most citedPhysical Backdoor Attack Against Deep Learning-Based Modulation Classification

2 citations · 3 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CR2026

On the Vulnerability of Deep Automatic Modulation Classifiers to Explainable Backdoor Threats

Younes Salmi, Hanna Bogucka

Deep learning (DL) has been widely studied for assisting applications of modern wireless communications. One of the applications is automatic modulation classification (AMC). Howev…

cs.CR20262 cited

Physical Backdoor Attack Against Deep Learning-Based Modulation Classification

Younes Salmi, Hanna Bogucka

Deep Learning (DL) has become a key technology that assists radio frequency (RF) signal classification applications, such as modulation classification. However, the DL models are v…

cs.CR20261 cited

Mitigating Evasion Attacks in Fog Computing Resource Provisioning Through Proactive Hardening

Younes Salmi, Hanna Bogucka

This paper investigates the susceptibility to model integrity attacks that overload virtual machines assigned by the k-means algorithm used for resource provisioning in fog network…

cs.NI2025

Nonlinear symbols combining for Power Amplifier-distorted OFDM signal reception

Pawel Kryszkiewicz, Hanna Bogucka

Nonlinear distortion of a multicarrier signal by a transmitter Power Amplifier (PA) can be a serious problem when designing new highly energy-efficient wireless systems. Although t…

cs.CR2025

An Open-RAN Testbed for Detecting and Mitigating Radio-Access Anomalies

Hanna Bogucka, Marcin Hoffmann, Paweł Kryszkiewicz +1

This paper presents the Open Radio Access Net-work (O-RAN) testbed for secure radio access. We discuss radio-originating attack detection and mitigation methods based on anomaly de…