DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment
The Internet of Things (IoT) network infrastructures are becoming more susceptible to distributed denial of service (DDoS) attacks because of the proliferation of IoT devices. Detecting and predicting such attacks in this complex and dynamic environment requires specialized techniques. This study pr...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-09-01
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| Series: | Egyptian Informatics Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524000896 |
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| author | Goda Srinivasa Rao P. Santosh Kumar Patra V.A. Narayana Avala Raji Reddy G.N.V. Vibhav Reddy D. Eshwar |
| author_facet | Goda Srinivasa Rao P. Santosh Kumar Patra V.A. Narayana Avala Raji Reddy G.N.V. Vibhav Reddy D. Eshwar |
| author_sort | Goda Srinivasa Rao |
| collection | DOAJ |
| description | The Internet of Things (IoT) network infrastructures are becoming more susceptible to distributed denial of service (DDoS) attacks because of the proliferation of IoT devices. Detecting and predicting such attacks in this complex and dynamic environment requires specialized techniques. This study presents an approach to detecting and predicting DDoS attacks from a realistic multidimensional dataset specifically tailored to IoT network environments, named DDoSNet. At the beginning of the data preprocessing phase, the dataset must be cleaned up, missing values must be handled, and the data needs to be transformed into an acceptable format for analysis. Several preprocessing approaches, including data-cleaning algorithms and imputation methods, are used to improve the accuracy and dependability of the data. Following this, feature selection uses the African Buffalo Optimization with Decision Tree (ABO-DT) method. This nature-inspired metaheuristic algorithm imitates the behaviour of African buffalos to determine which traits are the most important. By integrating ABO with the decision tree, a subset of features is selected that maximizes the discrimination between regular network traffic and DDoS attacks. After feature selection, an echo-state network (ESN) classifier is employed for detection and prediction. A recurrent neural network (RNN) that has shown potential for managing time-series data is known as an ESN. The ESN classifier utilizes the selected features to learn the underlying patterns and dynamics of network traffic, enabling accurate identification of DDoS attacks. Based on the simulations, the proposed DDOSNet had an accuracy of 98.98 %, a sensitivity of 98.62 %, a specificity of 98.85 %, an F-measure of 98.86 %, a precision of 98.27 %, an MCC of 98.95 %, a Dice coefficient of 98.04 %, and a Jaccard coefficient of 98.09 %, which are better than the current best methods. |
| format | Article |
| id | doaj-art-0a057e9274b34f2a935c66f5a34b358c |
| institution | OA Journals |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-0a057e9274b34f2a935c66f5a34b358c2025-08-20T01:54:45ZengElsevierEgyptian Informatics Journal1110-86652024-09-012710052610.1016/j.eij.2024.100526DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environmentGoda Srinivasa Rao0P. Santosh Kumar Patra1V.A. Narayana2Avala Raji Reddy3G.N.V. Vibhav Reddy4D. Eshwar5Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, IndiaDepartment of Computer Science and Engineering, St. Martin’s Engineering College, Dhulapally, Secunderabad, Telangana, IndiaDepartment of Computer Science and Engineering, CMR College of Engineering and Technology, Hyderabad, Telangana, IndiaCMR Technical Campus, Medchal, Hyderabad, Telangana, IndiaDepartment of Computer Science and Engineering, Sree Dattha Institute of Engineering and Science, Sheriguda, Hyderabad, Telangana, India; Corresponding author.Department of Computer Science and Engineering, KPRIT College of Engineering, Ghatkesar, Hyderabad, Telangana, IndiaThe Internet of Things (IoT) network infrastructures are becoming more susceptible to distributed denial of service (DDoS) attacks because of the proliferation of IoT devices. Detecting and predicting such attacks in this complex and dynamic environment requires specialized techniques. This study presents an approach to detecting and predicting DDoS attacks from a realistic multidimensional dataset specifically tailored to IoT network environments, named DDoSNet. At the beginning of the data preprocessing phase, the dataset must be cleaned up, missing values must be handled, and the data needs to be transformed into an acceptable format for analysis. Several preprocessing approaches, including data-cleaning algorithms and imputation methods, are used to improve the accuracy and dependability of the data. Following this, feature selection uses the African Buffalo Optimization with Decision Tree (ABO-DT) method. This nature-inspired metaheuristic algorithm imitates the behaviour of African buffalos to determine which traits are the most important. By integrating ABO with the decision tree, a subset of features is selected that maximizes the discrimination between regular network traffic and DDoS attacks. After feature selection, an echo-state network (ESN) classifier is employed for detection and prediction. A recurrent neural network (RNN) that has shown potential for managing time-series data is known as an ESN. The ESN classifier utilizes the selected features to learn the underlying patterns and dynamics of network traffic, enabling accurate identification of DDoS attacks. Based on the simulations, the proposed DDOSNet had an accuracy of 98.98 %, a sensitivity of 98.62 %, a specificity of 98.85 %, an F-measure of 98.86 %, a precision of 98.27 %, an MCC of 98.95 %, a Dice coefficient of 98.04 %, and a Jaccard coefficient of 98.09 %, which are better than the current best methods.http://www.sciencedirect.com/science/article/pii/S1110866524000896Distributed denial of serviceInternet of thingsAfrican buffalo optimizationDecision treeFeature selectionEcho state networks |
| spellingShingle | Goda Srinivasa Rao P. Santosh Kumar Patra V.A. Narayana Avala Raji Reddy G.N.V. Vibhav Reddy D. Eshwar DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment Egyptian Informatics Journal Distributed denial of service Internet of things African buffalo optimization Decision tree Feature selection Echo state networks |
| title | DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment |
| title_full | DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment |
| title_fullStr | DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment |
| title_full_unstemmed | DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment |
| title_short | DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment |
| title_sort | ddosnet detection and prediction of ddos attacks from realistic multidimensional dataset in iot network environment |
| topic | Distributed denial of service Internet of things African buffalo optimization Decision tree Feature selection Echo state networks |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524000896 |
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