Intrusion detection in internet of things using improved binary golden jackal optimization algorithm and LSTM

dc.authoridArasteh, Bahman/0000-0001-5202-6315
dc.authoridGhaffari, Ali/0000-0001-5407-8629
dc.authorwosidArasteh, Bahman/AAN-9555-2021
dc.authorwosidGhaffari, Ali/AAV-3651-2020
dc.contributor.authorHanafi, Amir Vafid
dc.contributor.authorGhaffari, Ali
dc.contributor.authorRezaei, Hesam
dc.contributor.authorValipour, Aida
dc.contributor.authorArasteh, Bahman
dc.date.accessioned2024-05-19T14:42:49Z
dc.date.available2024-05-19T14:42:49Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractInternet of things (IoT) technology has gained a reputation in recent years due to its ease of use and adaptability. Due to the amount of sensitive and significant data exchanged over the global Internet, intrusion detection is a challenging task in the vast IoT network. A variety of hostile behaviors and attacks are now detected by intrusion detection systems (IDSs), which are difficult or impossible for a single method to identify. An Improved Binary Golden Jackal Optimization (IBGJO) algorithm and Long Short-Term Memory (LSTM) network are used in this paper to develop a new IDS model for IoT networks. Firstly, the GJO is improved by opposition-based learning (OBL). A binary mode of the improved GJO algorithm is used to select features from IDS data in order to determine the best subset selection. IBGJO uses OBL strategy to improve the performance of the GJO and prevents the algorithm from getting trap in local optima by controlling initial population. LSTM is used in the IBGJO-LSTM model to classify samples. Although high detection rates are achieved by machine learning techniques, the efficiency of these methods decreases with the increase in the size of the dataset. To overcome these problems, deep learning methods are more suitable for distinguishing samples from huge amount of data. The proposed model was assessed using the NSL-KDD and CICIDS2017 datasets. For CICIDS2017 and NSL-KDD, the proposed model was 98.21% accurate. The results show that the recognition accuracy of the proposed model is higher than the models BGJO-LSTM, Binary Whale Optimization Algorithm-LSTM (BWOA-LSTM) and Binary Sine Cosine Algorithm-LSTM (BSCA-LSTM). This is likely because the binary mode of the improved GJO algorithm is able to more effectively select the most relevant features from the IDS data and the LSTM is able to more accurately classify the samples. Also, the proposed model has a significantly higher percentage accuracy than Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB).en_US
dc.identifier.doi10.1007/s10586-023-04102-x
dc.identifier.issn1386-7857
dc.identifier.issn1573-7543
dc.identifier.scopus2-s2.0-85165677122en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.1007/s10586-023-04102-x
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5288
dc.identifier.wosWOS:001036783800002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofCluster Computing-The Journal of Networks Software Tools and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectIntrusion Detection Systemen_US
dc.subjectInternet Of Thingsen_US
dc.subjectGolden Jackal Optimizationen_US
dc.subjectLstmen_US
dc.titleIntrusion detection in internet of things using improved binary golden jackal optimization algorithm and LSTMen_US
dc.typeArticleen_US

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