A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset

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Akinul Islam Jony
https://orcid.org/0000-0002-2942-6780
Arjun Kumar Bose Arnob
https://orcid.org/0009-0003-2244-2328

Abstract

The growth of Internet of Things (IoT) gadgets has ushered in a new era of connectedness and convenience, but it has also sparked worries about security flaws. Long Short-Term Memory (LSTM) networks are used in this research's use of intrusion detection as a novel strategy to strengthen IoT security. The proposed LSTM-based model excels in detecting both known and evolving cyber-attack patterns with an accuracy rate of 98.75% and an F1 score of 98.59% in extensive experimental evaluations using the vast CIC-IoT2023 dataset, representing a varied array of IoT network traffic scenarios. This research contributes significantly to IoT security while addressing the urgent need for adaptable intrusion detection systems to defend against changing cyber threats. It is an essential step toward ensuring IoT technology's long-term development and dependability in a world that is becoming more interconnected.

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How to Cite
Jony, A.I. and Arnob, A.K.B., 2024. A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset. Journal of Edge Computing [Online], 3(1), pp.28–42. Available from: https://doi.org/10.55056/jec.648 [Accessed 24 July 2024].
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How to Cite

Jony, A.I. and Arnob, A.K.B., 2024. A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset. Journal of Edge Computing [Online], 3(1), pp.28–42. Available from: https://doi.org/10.55056/jec.648 [Accessed 24 July 2024].
Received 2023-10-28
Accepted 2023-12-24
Published 2024-05-21

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