Towards Secure Localization in Randomly Deployed Wireless Networks

TL;DR

This paper introduces a novel secure localization method for randomly deployed wireless networks that effectively detects malicious attackers and accurately localizes devices even in hostile environments with minimal reference points.

Contribution

It proposes a low-complexity clustering-based localization approach that detects attackers and localizes targets without relying on restrictive network topology assumptions.

Findings

Outperforms existing methods in attacker detection accuracy.

Provides closed-form bounds on detection probability.

Validated through simulations demonstrating high effectiveness.

Abstract

Being able to accurately locate wireless devices, while guaranteeing high-level of security against spoofing attacks, benefits all participants in the localization chain (e.g., end users, network operators, and location service providers). On the one hand, most of existing localization systems are designed for innocuous environments, where no malicious adversaries are present. This makes them highly susceptible to security threats coming from interferers, attacks or even unintentional errors (malfunctions) and thus, practically futile in hostile settings. On the other hand, existing secure localization solutions make certain (favorable) assumptions regarding the network topology (e.g., that the target device lies within a convex hull formed by reference points), which restrict their applicability. Therefore, this work addresses the problem of target localization in randomly deployed wireless networks in the presence of malicious attackers, whose goal is to manipulate (spoof) the estimation process and disable accurate localization. We propose a low-complex solution based on clustering and weighted central mass to detect attackers, using only the bare minimum of reference points, after which we solve the localization problem by a bisection procedure. The proposed method is studied from both localization accuracy and success in attacker detection point of views, where closed-form expressions for upper and lower bounds on the probability of attacker detection are derived. Its performance is validated through computer simulations, which corroborate the effectiveness of the proposed scheme, outperforming the state-of-the-art methods.