Predicting Spread Probability of Learning-Effect Computer Virus
With the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6672630 |
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| _version_ | 1832560587439079424 |
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| author | Wei-Chang Yeh Edward Lin Chia-Ling Huang |
| author_facet | Wei-Chang Yeh Edward Lin Chia-Ling Huang |
| author_sort | Wei-Chang Yeh |
| collection | DOAJ |
| description | With the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by exploiting the convenience of the network protocol and the high connectivity of the network. Hence, there is an increasing need for accurate calculation of the probability of computer-virus-infected areas for developing corresponding strategies, for example, based on the possible virus-infected areas, to interrupt the relevant connections between the uninfected and infected computers in time. The spread of the computer virus forms a scale-free network whose node degree follows the power rule. A novel algorithm based on the binary-addition tree algorithm (BAT) is proposed to effectively predict the spread of computer viruses. The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments. |
| format | Article |
| id | doaj-art-2bd3f467ee2947729fdd9e5040e21769 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-2bd3f467ee2947729fdd9e5040e217692025-02-03T01:27:09ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/66726306672630Predicting Spread Probability of Learning-Effect Computer VirusWei-Chang Yeh0Edward Lin1Chia-Ling Huang2Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 300, TaiwanDepartment of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720-1234, USADepartment of International Logistics and Transportation Management, Kainan University, Taoyuan 33857, ChinaWith the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by exploiting the convenience of the network protocol and the high connectivity of the network. Hence, there is an increasing need for accurate calculation of the probability of computer-virus-infected areas for developing corresponding strategies, for example, based on the possible virus-infected areas, to interrupt the relevant connections between the uninfected and infected computers in time. The spread of the computer virus forms a scale-free network whose node degree follows the power rule. A novel algorithm based on the binary-addition tree algorithm (BAT) is proposed to effectively predict the spread of computer viruses. The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments.http://dx.doi.org/10.1155/2021/6672630 |
| spellingShingle | Wei-Chang Yeh Edward Lin Chia-Ling Huang Predicting Spread Probability of Learning-Effect Computer Virus Complexity |
| title | Predicting Spread Probability of Learning-Effect Computer Virus |
| title_full | Predicting Spread Probability of Learning-Effect Computer Virus |
| title_fullStr | Predicting Spread Probability of Learning-Effect Computer Virus |
| title_full_unstemmed | Predicting Spread Probability of Learning-Effect Computer Virus |
| title_short | Predicting Spread Probability of Learning-Effect Computer Virus |
| title_sort | predicting spread probability of learning effect computer virus |
| url | http://dx.doi.org/10.1155/2021/6672630 |
| work_keys_str_mv | AT weichangyeh predictingspreadprobabilityoflearningeffectcomputervirus AT edwardlin predictingspreadprobabilityoflearningeffectcomputervirus AT chialinghuang predictingspreadprobabilityoflearningeffectcomputervirus |