Cite this DOI

KAMEPALLI UMA, K. U. (2022). Using the K-Nearest Neighbour and Moth Blade Optimization Algorithm to Identify Malicious Sessions in IoT Network. *Journal of Science & Technology*, *7*(8), 1–8. https://doi.org/10.46243/jst.2022.v7.i08.pp1-8

@article{kamepalliuma2022using,
  author    = {KAMEPALLI UMA, KAMEPALLI UMA},
  title     = {{Using the K-Nearest Neighbour and Moth Blade Optimization Algorithm to Identify Malicious Sessions in IoT Network}},
  journal   = {Journal of Science \& Technology},
  year      = {2022},
  month     = {oct},
  volume    = {7},
  number    = {8},
  pages     = {1--8},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2022.v7.i08.pp1-8},
  url       = {https://doi.org/10.46243/jst.2022.v7.i08.pp1-8},
  language  = {en},
  abstract  = {There are many ways in which the convenience of an IOT network might improve people's daily lives. Due to the increasing number of potential targets, the security of IoT devices is a pressing issue of the present. In this study, we offer a method for detec ting intrusions into IoT networks, which classifies sessions into either attack or regular categories. Work for slection of characteristics for determining the class representative sessions employed a moth flame optimization genetic method. K-Nearest Neighbor was used to determine which class meeting it was. The experimental results, which were obtained using a real dataset, demonstrate that the suggested model, Moth Flame based IOT Network Security (MFIOTNS), is able to optimise different values of the eva luation parameters to provide greater gains in productivity}
}

TY  - JOUR
TI  - Using the K-Nearest Neighbour and Moth Blade Optimization Algorithm to Identify Malicious Sessions in IoT Network
AU  - KAMEPALLI UMA, KAMEPALLI UMA
JO  - Journal of Science & Technology
PY  - 2022
DA  - 2022/10/27/
VL  - 7
IS  - 8
SP  - 1
EP  - 8
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - There are many ways in which the convenience of an IOT network might improve people's daily lives. Due to the increasing number of potential targets, the security of IoT devices is a pressing issue of the present. In this study, we offer a method for detec ting intrusions into IoT networks, which classifies sessions into either attack or regular categories. Work for slection of characteristics for determining the class representative sessions employed a moth flame optimization genetic method. K-Nearest Neighbor was used to determine which class meeting it was. The experimental results, which were obtained using a real dataset, demonstrate that the suggested model, Moth Flame based IOT Network Security (MFIOTNS), is able to optimise different values of the eva luation parameters to provide greater gains in productivity
DO  - 10.46243/jst.2022.v7.i08.pp1-8
UR  - https://doi.org/10.46243/jst.2022.v7.i08.pp1-8
ER  -

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    "type": "article-journal",
    "id": "10.46243/jst.2022.v7.i08.pp1-8",
    "DOI": "10.46243/jst.2022.v7.i08.pp1-8",
    "URL": "https://doi.org/10.46243/jst.2022.v7.i08.pp1-8",
    "title": "Using the K-Nearest Neighbour and Moth Blade Optimization Algorithm to Identify Malicious Sessions in IoT Network",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
        {
            "family": "KAMEPALLI UMA",
            "given": "KAMEPALLI UMA"
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    "issued": {
        "date-parts": [
            [
                2022,
                10,
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    },
    "volume": "7",
    "issue": "8",
    "page": "1-8",
    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "There are many ways in which the convenience of an IOT network might improve people's daily lives. Due to the increasing number of potential targets, the security of IoT devices is a pressing issue of the present. In this study, we offer a method for detec ting intrusions into IoT networks, which classifies sessions into either attack or regular categories. Work for slection of characteristics for determining the class representative sessions employed a moth flame optimization genetic method. K-Nearest Neighbor was used to determine which class meeting it was. The experimental results, which were obtained using a real dataset, demonstrate that the suggested model, Moth Flame based IOT Network Security (MFIOTNS), is able to optimise different values of the eva luation parameters to provide greater gains in productivity",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2022.v7.i08.pp1-8 gives all four in one JSON answer.