relationship between R software packages, which reflects the evolution process of online knowledge production structure in R software system, and studied the process of knowledge propagation and inheritance via the dynamic complex network analysis method. These results show that the network size increases with time, reflecting the tendency of R software to accelerate the accumulation of knowledge. The network density and network cohesion decrease with the increase of scale, indicating that the knowledge structure of R software presents a trend of expansion. The unique extension structure of R software provides a rich research foundation for the propagation of knowledge; thus, the results can provide us a new perspective for knowledge discovery and technological innovation.

A new method for Community detection in complex network on the basis of Similarity

2020 ◽  
Vol 13 ◽  
Author(s):  
Munawar Hussain ◽  
Awais Akram
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Evaluation Method ◽  
Structural Similarity ◽  
New Method ◽  
Edge Removal ◽  
Unweighted Network ◽  
Novel Method ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Introduction: Regarding complex network, to find optimal communities in the network has become a key topic in the field of network theory. It is crucial to understand the Structure and functionality of associated networks. In this paper, we propose a new method of community detection that works on the structural similarity of a network (SSN). Method: This method works in two steps, at the first step, it removes edges between the different groups of nodes which are not very similar to each other. As a result of edge removal, the network is divided into many small random communities, which are referred as main communities. Result: In the second step, we apply the evaluation method (EM), it chooses the best quality communities, from all main communities which already produced at the first step. At last, we apply evaluation metrics to our proposed method and benchmarking methods, which show that the SSN method can detect comparatively more accurate results than other methods in this paper. Conclusion: In this article, we proposed a novel method for community detection in networks, called structural similarity of network (SSN). It works in two steps. In the first step, it randomly removes low similarity edges from the network, which makes several small disconnected communities, called as main communities. Afterward, the main communities are merged to search for the final communities, which are near to actual existing communities of the network. Discussion: This approach is defined on the base of the unweighted network, so in Further research it could be used on weighted networks and can explore some new deep-down attributes. Furthermore, it will be used Facebook and twitter weighted data with the artificial intelligence approach.

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