Chinese Journal of Network and Information Security

Improved Epidemic algorithm based on trust mechanism in opportunistic networks

Guang-hua ZHANG,Shao-bo PANG,Yao-hong YANG,Zhen-guo CHEN

2017, 3(8): 1-7. doi:10.11959/j.issn.2096-109x.2017.00187

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Aiming at the problem of routing reliability caused by Epidemic algorithm,an improved Epidemic algorithm based on trust mechanism was proposed.Through the establishment of trust mechanism between nodes,this mechanism can provide sufficient trusted nodes as the next hop node of message forwarding,and can promote that the Epidemic algorithm performs the limited scope of flooding under the constraint of trust mechanism.Simulation results and analysis show that the improved Epidemic algorithm can avoid the network congestion caused by flooding mechanism,and the reliability of routing and transmission performance are improved to some extent.

Research on dynamic migration of critical virtual machines for multi-tenancy

Shuo ZHAO,Xin-sheng JI,Guo-zhen CHENG,Yu-xing MAO

2017, 3(8): 8-17. doi:10.11959/j.issn.2096-109x.2017.00191

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A dynamic migration method of critical virtual machines for multi-tenancy was proposed.Firstly,the tenants can set ratio of critical virtual machines according to tenant's security requirements,then the dynamic migration frequency of virtual machines were maximized by the coexistence time constraints between virtual machines,finally the migration cost was minimized as an object function for the virtual machines migrated to further reduce the migration cost.Simulation experiments demonstrate that the proposed approach can reduce the impact on the performance of network services and the cost of virtual machines migration,and is more suitable for large-scale network scenarios under the premise of effective defense side-channel attacks.

Research on parallel coordinate visualization technology based on principal component analysis and K-means clustering

Guo-jun MA,Shui-bo WANG,Qing-qi PEI,Yang ZHAN

2017, 3(8): 18-27. doi:10.11959/j.issn.2096-109x.2017.00189

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In order to solve the problem that parallel coordinate visualization graphic lines are intensive,overlap and rules of data is not easy to be obtained which caused by high dimension and immense amount of multidimensional data.Parallel coordinate visualization method based on principal component analysis and K-means clustering was proposed.In this method,the principal component analysis method was used to reduce the dimensionality of the multidimensional data firstly.Secondly,the data of the dimension reduction was clustered by K-means.Finally,the data of the clustering were visualized by parallel coordinate visualization.The PCAKP visualization method is tested with the data published by the Bureau of Statistics as the test data,and compared with the traditional parallel coordinate visualization graph,the validity and effectiveness of the PCAKP visualization method are verified.

Multilevel secure access control policy for distributed systems

Ming-xin MA,Guo-zhen SHI,Ya-qiong WANG,Hao-jie WANG,Wen-wen CHENG

2017, 3(8): 28-34. doi:10.11959/j.issn.2096-109x.2017.00184

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A multilevel secure access control strategy for distributed system was proposed,which could guarantee the data confidentiality and security,through adding platform and middleware modules.The security policy was described in the XACML language,and the security and flexibility of the policy were analyzed.

Research on the reverse analyses and monitoring data of Mirai malware botnet

Ya-liang CHEN,Qin-yun DAI,Hai-yan WU,Zheng WEI

2017, 3(8): 35-43. doi:10.11959/j.issn.2096-109x.2017.00181

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In recent years,with the rapid development of Internet of things(IoT),malwares have come into Internet of things,and botnet is the typical one.Malwares spreading through IoT vulnerable devices have emerged.The overall structure of Mirai botnet and the function of its components were introduced,such as bots and C&C server.The monitoring data obtained through active and passive way were analyzed.Based on that,the discovery methods and response modes on such kind of botnet malwares were discussed.

Study of high-speed malicious Web page detection system based on two-step classifier

Zheng-qi WANG,Xiao-bing FENG,Chi ZHANG

2017, 3(8): 44-60. doi:10.11959/j.issn.2096-109x.2017.00186

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In view of the increasing number of new Web pages and the increasing pressure of traditional detection methods,the naive Bayesian algorithm and the support vector machine algorithm were used to design and implement a malicious Web detection system with both efficiency and function,TSMWD ,two-step malicious Web page detection.The first step of detection system was mainly used to filter a large number of normal Web pages,which was characterized by high efficiency,speed,update iteration easy,real rate priority.After the former filter,due to the limited number of samples,the main pursuit of the second step was the detection rate.The experimental results show that the proposed scheme can improve the detection speed of the system under the condition that the overall detection accuracy is basically the same,and can accept more detection requests in certain time.

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