-
Real-Time Radio Technology and Modulation Classification via an LSTM Auto-Encoder
Identification of the type of communication technology and/or modulation...
read it
-
SenseNet: Deep Learning based Wideband spectrum sensing and modulation classification network
Next generation networks are expected to operate in licensed, shared as ...
read it
-
Scan Correlation – Revealing distributed scan campaigns
Public networks are exposed to port scans from the Internet. Attackers s...
read it
-
Unpaired Point Cloud Completion on Real Scans using Adversarial Training
As 3D scanning solutions become increasingly popular, several deep learn...
read it
-
Adversarial Deep Learning for Over-the-Air Spectrum Poisoning Attacks
An adversarial deep learning approach is presented to launch over-the-ai...
read it
-
Generative Prior Knowledge for Discriminative Classification
We present a novel framework for integrating prior knowledge into discri...
read it
-
Linear Support Vector Regression with Linear Constraints
This paper studies the addition of linear constraints to the Support Vec...
read it
LinksIQ: Robust and Efficient Modulation Recognition with Imperfect Spectrum Scans
While critical for the practical progress of spectrum sharing, modulation recognition has so far been investigated under unrealistic assumptions: (i) a transmitter's bandwidth must be scanned alone and in full, (ii) prior knowledge of the technology must be available and (iii) a transmitter must be trustworthy. In reality these assumptions cannot be readily met, as a transmitter's bandwidth may only be scanned intermittently, partially, or alongside other transmitters, and modulation obfuscation may be introduced by short-lived scans or malicious activity. This paper presents LinksIQ, which bridges the gap between real-world spectrum sensing and the growing body of modrec methods designed under simplifying assumptions. Our key insight is that ordered IQ samples form distinctive patterns across modulations, which persist even with scan deficiencies. We mine these patterns through a Fisher Kernel framework and employ lightweight linear support vector machine for modulation classification. LinksIQ is robust to noise, scan partiality and data biases without utilizing prior knowledge of transmitter technology. Its accuracy consistently outperforms baselines in both simulated and real traces. We evaluate LinksIQ performance in a testbed using two popular SDR platforms, RTL-SDR and USRP. We demonstrate high detection accuracy (i.e. 0.74) even with a 20 RTL-SDR scanning at 50 This constitutes an average of 43 employed on RTL-SDR scans. We also explore the effects of platform-aware classifier training and discuss implications on real-world modrec system design. Our results demonstrate the feasibility of low-cost transmitter fingerprinting at scale.
READ FULL TEXT
Comments
There are no comments yet.