Developing an Intelligent System for Efficient Botnet Detection in IoT Environment
Smart technological instruments and Internet of Things (IoT) systems are now targeted by network attacks because of their widespread rising use. Attackers can take over IoT devices via botnets, and pre-configured attack vectors, and use them to do harmful actions. Thus, effective machine learning is...
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| Language: | English |
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Ram Arti Publishers
2025-04-01
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| Series: | International Journal of Mathematical, Engineering and Management Sciences |
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| Online Access: | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/27-IJMEMS-24-0684-10-2-537-553-2025.pdf |
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| _version_ | 1825201921347026944 |
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| author | Ramesh Singh Rawat Manoj Diwakar Umang Garg Prakash Srivastava |
| author_facet | Ramesh Singh Rawat Manoj Diwakar Umang Garg Prakash Srivastava |
| author_sort | Ramesh Singh Rawat |
| collection | DOAJ |
| description | Smart technological instruments and Internet of Things (IoT) systems are now targeted by network attacks because of their widespread rising use. Attackers can take over IoT devices via botnets, and pre-configured attack vectors, and use them to do harmful actions. Thus, effective machine learning is required to solve these security issues. Additionally, deep learning with the necessary elements is advised to defend the network from these threats. In order to achieve proper detection of hacks in the future, relevant datasets must be used. The device's operation could occasionally be delayed. The sample dataset must be well structured for training the model and validating the suggested model to create the best protection system model feasible for detecting cyber risks. This paper focused on analyzing botnet traffic in an IoT environment using machine learning and deep learning classifiers: Decision tree classifier, Naïve Bayes, K nearest neighbor, Convolution neural network, Recurrent neural network, and Random Forest. We calculated each algorithm's Accuracy, True Positive, False Positive, False Negative, True Negative, Precision, and Recall. We obtained impressive results using these CNN, and LSTM RNN classifiers. We have also achieved a high attack detection rate. |
| format | Article |
| id | doaj-art-8045b502cf6848aaa140f4ef6b37b4a9 |
| institution | Kabale University |
| issn | 2455-7749 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Ram Arti Publishers |
| record_format | Article |
| series | International Journal of Mathematical, Engineering and Management Sciences |
| spelling | doaj-art-8045b502cf6848aaa140f4ef6b37b4a92025-02-07T16:30:58ZengRam Arti PublishersInternational Journal of Mathematical, Engineering and Management Sciences2455-77492025-04-01102537553https://doi.org/10.33889/IJMEMS.2025.10.2.027Developing an Intelligent System for Efficient Botnet Detection in IoT EnvironmentRamesh Singh Rawat0Manoj Diwakar1Umang Garg2Prakash Srivastava3Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India.Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India.Computer Science and Engineering, MIT Art Design and Technology University, Pune, India.Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India.Smart technological instruments and Internet of Things (IoT) systems are now targeted by network attacks because of their widespread rising use. Attackers can take over IoT devices via botnets, and pre-configured attack vectors, and use them to do harmful actions. Thus, effective machine learning is required to solve these security issues. Additionally, deep learning with the necessary elements is advised to defend the network from these threats. In order to achieve proper detection of hacks in the future, relevant datasets must be used. The device's operation could occasionally be delayed. The sample dataset must be well structured for training the model and validating the suggested model to create the best protection system model feasible for detecting cyber risks. This paper focused on analyzing botnet traffic in an IoT environment using machine learning and deep learning classifiers: Decision tree classifier, Naïve Bayes, K nearest neighbor, Convolution neural network, Recurrent neural network, and Random Forest. We calculated each algorithm's Accuracy, True Positive, False Positive, False Negative, True Negative, Precision, and Recall. We obtained impressive results using these CNN, and LSTM RNN classifiers. We have also achieved a high attack detection rate.https://www.ijmems.in/cms/storage/app/public/uploads/volumes/27-IJMEMS-24-0684-10-2-537-553-2025.pdfiotiot botnetsecured systemmachine learning |
| spellingShingle | Ramesh Singh Rawat Manoj Diwakar Umang Garg Prakash Srivastava Developing an Intelligent System for Efficient Botnet Detection in IoT Environment International Journal of Mathematical, Engineering and Management Sciences iot iot botnet secured system machine learning |
| title | Developing an Intelligent System for Efficient Botnet Detection in IoT Environment |
| title_full | Developing an Intelligent System for Efficient Botnet Detection in IoT Environment |
| title_fullStr | Developing an Intelligent System for Efficient Botnet Detection in IoT Environment |
| title_full_unstemmed | Developing an Intelligent System for Efficient Botnet Detection in IoT Environment |
| title_short | Developing an Intelligent System for Efficient Botnet Detection in IoT Environment |
| title_sort | developing an intelligent system for efficient botnet detection in iot environment |
| topic | iot iot botnet secured system machine learning |
| url | https://www.ijmems.in/cms/storage/app/public/uploads/volumes/27-IJMEMS-24-0684-10-2-537-553-2025.pdf |
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