# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 4 Protection Techniques
### Resilience against Jamming  
One of the key features of LoRaWAN is the robustness against noise and interference, which allows for higher data availability in difficult signal propagation conditions. However, this condition holds only given that the nature of the interference is unintentional,  
Figure 8: Block diagram of secret-key-generation for LoRaWAN, including necessary communication between a node and a gateway. e.g., another (non-LoRaWAN) device signaling on the same RF channel. Conversely, an intentional RF jamming, as discussed in Section 3.3, might cause a serious DoS attack condition as, potentially, access from hundreds of LoRaWAN devices is blocked. Moreover, since LoRaWAN operates on unlicensed bands such the physical layer, DoS attempts might be more difficult to detect in comparison to conventional DoS attacks in IP networks. Hence, research efforts have been devoted to at least three fronts to measure and to improve resilience of LoRaWAN under jamming attacks. * Performance evaluations have been conducted to reveal the network system performance under jamming attacks;
* Detection techniques have been developed to distinguish jamming attacks from e.g., faulty gateway/device operation;
* Improved signaling strategies have been presented, which can improve data throughput in the presence of intentional jamming. In terms of performance evaluations, works studying the impact of jamming have been presented on the physical layer. Firstly, in [69], analytical results were derived for LoRa modulation by considering reactive-band jamming, i.e., noise signals overlapping with a selected part of the LoRaWAN channel bandwidth, and reactive-tone jamming, i.e., jamming with single-/multitone signals. According to the authors, the effect of band jamming was nearly constant, while comparing symbol-error-rate performance for a full-band jammer and for a partial-band jammer. Further, a negligible performance difference was reported between single-tone and multi-tone jammers. As a conclusion, the authors verified the robustness of LoRa signaling against classical noise- and tone-jamming waveforms. Another work [46] studied the effects of jamming with LoRa waveforms with an experimental setup. With a triggered jammer based on a channel-activity-detection algorithm, the authors reported degraded packet delivery ratios (the ratio between the number of correctly received packets and the number of transmitted packets), given that the jamming-waveform power exceeded the legitimate waveform by 6 dB. Another interesting finding concerned a difference in packet-delivery-ratio behaviour while applying constant and triggered jamming. As indicated by the authors, a triggered jamming leads to a worse packet-delivery ratio with less energy overhead. Next to physical layer studies, jamming resilience has also been explored on medium access level. The authors of [70] considered channel-oblivious-jamming attacks and their impact on LoRaWAN-system performance. Given that downlink acknowledgement messages, including payload re-transmissions, are enabled, a network of up to 500 nodes can achieve a reasonably good message-success probability of ca 80% while being attacked on several uplink channels. Despite the built-in robustness against jamming in limited attack scenarios, as discussed above, carefully executed DoS with the potential for multiple jammers could reduce LoRaWAN payload delivery significantly. Hence, efforts have also devoted to various jammer-detection methods. For instance, [22] presented a detection mechanism based on recurrent neural networks, where RSSI and inter-arrival time values were utilized as model inputs. The model training and validation data sets in the study included data from a LoRaWAN simulator and from a real world test bed. A comparison between several activation functions revealed a detection accuracy, measured using F1 score, of between 0.9 and 0.98. Moreover, according to the author, the inter-arrival time contributed the most to the detection accuracy.