Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning
With various malware, botnets are the legitimate risk increasing against cybersecurity providing criminal operations like malware dispersal, distributed denial of service attacks, fraud clicking, phishing, and identification of theft. Existing techniques used for detection of botnet, which are suita...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-02-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917422002586 |
| _version_ | 1828052001291763712 |
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| author | Sathiyandrakumar Srinivasan Deepalakshmi P |
| author_facet | Sathiyandrakumar Srinivasan Deepalakshmi P |
| author_sort | Sathiyandrakumar Srinivasan |
| collection | DOAJ |
| description | With various malware, botnets are the legitimate risk increasing against cybersecurity providing criminal operations like malware dispersal, distributed denial of service attacks, fraud clicking, phishing, and identification of theft. Existing techniques used for detection of botnet, which are suitable only for specific command of botnet and protocol for controlling and do not support botnet detection at earlier stages. In several computer security defense systems, honeypots are deployed successfully by security defenders. As honeypots can attract botnet compromises and expose spies in botnet membership and behaviors of the attacker, they are broadly employed in botnet defense. Thus, attackers whose role is to construct and maintain botnets have to determine honeypot trap avoiding methods. To handle the issues related to botnet attacks, machine learning techniques are used to support detection and prevent bot attacks. An Ensemble Classifier Algorithm with Stacking Process (ECASP) is proposed in this paper to select optimal features fed as input to the machine learning classifiers to estimate the botnet detection performance. As a result, the method achieves proposed achieves 94.08% accuracy, 86.5% sensitivity, 85.68% specificity, and 78.24% F-measure. |
| first_indexed | 2024-04-10T19:46:59Z |
| format | Article |
| id | doaj.art-0a610d4241af4306981de9d20017deff |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-04-10T19:46:59Z |
| publishDate | 2023-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-0a610d4241af4306981de9d20017deff2023-01-29T04:21:52ZengElsevierMeasurement: Sensors2665-91742023-02-0125100624Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learningSathiyandrakumar Srinivasan0Deepalakshmi P1School of Computing, Kalasalingam Academy of Research and Education, Tamilnadu, IndiaCorresponding author.; School of Computing, Kalasalingam Academy of Research and Education, Tamilnadu, IndiaWith various malware, botnets are the legitimate risk increasing against cybersecurity providing criminal operations like malware dispersal, distributed denial of service attacks, fraud clicking, phishing, and identification of theft. Existing techniques used for detection of botnet, which are suitable only for specific command of botnet and protocol for controlling and do not support botnet detection at earlier stages. In several computer security defense systems, honeypots are deployed successfully by security defenders. As honeypots can attract botnet compromises and expose spies in botnet membership and behaviors of the attacker, they are broadly employed in botnet defense. Thus, attackers whose role is to construct and maintain botnets have to determine honeypot trap avoiding methods. To handle the issues related to botnet attacks, machine learning techniques are used to support detection and prevent bot attacks. An Ensemble Classifier Algorithm with Stacking Process (ECASP) is proposed in this paper to select optimal features fed as input to the machine learning classifiers to estimate the botnet detection performance. As a result, the method achieves proposed achieves 94.08% accuracy, 86.5% sensitivity, 85.68% specificity, and 78.24% F-measure.http://www.sciencedirect.com/science/article/pii/S2665917422002586CyberattacksBotnetsClassificationSecurityFeature extractionMachine learning |
| spellingShingle | Sathiyandrakumar Srinivasan Deepalakshmi P Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning Measurement: Sensors Cyberattacks Botnets Classification Security Feature extraction Machine learning |
| title | Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning |
| title_full | Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning |
| title_fullStr | Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning |
| title_full_unstemmed | Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning |
| title_short | Enhancing the security in cyber-world by detecting the botnets using ensemble classification based machine learning |
| title_sort | enhancing the security in cyber world by detecting the botnets using ensemble classification based machine learning |
| topic | Cyberattacks Botnets Classification Security Feature extraction Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2665917422002586 |
| work_keys_str_mv | AT sathiyandrakumarsrinivasan enhancingthesecurityincyberworldbydetectingthebotnetsusingensembleclassificationbasedmachinelearning AT deepalakshmip enhancingthesecurityincyberworldbydetectingthebotnetsusingensembleclassificationbasedmachinelearning |