IPSN 2019: Roberto Calvo-Palomino, Héctor Cordobés, Fabio Ricciato, Domenico Giustiniano, Vincent Lenders

One of the major drawbacks of low-cost spectrum receivers is their limited sampling rate, which does not allow to decode wideband signals. In order to circumvent the hardware limitations of single receivers, we envision a scenario where non-coherent receivers sample the signal collaboratively to cover a larger bandwidth than the one of the single receiver and then, enable the signal reconstruction and decoding in the backend. We present a methodology to enable the signal reconstruction in the backend by multiplexing in frequency a certain number of non-coherent receivers in order to cover a signal bandwidth that would not otherwise be possible using a single receiver. We propose a method that does not use the knowledge of the modulation scheme, and has been designed to be transparent to the subsequent decoding process.

TCCN 2019: Sreeraj Rajendran, Wannes Meert, Vincent Lenders and Sofie Pollin

Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a licensed band to the absence of an expected signal, which makes manual labeling of anomalies difficult and suboptimal. We present, Spectrum Anomaly Detector with Interpretable FEatures (SAIFE), an Adversarial Autoencoder (AAE) based anomaly detector for wireless spectrum anomaly detection using Power Spectral Density (PSD) data. This model achieves an average anomaly detection accuracy above 80% at a constant false alram rate of 1% along with anomaly localization in an unsupervised setting. In addition, we investigate the model’s capabilities to learn interpretable features such as signal bandwidth, class and center frequency in a semi-supervised fashion. Along with anomaly detection the model exhibits promising results for lossy PSD data compression up to 120X and semi-supervised signal classification accuracy close to 100% on three datasets just using 20% labeled samples. Finally the model is tested on data from one of the distributed Electrosense sensors over a long term of 500 hours showing its anomaly detection capabilities.

Opensky Workshop 2019: Matthias Schäfer, Roberto Calvo-Palomino, Franco Minucci, Brecht Reynders, Gérôme Bovet and Vincent Lenders

Receiving signals on the 1090 MHz frequency, one of the most important radio frequencies used in aviation, is typically done using ground-based receivers. However, an increasing number of airborne or even space-based receivers also aim to receive these signals for applications such as air traffic surveillance and collision avoidance. In this paper, we present our results from a high-altitude radio frequency measurement campaign with the goal to gain insights about the challenges and limitations of receiving 1090 MHz signals at high altitudes. We used a high-altitude balloon equipped with a software-defined radio to collect 1090 MHz signal data. In an extensive analysis of these data, we identify several challenges and provide a first impression of the radio environment at altitudes up to 33.5 km.

TCCN 2019: Sreeraj Rajendran, Wannes Meert, Vincent Lenders and Sofie Pollin

Automated wireless spectrum monitoring across frequency, time and space will be essential for many future applications. Manual and fine-grained spectrum analysis is becoming impossible because of the large number of measurement locations and complexity of the spectrum use landscape. Detecting unexpected behaviors in the wireless spectrum from the collected data is a crucial part of this automated monitoring, and the control of detected anomalies is a key functionality to enable interaction between the automated system and the end user. In this paper we look into the wireless spectrum anomaly detection problem for crowdsourced sensors. We first analyze in detail the nature of these anomalies and design effective algorithms to bring the higher dimensional input data to a common feature space across sensors. Anomalies can then be detected as outliers in this feature space. In addition, we investigate the importance of user feedback in the anomaly detection process to improve the performance of unsupervised anomaly detection. Furthermore, schemes for generalizing user feedback across sensors are also developed to close the anomaly detection loop.

Elsevier Computer Networks 2020: Roberto Calvo-Palomino et al.

Web spectrum monitoring systems based on crowdsourcing have recently gained popularity. These systems are however limited to applications of interest for governamental organizations or telecom providers, and only provide aggregated information about spectrum statistics. Theresult is that there is a lack of interest for layman users to participate, which limits its widespreaddeployment. We present Electrosense+ which addresses this challenge and creates a general-purpose and open platform for spectrum monitoring using low-cost, embedded, and software-defined spectrum IoT sensors. Electrosense+ allows users to remotely decode specific parts ofthe radio spectrum. It builds on the centralized architecture of its predecessor, Electrosense, forcontrolling and monitoring the spectrum IoT sensors, but implements a real-time and peer-to-peercommunication system for scalable spectrum data decoding. We propose different mechanismsto incentivize the participation of users for deploying new sensors and keep them operational inthe Electrosense network. As a reward for the user, we propose an incentive accounting systembased on virtual tokens to encourage the participants to host IoT sensors. We present the new Electrosense+ system architecture and evaluate its performance at decoding various wireless sig-nals, including FM radio, AM radio, ADS-B, AIS, LTE, and ACARS.

