Smart Approach for Botnet Detection Based on Network Traffic Analysis
Today, botnets are the most common threat on the Internet and are used as the main attack vector against individuals and businesses. Cybercriminals have exploited botnets for many illegal activities, including click fraud, DDOS attacks, and spam production. In this article, we suggest a method for i...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/3073932 |
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| _version_ | 1832552476815917056 |
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| author | Alaa Obeidat Rola Yaqbeh |
| author_facet | Alaa Obeidat Rola Yaqbeh |
| author_sort | Alaa Obeidat |
| collection | DOAJ |
| description | Today, botnets are the most common threat on the Internet and are used as the main attack vector against individuals and businesses. Cybercriminals have exploited botnets for many illegal activities, including click fraud, DDOS attacks, and spam production. In this article, we suggest a method for identifying the behavior of data traffic using machine learning classifiers including genetic algorithm to detect botnet activities. By categorizing behavior based on time slots, we investigate the viability of detecting botnet behavior without seeing a whole network data flow. We also evaluate the efficacy of two well-known classification methods with reference to this data. We demonstrate experimentally, using existing datasets, that it is possible to detect botnet activities with high precision. |
| format | Article |
| id | doaj-art-03445c72515d475997ca960f5bc95171 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-03445c72515d475997ca960f5bc951712025-02-03T05:58:32ZengWileyJournal of Electrical and Computer Engineering2090-01552022-01-01202210.1155/2022/3073932Smart Approach for Botnet Detection Based on Network Traffic AnalysisAlaa Obeidat0Rola Yaqbeh1Basic Sciences DepartmentNursing FacultyToday, botnets are the most common threat on the Internet and are used as the main attack vector against individuals and businesses. Cybercriminals have exploited botnets for many illegal activities, including click fraud, DDOS attacks, and spam production. In this article, we suggest a method for identifying the behavior of data traffic using machine learning classifiers including genetic algorithm to detect botnet activities. By categorizing behavior based on time slots, we investigate the viability of detecting botnet behavior without seeing a whole network data flow. We also evaluate the efficacy of two well-known classification methods with reference to this data. We demonstrate experimentally, using existing datasets, that it is possible to detect botnet activities with high precision.http://dx.doi.org/10.1155/2022/3073932 |
| spellingShingle | Alaa Obeidat Rola Yaqbeh Smart Approach for Botnet Detection Based on Network Traffic Analysis Journal of Electrical and Computer Engineering |
| title | Smart Approach for Botnet Detection Based on Network Traffic Analysis |
| title_full | Smart Approach for Botnet Detection Based on Network Traffic Analysis |
| title_fullStr | Smart Approach for Botnet Detection Based on Network Traffic Analysis |
| title_full_unstemmed | Smart Approach for Botnet Detection Based on Network Traffic Analysis |
| title_short | Smart Approach for Botnet Detection Based on Network Traffic Analysis |
| title_sort | smart approach for botnet detection based on network traffic analysis |
| url | http://dx.doi.org/10.1155/2022/3073932 |
| work_keys_str_mv | AT alaaobeidat smartapproachforbotnetdetectionbasedonnetworktrafficanalysis AT rolayaqbeh smartapproachforbotnetdetectionbasedonnetworktrafficanalysis |