# A Distributed RF Threat Sensing Architecture
## 2 Related Work
### Jamming Sensing  
Being in the era of the vast utilization of wireless networks in everyday typical or critical operations, the obvious challenges communicated to the research community are towards ensuring security and reliability of those systems. The risk of malicious jamming attacks has risen dramatically and for this a number of jamming detection techniques have been proposed. These techniques can be categorized into two main classes: non-machine learning, such as [29; 30; 31; 32; 33; 34; 35; 36; 37; 38], and Artificial Intelligence (AI) machine learning (ML), such as [39; 40; 41; 42; 43; 44]. Non-machine learning methods perform using some parameters and strategies including threshold, fuzzy logic, game theory, channel surfing, mapping jammed region, and timing channel. More specifically, in [29], the authors evaluate the performance of the wireless channels in time-critical applications by applying a threshold-based model that utilizes the packet loss, throughput, and message invalidation ratio. In [30], a Stackelberg game framework is utilized to analyze interactions between a smart jammer and an actual network user. The authors use a variable sampling approach to manage data packets dropout between a sensor and a controller. A time series model is presented in [31], where the state of the communication link is detected by measuring the current state of the link over series of time and compared with the past link data. The authors in [32] propose a fuzzy logic centralized jamming detection technique that calculates a jamming index (JI) to identify the level of jamming effects on a node. The proposed system input is based on the received signal strength, packet delivery ratio, bad packet ratio, and channel clear assessment parameters. In [33], researchers implement a new layer to an NS-2 simulator to support multi-radio multichannel capability. The aim is to increase network capacity and implement anti-jamming models for improving the resiliency of multi-radio networks against jamming attacks using channel hopping and error-correcting code (ECC). In [34], a detection approach for reactive jamming attacks in tactical wireless ad hoc networks is proposed. The authors claim that the proposed mechanism is capable of detecting jamming either by comparing the behavior of the packet delivery rate across the short term and the long term or by observing the required number of re-transmission attempts in the network. Physical layer security is considered in massive MIMO (MaMIMO) systems that experience attacks of a multi-antenna jammer in [35]. The authors propose a jamming detection method that utilizes intentionally unused pilots in the network and calculate a generalized likelihood ratio test over some coherence blocks. The researchers claim that the performance of the detector can be improved by increasing the number of base station antennas, unused pilots, and coherence blocks that are utilized. In [36], the researchers introduce a new class of low-cost detectors that implement an algorithm that is based on the hypothesis that jamming causes correlated changes in all the measured Carrier-to-Noise density power ratio (C/No) values. The decision parameter, which is calculated based on the sum of squared C/No variations, is tested in a real-time platform using a typical GPS receiver and an android device. The work in [37] is focused on detecting jamming attacks in IoT networks. The limited computational resources of IoT networks make it difficult to implement sophisticated and resource-hungry detection algorithms. For this reason, the authors propose a real-time jamming detection mechanism that can identify attacks on multiple channels in 2.4 GHz bandwidth simultaneously, based on the proven hypothesis that, if a jamming attack occurs, the throughput, PDR, delay, and RSSI values will deviate (deteriorate) from the regular state parameters. This is also valid even if the jammer adapts its settings by sensing the effect of jamming in the IoT network.