# A Distributed RF Threat Sensing Architecture
## 2 Related Work
### Jamming Sensing
Finally, in [38], an energy-efficient scheme is proposed for the protection of reactive alarm systems with very low network traffic. The scheme is claimed to be able to identify the cause of bit errors for individual packets, by looking at the RSS during the reception of these bits. Three techniques are presented for the identification of bit errors based on predetermined knowledge, error-correcting codes, and limited node wiring. On the other hand, AI/ML-based scientific work includes implementation of machine learning models, deep learning, and neural networks, all trained with utilization of large datasets that encapsulate several parameters and jamming indexes. Some examples of related work in this area are provided briefly. In [39], the authors focus on jamming detection for 802.11 networks, using metrics that are accessible through standard device drivers, and perform detection via machine learning. The proposed approach allows for both stand-alone operation and cooperative detection, with preliminary tests indicating high detection rates in indoor and mobile outdoor scenarios under challenging link conditions. In [40], the researchers create a large dataset of signal and jamming features and compare the efficiency of several machine learning algorithms after their training. More specifically, they evaluate the performance of random forest, support vector machine, and neural network, using as indicators the probability of detection, probability of false alarm, probability of miss detection, and accuracy. They finally conclude that a random forest algorithm seems to detect RF jammers with a higher accuracy and low probability of false alarms. In [41], the proposed approach extends the state-of-the-art jamming detection and classification methods by implementing a deep learning (DL) model on an IoT edge device. The DL model, which is trained to detect constant and periodic jamming, is built using TensorFlow and deployed on the IoT device using TensorFlow lite. In [42], the authors focus on detecting jamming preemptively, before an increase in the BER (in low BER regimes) and the decrease in the SNR at a level that will completely disrupt the communication link. This method is important for drones and similar-use mobile devices, where communication loss will probably result in critical security and safety issues for the related system and humans. Their approach analyzes raw physical-layer information (I-Q samples) acquired from the wireless channel and converts this information to grayscale images. Then they detect image anomalies caused by jamming attacks, by using sparse autoencoders. Their method, dubbed Bloodbound+, can detect indoor jamming up to 20 m from the jamming source, at the minimum available relative jamming power. In [43], an ML approach is proposed to detect and classify jamming attacks against orthogonal frequency division multiplexing (OFDM) receivers for unmanned aerial vehicle (UAV) applications. The first algorithm utilizes SNR, energy threshold, OFDM parameters to develop a feature-based classification model with ML, while the second collects spectrogram images to build a spectrogram-based classification model via deep learning algorithms. In the same direction, researchers in [44] propose a framework for jamming detection in drone networks, by using a distributed approach based on Multi-layer Perceptron and Decision Tree supervised ML techniques. Their algorithm compares throughput, PDR, and RSS parameters that vary during a jamming attack, with respect to a reference data packet trace set, detecting in this way the anomaly. Summing up, it can be concluded that numerous signal-specific metrics are used for detecting jamming attacks, such as RSS, PDR, Carrier-to-Noise density power ratio, re-transmission attempts, and the packet re-transmission profile. Additionally, other modulation specific metrics can be combined to improve the performance of the proposed detection algorithms, such as OFDM.