Smart City Gnosys
Smart city article details
| Title | A New Design Of Intrusion Detection In Iot Sector Using Optimal Feature Selection And High Ranking-Based Ensemble Learning Model |
|---|---|
| ID_Doc | 3003 |
| Authors | Gopalakrishnan B.; Purusothaman P. |
| Year | 2022 |
| Published | Peer-to-Peer Networking and Applications, 15, 5 |
| DOI | http://dx.doi.org/10.1007/s12083-022-01336-1 |
| Abstract | Internet of Things (IoT) is a significant area in the digital era for the purpose of data collection and transferring them on the network without the help of a human, which shows that the whole world is linked over a single system. It can also be seen as the powerful force that operates modern health systems, home automation, improved manufacturing, and smart cities. IoT increases the chances of cyber threats due to the extensive usage of IoT devices and services. Thus, there is a required to design a robust Intrusion Detection System (IDS) to obtain better network security. The conventional machine learning methods are not optimum for processing complex network data by various intrusion methods. Subsequently, the conventional deep learning approaches in intrusion detection show their efficiency in only one-dimensional feature data, and also they are insufficient for predicting the unknown intrusions. This paper focuses on proposing a novel High Ranking-based Optimized Ensemble Learning Model (HR-OELM) using three different classifiers for developing an intelligent IDS. The first phase is data collection, in which the benchmark datasets are gathered. As the features or attributes associated with the IoT devices from benchmark source datasets are more, it is required to extract the most relevant data that could be highly efficient for attaining the high detection rate. Thus, the accurate feature selection is developed to construct a powerful classification methgods and to decreases the data dimensionality. The major highlight of the optimal feature selection is to decreases the correlation between the features giving unique information. These features are subjected to the proposed HR-OELM, in which the Deep Neural Network (DNN), Random Forest, and Adaboost classifiers are used. The detection performance is finalized based on the high ranking of output from three classifiers. One of the main contributions of the proposed IDS is the development of Adaptive Frequency-based Electric Fish Optimization (AF-EFO) for the optimal feature selection and variable optimization of HR-OELM, thus ensuring superior performance. Finally, the suggested ensemble learning model holds a minimum false-positive range and a maximum detection range than the other conventional traditional methods. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. |
| Author Keywords | Adaboost; Adaptive frequency-based electric fish optimization; Deep neural network; Ensemble learning model; Intrusion detection; IoT security; Random forest |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
23837  | 0.931 | Almotairi A.; Atawneh S.; Khashan O.A.; Khafajah N.M. | Enhancing Intrusion Detection In Iot Networks Using Machine Learning-Based Feature Selection And Ensemble Models | Systems Science and Control Engineering, 12, 1 (2024) |
| 36055 | 0.92 | Ngo V.-D.; Vuong T.-C.; Van Luong T.; Tran H. | Machine Learning-Based Intrusion Detection: Feature Selection Versus Feature Extraction | Cluster Computing, 27, 3 (2024) |
| 57650 | 0.907 | Hazman C.; Guezzaz A.; Benkirane S.; Azrour M. | Toward An Intrusion Detection Model For Iot-Based Smart Environments | Multimedia Tools and Applications, 83, 22 (2024) |
| 8070 | 0.905 | Indra G.; Nirmala E.; Nirmala G.; Senthilvel P.G. | An Ensemble Learning Approach For Intrusion Detection In Iot-Based Smart Cities | Peer-to-Peer Networking and Applications, 17, 6 (2024) |
| 33346 | 0.901 | Berhili M.; Chaieb O.; Benabdellah M. | Intrusion Detection Systems In Iot Based On Machine Learning: A State Of The Art | Procedia Computer Science, 251 (2024) |
| 22935 | 0.9 | Merlin R.T.; Ravi R. | Empowering Smart City Iot Network Intrusion Detection With Advanced Ensemble Learning-Based Feature Selection | International Journal of Electrical and Electronics Research, 12, 2 (2024) |
| 8068 | 0.899 | Hariprasad S.; Deepa T. | An Ensemble Intrusion Detection System Based On Acute Feature Selection | Multimedia Tools and Applications, 83, 3 (2024) |
| 7859 | 0.899 | Thomas L.; Anoop B.K. | An Efficient Iot Based Intrusion Detection System Using Optimization Kernel Extreme Learning Machine | International Journal of Computer Network and Information Security, 17, 2 (2025) |
| 2483 | 0.894 | Nepolo E.; Ngxande M.; Zodi G.-A.L. | A Machine Learning-Based Performance Analysis Of Feature Selection Methods For Anomaly Detection For Iot Network Security | Learning and Analytics in Intelligent Systems, 43 (2025) |
| 3095 | 0.894 | Rafrafi M.; Ghazel C.; Saidane L. | A New Model For Enhancing Iot Security Through Hybrid Optimization Of Intrusion Detection | 2024 13th IFIP/IEEE International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks, PEMWN 2024 (2024) |