ACM Mobisys 2020: Brecht Reynders et al.

Given the availability of lightweight radio and processing technology, it becomes feasible to imagine spectrum sensing systems using weather balloons. Such balloons navigate the airspace up to 40 km, and can provide a bird's eye and clear view of terrestrial, as well as aerial spectrum use. In this paper, we present SkySense, which is an extension of the Electrosense sensing framework with mobile GPS-located sensors and local data logging. In addition, we present 6 different sensing campaigns, targeting multiple terrestrial or aerial technologies such as ADS-B, AIS or LTE. For instance, for ADS-B, we can clearly conclude that the number of airplanes that are detected is the same for each balloon altitude, but the message reception rate decreases strongly with altitude because of collisions. For each sensing campaign, the dataset is described, and some example spectrum analysis results are presented. In addition, we analyse and quantify important trends visible when sensing from the sky, such as temperature and hardware variations, increased ambient interference levels, as well as hardware limitations of the lightweight system. A key challenge is the automatic gain control and dynamic range of the system, as a radio navigating over 30km, sees a very wide range of possible signal levels. All data is publicly available through the Electrosense framework, to encourage the spectrum sensing community to further analyse the data or motivate further measurement campaigns using weather balloons.

WoWMoM 2020: Roberto Calvo-Palomino, Arani Bhattacharya, Gérôme Bovet, Domenico Giustiniano

GNSS/GPS is a positioning system widely used nowadays in our lives for real-time localization in Earth. This technology is highly vulnerable to spoofing/jamming attacks caused by malicious intruders. In the recent years, commodity and low-cost radio-frequency hardware have been used to interfere with the legitimate GPS signal. Existing spoofing detection solutions use costly receivers and computationally expensive algorithms which limit the large-scale deployment. In this work we propose a GNSS spoofing detection system that can run on spectrum sensors with Software-Defined Radio (SDR) capabilities and cost in the order of 20 euros. Our approach exploits the predictability of the Doppler characteristics of the received GPS signals to determine the presence of anomalies or malicious attackers. We propose an artificial recurrent neural network (RNN) based on Long short-term memory (LSTM) for anomaly detection. We use data received by low-cost SDR receivers that are processed locally by low-cost embedded machines such as Nvidia Jetson Nano to provide inference capabilities. We show that our solution predicts very accurately the Doppler shift of GNSS signals and can determine the presence of a spoofing transmitter.

Chapter of Springer Book under the NATO Science Series: Domenico Giustiniano, Sofie Pollin, Vincent Lenders.

In the 21st century, the security of the electromagnetic spectrum has tremendous strategic importance to society. In particular, the wireless infrastructure that carries vital services such as 5G cellular networks, communication to aircraft and Global Navigation Satellite System is especially critical. This rapid change is even more impressive considering that in the 80s the only concern for spectrum management was mostly about radio/television broadcasting and military communications. The allocation of spectrum has become over the years more and more complex with different players and stakeholders that depend on largely of their correct operation. However, today, the cost of commodity radio technology prices is so low that access to it is no longer restricted to governments and network operators. It is now affordable to individuals, giving them the potential to become malicious intruders. More frequent and more sophisticated threats from such infiltrators could wreak havoc and are among the most serious challenges faced by society. Unauthorized transmissions could threaten the operation of networks used by air traffic control systems, police, security and emergency services in populated areas. The SOCRATES (Large Scale Collaborative Detection and Location of Threats in the Electromagnetic Space, Grant G5461) project started in June 2018 and aims to deliver a security system to protect our electromagnetic environment and the services and users that depend upon it. SOCRATES will provide an accurate, autonomous, fast and secure system based on a novel and disruptive IoT (Internet of Things) architecture. By detecting and locating unusual RF signal and source activity it will identify intruders in the electromagnetic space, before a threat can become serious, learning about its physical layer features and its geographic location. By providing the capability to detect, identify and locate potential threats to electromagnetic infrastructure security, SOCRATES represents an important step in ensuring society's readiness to respond effectively to them. SOCRATES will shield economic and social structures from those who would harm them. In this contribution we present a summary of published results achieved in the first year of the project, how funds from SOCRATES have foster the collaboration between NATO countries Spain and Belgium and partner country Switzerland, including the activities led by Electrosense as partner country.