DDoS Intrusion Detection with Ensemble Stream Mining for IoT Smart Sensing Devices

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dc.contributor.authorGhazal, Taher M.
dc.contributor.authorAl-Dmour, Nidal A.
dc.contributor.authorSaid, Raed A.
dc.contributor.authorOmidvar, Alireza
dc.contributor.authorKhan, Urooj Yousuf
dc.contributor.authorSoomro, Tariq Rahim
dc.contributor.authorSoomro, Tariq Rahim
dc.contributor.authorAlshurideh, Muhammad
dc.contributor.authorAbdellatif, Tamer Mohamed
dc.contributor.authorMoubayed, Abdullah
dc.contributor.authorAli, Liaqat
dc.date.accessioned2023-10-09T09:32:31Z
dc.date.available2023-10-09T09:32:31Z
dc.date.copyright© 2023
dc.date.issued2023
dc.description.abstractSecurity threats in the Smart City Systems are becoming a challenge. These Smart City Systems, generating Big Data, are a revolutionizing application of the Internet of Things(IoT). Data Stream Mining, which is an efficient way of handling Big Data, is now of great concern. The acquired information is computationally expensive to process in terms of efficiency and runtime. Detection of suspicious activities on decentralized servers, generating and computing massive data streams requires time. Moreover, several stakeholders should be engaged to train the heterogenous malware data streams in the level of service application. Small experiments can be performed on the functionality of Batch ML on IoT datasets with available heap size resources. Among these candidate datasets, a little contribution has been already represented on the Mirai Attack. This research aims at the study of Data Stream Mining algorithms. Owing to the accuracy and interferences of the measurement, these algorithms are able to handle the non-hierarchical and unbalanced datasets similar to the Mirai Attacks. No single method can solely improve these critical standpoints. Thus, an Ensemble technique should be implemented. According to our study, a pool of meta or selective classifiers that interact based on the temporal Data Mining swiftly can outperform others. The maintainability and security concerns of such applications can be best fulfilled in meta-heuristics with the one-time scanning network approach for the recognition of the most frequent attacking pattern with the on-the-fly scheme. These are implemented in Create, Read, Update and Delete (CRUD) operations of the Big Data Systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.identifier.citationGhazal, T. M., Al-Dmour, N. A., Said, R. A., Omidvar, A., Khan, U. Y., Soomro, T. R., Alzoubi, H. M., Alshurideh, M., Abdellatif, T. M. & Ali, L. (2023). DDoS Intrusion Detection with Ensemble Stream Mining for IoT Smart Sensing Devices. In M. Alshurideh, B.H. Al Kurdi, R. Masa’deh, H.M. Alzoubi, & S. Salloum (Eds.) The Effect of Information Technology on Business and Marketing Intelligence Systems. Studies in Computational Intelligence, 1056 (pp. 1987-2012). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-12382-5_109
dc.identifier.issn1860949X
dc.identifier.urihttps://doi.org/10.1007/978-3-031-12382-5_109
dc.identifier.urihttps://hdl.handle.net/20.500.12519/881
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofseriesThe Effect of Information Technology on Business and Marketing Intelligence Systems. Studies in Computational Intelligence; Volume 1056
dc.rightsLicense to reuse abstract has been secured and provided by Springer Nature and Copyright Clearance Center.
dc.rights.holderCopyright : © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.subjectAnd internet of things
dc.subjectData stream mining
dc.subjectEnsemble active learning
dc.subjectMirai dataset
dc.subjectPrequential learning
dc.subjectSecurity and privacy
dc.subjectSmart city
dc.subjectWireless sensors
dc.titleDDoS Intrusion Detection with Ensemble Stream Mining for IoT Smart Sensing Devices
dc.typeBook chapter

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