A long short-term memory based approach for detecting cyber attacks in IoT using CIC-IoT2023 dataset
Akinul Islam Jony, Arjun Kumar Bose Arnob
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.