Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (IDS)
In order to cope with the growing complexity of cyber attacks, it is imperative to have efficient intrusion detection systems (IDSs) that can monitor computer resources and produce data on abnormal or suspicious activities. The security of IoT networks is increasingly becoming a crucial concern as t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek
2024-01-01
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| Series: | Tehnički Vjesnik |
| Subjects: | |
| Online Access: | https://hrcak.srce.hr/file/455006 |
| Summary: | In order to cope with the growing complexity of cyber attacks, it is imperative to have efficient intrusion detection systems (IDSs) that can monitor computer resources and produce data on abnormal or suspicious activities. The security of IoT networks is increasingly becoming a crucial concern as the Internet of Things (IoT) technology receives widespread use. Protecting the IoT framework with a conventional Intrusion Detection System (IDS) might be challenging due to the vast quantity and diversity of IoT devices. Traditional Intrusion Detection Systems (IDSs) face limitations when deployed in IoT networks due to resource limitations and the inherent complexity of these networks.This research proposed the Blockchain Assisted Fireworks Optimization with Machine Learning based Intrusion Detection System (BAFWO-MLIDS) technique in the healthcare platform. The major purpose of the BAFWO-MLIDS system is to apply BC technology (BCT) with IDS for enhanced security in the healthcare sector. The BCT enables to achieve secure data transmission in the healthcare platform. The BCT enables to achieve secure data broadcast in the healthcare environment. The BAFWO-MLIDS technique involves a three-stage procedure: FWO based FS process, ENN-based detection, and BO-based parameter optimization. In the proposed BAFWO-MLIDS technique, the FWO-based feature selection process is involved to select optimal features. For intrusion detection, the BAFWO-MLIDS technique uses Elman Neural Network (ENN) model. Finally, the Bayesian optimization (BO) technique is applied to modify the parameters compared with the ENN model and thereby it accomplishes enhanced detection performance. The simulation results of the BAFWO-MLIDS system can be inspected in a series of experiments and the obtained results ensured greater efficiency of the BAFWO-MLIDS methodology with other recent algorithms. |
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| ISSN: | 1330-3651 1848-6339 |