Smart City Gnosys
Smart city article details
| Title | A Novel Metaheuristics With Deep Learning Enabled Intrusion Detection System For Secured Smart Environment |
|---|---|
| ID_Doc | 3440 |
| Authors | Malibari A.A.; Alotaibi S.S.; Alshahrani R.; Dhahbi S.; Alabdan R.; Al-wesabi F.N.; Hilal A.M. |
| Year | 2022 |
| Published | Sustainable Energy Technologies and Assessments, 52 |
| DOI | http://dx.doi.org/10.1016/j.seta.2022.102312 |
| Abstract | With the deep integration of Internet and ubiquitous computing, smart environment becomes familiar in several real time applications such as smart city, smart building, healthcare, transportation, etc. Despite the benefits of smart environment, security remains a challenging issue which needs to be resolved. Intrusion detection system (IDS) acts as a vital part in assuring data security and the main technology is to precisely detect distinct kinds of attacks in the network. Since the networking data comprises of huge number of features, feature selection techniques can be integrated into the IDS for enhanced security. In this view, this article introduces a novel metaheuristic with deep learning enabled intrusion detection system for secured smart environment, named MDLIDS-SSE technique. The major intention of the MDLIDS-SSE technique is to identify the existence of intrusions in the secured smart environment. The MDLIDS-SSE technique employs Z-score normalization approach as a data pre-processing step. In addition, the MDLIDS-SSE technique enables improved arithmetic optimization algorithm based feature selection (IAOA-FS) technique to elect an optimal subset of features. Moreover, quantum behaved particle swarm optimization (QPSO) with deep wavelet neural network (DWNN) model is employed for the detection and classification of intrusions in the secured smart environment. The utilization of the QPSO algorithm assists in the optimal choice of the variables involved in the DWNN model. For assessing the enhanced intrusion detection outcomes of the MDLIDS-SSE technique, a series of simulations were carried out and the results are examined under various aspects. The comparative result analysis reported the enhanced outcomes of the MDLIDS-SSE technique over the recent approaches. © 2022 Elsevier Ltd |
| Author Keywords | Deep learning; Feature subset selection; Intrusion detection system; Security; Smart environment; Ubiquitous computing |
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