# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 5 Experiments and Results  
### Environment Setup  
We have designed two experiments involving different forms of obstacles and moving objects to best collect the noise and other disturbances that IoT devices may face when attempting to profile their neighboring nodes using RSSI observations. In our experiments we have used three Digi XBee 3 Series programmable modules implementing IEEE 802.15.4. [21] (as depicted in Figures 5 and 6), where one device acts as the legitimate temperature reading sensor (denoted by \(s\)) transmitting its reading to the legitimate receiver (denoted by \(r\)) and the adversary (denoted by \(a\)) who spoofs the \(s^{\prime}\) MAC address in the hope of providing false temperature readings to \(r\).  
In the first experiment (refer to Figures 6 and 7), \(s\) is situated in a waterproof container on the lawn outside the house, while \(r\) is situated in the second-floor bedroom. Aside from 5 occupants living on the property that move about the house during the day, outside pedestrians and moving vehicles affect \(s^{\prime}\) RSSI values observed by \(r\). The adversary is free to move about, both inside the property and outside, to carry out its spoofing attack (this is a very generous assumption to highlight a worst case scenario and superiority of our approach. In most settings, there is some degree of physical security that constrains adversaries in their physical positioning). This is an ideal experiment for resembling scenarios where IoT devices are separated by exterior walls and experience some degree of moving objects during the course of their daily operations.  
Figure 5: The legitimate transmitter is situated in the first floor family room while the legitimate receiver is situated in the second floor’s bedroom separated by interior walls and an interior floor. The 5 occupants in the property are considered to be the influencing moving objects.  
Figure 6: The legitimate transmitter is situated outdoors on the lawn transmitting temperature readings and the receiver is situated in the bedroom of the second floor separated by exterior building walls. The pedestrians and motor vehicles in the nearby residential area as well as the 5 occupants in the property are considered to be the influencing moving objects.  
In the second experiment both \(r\) and \(s\) are situated in the property separated by a floor/ceiling and interior walls (depicted in Figure 6) while the adversary is allowed to move about inside and outside of the property. Similar to the first experiment the house occupants have their routine daily schedule of moving around the property during the day and resting (i.e., minimal movement) at right.  
In the second experiment both \(r\) and \(s\) are situated in the property separated by a floor/ceiling and interior walls (depicted in Figure 5) while the adversary is allowed to move about inside and outside of the property. Similar to the first experiment, the house occupants have their routine daily schedule of moving around the property during the day and resting (i.e., minimal movement) at right.  
In both experimental setups, \(r\) starts its training phase by collecting RSSI samples from \(s\) (refer to Figures 8 and 9) both during hours of minimal and significant movements (24 h of capture of RSSI at the sample rate of 1 frame/s)--where these hours are assumed to be empty of any adversarial presence to perturb the training dataset. Once the training stage is completed, \(r\) starts using its two trained LSTM autoencoder models to authenticate received signals and detect MAC-spoofed frames (each LSTM autoencoder has 2 LSTM layers with 20 nodes each and a final dense lake of size 1 and using Adam [22] optimizer for training).  
Figure 8: \(s^{\prime}\) RSSI stream received by \(r\) during \(s^{\prime}\) deployment outside of the property.  
Figure 7: Digi XBee 3 Series programmable module implementing IEEE 802.15.4. in a weatherproof secure enclosure protecting the devices from the elements when deployed.