Interpretable intrusion detection for IoT environments using a self-attention-based explainable AI framework

Abstract The rapid growth of Internet of Things (IoT) devices has introduced critical cybersecurity challenges due to the large attack surface and diverse interconnected systems. This work proposes a novel self-attention deep neural network (SA-DNN) augmented with a learnable feature gating (LFG) me...

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Ngā kaituhi matua:	Kanta Prasad Sharma, Tapsi Nagpal, Tarak Vora, Anupam Yadav, Muhammad Irsyad Abdullah, B. Jayaprakash, Aditya Kashyap, G. Sridevi, A. Bhowmik, Bethelehem Burju Bukate
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Nature Portfolio 2025-11-01
Rangatū:	Scientific Reports
Ngā marau:	Self-attention mechanism Deep neural networks Explainable artificial intelligence Internet of things IoT intrusion detection Network security
Urunga tuihono:	https://doi.org/10.1038/s41598-025-23750-0
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Interpretable intrusion detection for IoT environments using a self-attention-based explainable AI framework

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