Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT
The Internet of things (IoT) is being used in a variety of industries, including agriculture, the military, smart cities and smart grids, and personalized health care. It is also being used to control critical infrastructure. Nevertheless, because the IoT lacks security procedures and lack the proce...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/8/4699 |
| _version_ | 1827746137472237568 |
|---|---|
| author | Worku Gachena Negera Friedhelm Schwenker Taye Girma Debelee Henock Mulugeta Melaku Degaga Wolde Feyisa |
| author_facet | Worku Gachena Negera Friedhelm Schwenker Taye Girma Debelee Henock Mulugeta Melaku Degaga Wolde Feyisa |
| author_sort | Worku Gachena Negera |
| collection | DOAJ |
| description | The Internet of things (IoT) is being used in a variety of industries, including agriculture, the military, smart cities and smart grids, and personalized health care. It is also being used to control critical infrastructure. Nevertheless, because the IoT lacks security procedures and lack the processing power to execute computationally costly antimalware apps, they are susceptible to malware attacks. In addition, the conventional method by which malware-detection mechanisms identify a threat is through known malware fingerprints stored in their database. However, with the ever-evolving and drastic increase in malware threats in the IoT, it is not enough to have traditional antimalware software in place, which solely defends against known threats. Consequently, in this paper, a lightweight deep learning model for an SDN-enabled IoT framework that leverages the underlying IoT resource-constrained devices by provisioning computing resources to deploy instant protection against botnet malware attacks is proposed. The proposed model can achieve 99% precision, recall, and F1 score and 99.4% accuracy. The execution time of the model is 0.108 milliseconds with 118 KB size and 19,414 parameters. The proposed model can achieve performance with high accuracy while utilizing fewer computational resources and addressing resource-limitation issues. |
| first_indexed | 2024-03-11T05:17:14Z |
| format | Article |
| id | doaj.art-c01ad831e13244cba3087a43fede3ba1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:17:14Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c01ad831e13244cba3087a43fede3ba12023-11-17T18:07:56ZengMDPI AGApplied Sciences2076-34172023-04-01138469910.3390/app13084699Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoTWorku Gachena Negera0Friedhelm Schwenker1Taye Girma Debelee2Henock Mulugeta Melaku3Degaga Wolde Feyisa4Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 445, EthiopiaInstitute of Neural Information Processing, University of Ulm, 89069 Ulm, GermanyEthiopian Artificial Intelligence Institute, Addis Ababa 40782, EthiopiaAddis Ababa Institute of Technology, Addis Ababa University, Addis Ababa 445, EthiopiaEthiopian Artificial Intelligence Institute, Addis Ababa 40782, EthiopiaThe Internet of things (IoT) is being used in a variety of industries, including agriculture, the military, smart cities and smart grids, and personalized health care. It is also being used to control critical infrastructure. Nevertheless, because the IoT lacks security procedures and lack the processing power to execute computationally costly antimalware apps, they are susceptible to malware attacks. In addition, the conventional method by which malware-detection mechanisms identify a threat is through known malware fingerprints stored in their database. However, with the ever-evolving and drastic increase in malware threats in the IoT, it is not enough to have traditional antimalware software in place, which solely defends against known threats. Consequently, in this paper, a lightweight deep learning model for an SDN-enabled IoT framework that leverages the underlying IoT resource-constrained devices by provisioning computing resources to deploy instant protection against botnet malware attacks is proposed. The proposed model can achieve 99% precision, recall, and F1 score and 99.4% accuracy. The execution time of the model is 0.108 milliseconds with 118 KB size and 19,414 parameters. The proposed model can achieve performance with high accuracy while utilizing fewer computational resources and addressing resource-limitation issues.https://www.mdpi.com/2076-3417/13/8/4699botnetIoTSDNSDN-enabled IoT: detectionlightweight modeldeep learning |
| spellingShingle | Worku Gachena Negera Friedhelm Schwenker Taye Girma Debelee Henock Mulugeta Melaku Degaga Wolde Feyisa Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT Applied Sciences botnet IoT SDN SDN-enabled IoT: detection lightweight model deep learning |
| title | Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT |
| title_full | Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT |
| title_fullStr | Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT |
| title_full_unstemmed | Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT |
| title_short | Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT |
| title_sort | lightweight model for botnet attack detection in software defined network orchestrated iot |
| topic | botnet IoT SDN SDN-enabled IoT: detection lightweight model deep learning |
| url | https://www.mdpi.com/2076-3417/13/8/4699 |
| work_keys_str_mv | AT workugachenanegera lightweightmodelforbotnetattackdetectioninsoftwaredefinednetworkorchestratediot AT friedhelmschwenker lightweightmodelforbotnetattackdetectioninsoftwaredefinednetworkorchestratediot AT tayegirmadebelee lightweightmodelforbotnetattackdetectioninsoftwaredefinednetworkorchestratediot AT henockmulugetamelaku lightweightmodelforbotnetattackdetectioninsoftwaredefinednetworkorchestratediot AT degagawoldefeyisa lightweightmodelforbotnetattackdetectioninsoftwaredefinednetworkorchestratediot |