English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Location based services are expected to play a major role in futuregeneration cellular networks, starting from the incoming 5G systems. At thesame time, localization technologies may be severely affected by attackerscapable to deploy low cost fake base stations and use them to alterlocalization signals. In this paper, we concretely focus on two classes ofthreats: noise-like jammers, whose objective is to reduce the signal-to-noiseratio, and spoofing/meaconing attacks, whose objective is to inject false orerroneous information into the receiver. Then, we formulate the detectionproblems as binary hypothesis tests and solve them resorting to the generalizedlikelihood ratio test design procedure as well as the Latent Variable Models,which involves the expectation-maximization algorithm to estimate the unknowndata distribution parameters. The proposed techniques can be applied to a largeclass of location data regardless the subsumed network architecture. Theperformance analysis is conducted over simulated data generated by usingmeasurement models from the literature and highlights the effectiveness of theproposed approaches in detecting the aforementioned classes of attacks.

Anomaly Detection Algorithms for Location Security in 5G Scenarios