UGC Approved Journal no 63975(19)

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 9 Issue 9
September-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR2209547


Registration ID:
503021

Page Number

f182-f190

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Title

Insider Threat Detection using Optimal LSTM: A Novel Hybrid Beetle Swarm-based Cuckoo Search Optimization

Abstract

Abstract - Insider threats are the hostile operations intending to do harm which are malevolent by authorized users such as theft of intellectual property or security information, fraud, and sabotage. While insider threats are far less common than external network assaults, they can nevertheless do significant harm. Insiders' harmful conduct is very difficult to detect since they are fully acquainted with an organization's system. Conventional techniques for detecting insider threats rely on rule-oriented techniques developed by domain specialists, but they are neither adaptable nor resilient. Insider-threat detection approaches on the basis of anomaly detection algorithms and user behavior modeling are proposed in this research. Three forms of datasets were created on the basis of user log data: e-mail distribution, daily activity summary, and weekly e-mail history. Further the malicious activities are detected by optimal LSTM, where the hidden neurons of the LSTM are tuned by novel hybrid BS-CSO by merging BSO and CSO with the intention of accuracy maximization. Experiments show that the suggested methodology works effectively for unbalanced datasets with minimal insider risks and absence of domain experts' knowledge.

Key Words

Insider Threat Detection; Optimal Long Short-Term Memory; Beetle Swarm-based Cuckoo Search Optimization; Accuracy Maximization

Cite This Article

"Insider Threat Detection using Optimal LSTM: A Novel Hybrid Beetle Swarm-based Cuckoo Search Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 9, page no.f182-f190, September-2022, Available :http://www.jetir.org/papers/JETIR2209547.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Insider Threat Detection using Optimal LSTM: A Novel Hybrid Beetle Swarm-based Cuckoo Search Optimization", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 9, page no. ppf182-f190, September-2022, Available at : http://www.jetir.org/papers/JETIR2209547.pdf

Publication Details

Published Paper ID: JETIR2209547
Registration ID: 503021
Published In: Volume 9 | Issue 9 | Year September-2022
DOI (Digital Object Identifier):
Page No: f182-f190
Country: Kalapet, puducherry, India .
Area: Science & Technology
ISSN Number: 2349-5162
Publisher: IJ Publication


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