Neural Representation Models for Intelligent Threat Identification in IoT Networks

DOI: https://doie.org/10.10399/JBSE.2025458054

Thanuja Narasimhamurthy, Gunavathi Hosahalli Swamy

Keywords:

Internet-of-Things, Machine Learning, Dynamic Threats, Neural Network, Performance Index.

Abstract:

Machine Learning is one of the potential solutions towards optimizing the security strength towards identifying complex forms of threats in Internet-of-Things (IoT). However, a review of existing machine learning-based approaches showcases their sub-optimal performance when exposed to dynamic forms of unseen threats without any apriori information during the training stage. Hence, this manuscript presents a novel machine-learning framework towards potential threat detection capable of identifying the underlying patterns of rapidly evolving threats. The proposed system uses a neural network-based learning model emphasizing representation learning where an explicit masked indexing mechanism is presented for high-level security against unknown and dynamic adversaries. The benchmarked outcome of the study shows to accomplish 11% maximized threat detection accuracy and 33% minimized algorithm processing time.

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