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Next–Generation Intrusion Detection for Iot evcs: Integrating cnn, lstm, and gru modelsBog'liq mathematics-12-005717. Conclusions
Our investigation into the domain of cybersecurity for IoT infrastructures, particularly
focusing on EVCS, culminates with a suite of notable contributions that set a new bench-
mark for IDS. The introduction of an innovative ensemble architecture that leverages the
combined strengths of CNN, LSTM, and GRU, represents a leap forward in the detection of
intricate intrusion patterns. The model, rigorously trained and validated against real-world
datasets, demonstrates a superior ability to navigate the complexities of cyber threats
with impressive accuracy. This study not only demonstrates the feasibility of employing
advanced neural network architectures for intrusion detection but also paves the way for
future research in securing IoT ecosystems against sophisticated attacks.
The advanced data processing techniques and comprehensive performance analysis
employed in this study underscore the depth and rigor of our approach. By achieving high
accuracy across binary, six-class, and fifteen-class classifications, the proposed model is
robust and adaptable to several potential security breaches. The practical implications of
this research extend well beyond theoretical exploration, offering scalable solutions for
real-time applications across various IoT scenarios.
As we lay down the groundwork for future explorations, the proposed model stands
as a benchmark in the field and a touchstone for ensuing innovations in cybersecurity,
inviting the scholarly community to engage with our findings, replicate our success, and
venture further into the untapped potential of DL models. Thus, this study does not signal
a terminus but rather a beacon, illuminating the path toward a more secure and resilient
digital future.
Author Contributions:
This manuscript was designed and written by W.K., D.K. and D.T. W.K.
conceived the main idea of this study. D.K. and D.T. wrote the programs and conducted all the
experiments. W.K., D.K. and D.T. contributed to the analysis and discussion of the algorithms and
results. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was supported by the Basic Science Research Program through the National
Research Foundation of Korea (NRF) (no. NRF2022R1F1A1074767).
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