Robust Set Partitioning Strategy for Malicious Information Detection in Large-Scale Internet of Things

TL;DR

This paper introduces a robust set partitioning strategy for malicious information detection in large-scale IoT networks, significantly reducing computational costs while maintaining high detection accuracy.

Contribution

It proposes a Grassmann distance-based set partitioning method that enhances robustness and efficiency in distributed attack detection for IoT systems.

Findings

Limits detection performance gap to 1.648% between distributed and centralized methods.

Reduces computational cost by an order of O(1/m) with the number of subsets.

Maintains detection performance bounds with intrinsic sensor features.

Abstract

With the rapid development of the Internet of Things (IoT), the risks of data tampering and malicious information injection have intensified, making efficient threat detection in large-scale distributed sensor networks a pressing challenge. To address the decline in malicious information detection efficiency as network scale expands, this paper investigates a robust set partitioning strategy and, on this basis, develops a distributed attack detection framework with theoretical guarantees. Specifically, we introduce a gain mutual influence metric to characterize the inter-subset interference arising during gain updates, thereby revealing the fundamental reason for the performance gap between distributed and centralized algorithms. Building on this insight, the set partitioning strategy based on Grassmann distance is proposed, which significantly reduces the computational cost of gain updates while maintaining detection performance, and ensures that the distributed setting under subset partitioning preserves the same theoretical performance bound as the baseline algorithm. Unlike conventional clustering methods, the proposed set partitioning strategy leverages the intrinsic observational features of sensors for robust partitioning, thereby enhancing resilience to noise and interference. Simulation results demonstrate that the proposed method limits the performance gap between distributed and centralized detection to no more than 1.648$\%$, while the computational cost decreases at an order of $O(1/m)$ with the number of subsets $m$. Therefore, the proposed algorithm effectively reduces computational overhead while preserving detection accuracy, offering a practical low-cost and highly reliable security detection solution for edge nodes in large-scale IoT systems.