Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach (Record no. 640)

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fixed length control field 01425nam a2200169 a 4500
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260901030328.0
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fixed length control field 250101s2020 xx o 000 0 eng d
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name MohammadNoor Injadat
245 10 - TITLE STATEMENT
Title Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Name of producer, publisher, distributor, manufacturer arXiv
Date of production, publication, distribution, manufacture, or copyright notice 2020
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Content type term text
338 ## - CARRIER TYPE
Carrier type term online resource
520 ## - SUMMARY, ETC.
Summary, etc. The increased reliance on the Internet and the corresponding surge in connectivity demand has led to a significant growth in Internet-of-Things (IoT) devices. The continued deployment of IoT devices has in turn led to an increase in network attacks due to the larger number of potential attack surfaces as illustrated by the recent reports that IoT malware attacks increased by 215.7% from 10.3 million in 2017 to 32.7 million in 2018. This illustrates the increased vulnerability and susceptibility of IoT devices and networks. Therefore, there is a need for proper effective and efficient attack detection and mitigation techniques in such environments. Machine learning (ML) has emerged as one potential solution due to the abundance of data generated and available for IoT devices and networks. Hence, they have significant potential to be adopted for intrusion detection for IoT environments. To
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open access — freely available to read.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://arxiv.org/pdf/2012.11325v1">https://arxiv.org/pdf/2012.11325v1</a>
Link text Read the full paper (PDF)
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Electronic resource (link)
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Withdrawn status Lost status Damaged status Not for loan Collection Home library Current library Shelving location Date acquired Total checkouts Barcode Date last seen Uniform resource identifier Price effective from Koha item type
      Available online Cybersecurity Yegates University Library Yegates University Library Science and Computing 09/01/2026   YGE000901 09/01/2026 https://arxiv.org/pdf/2012.11325v1 09/01/2026 Research paper — read online