Use Spectrum Monitoring to Uncover Warning Signs

Radio frequency (RF) communications continue to bring many wonderful capabilities to the world. Examples range from advances in healthcare treatment and monitoring to connecting people—especially when it comes to critical communications. Yet RF signals can also cause harm or be used with ill intent. By monitoring spectrum, it may be possible to detect suspicious activity before an event occurs.

Sources of harm include improvised explosive devices (IEDs), bombs, and even drones. If an explosive device is triggered by an RF signal, a communications device—usually a cell phone—is used to perform that function. Modifications must be done to the cell phone to enable this capability, but they are difficult to detect. As a result, it is better to monitor spectrum for unusual communications patterns.

Spectrum monitoring also can discern odd user behavior or concerning flight paths in drones. Many drone users are active in today’s world, thanks to the accessibility of these devices. Often, a drone’s presence in an unwanted or restricted area is simply accidental or naïve in intention. If a wandering drone happens to collide with an airplane, however, the outcome could be severe.

Using spectrum monitoring, you can detect, identify, and locate drones. Around airspace, such capabilities prove extremely useful. Other potential risks pertain to drones as well. Drones can do reconnaissance missions on potential targets, for example—studying a building to get a better sense of the layout or monitoring the flow of people coming and going. By detecting a drone, identifying it, and locating it, you could infer more about the drone controller’s intentions.

Given the potential for drones to carry unknown payloads, drone detection also could prevent an illegal or harmful act. Drones can deliver illegal substances, for example, or carry a bomb or chemical for an attack. Keeping in mind that the drone is only a tool used to carry out an act, a key goal in the case of a drone threat is to track down the controller.

Finding the Controller

Sensor-based spectrum monitoring will successfully detect the remote control. In fact, the only way to detect the person on the ground controlling the drone is with an RF fingerprint or search. This process involves setting up a perimeter of sensors and using a geolocation algorithm. You can then look for the pattern or signature made by the remote control in the spectrum. When you see it, trigger all of the sensors to do a geolocation measurement on the pattern.

This process is effective even if the controller is moving, as you can do geolocations once every second or once every couple seconds. You might not see a smooth movement if the person moves quickly, but you can see the controller’s trajectory. At a walking pace, it is easy to detect the signal and follow the person’s path. You continue to do these geolocations every second and plot them on a map to determine the controller’s direction.

This spectrum monitoring approach will not work, however, if the controller does not stay in contact with the drone. During initial setup, it is possible to program a drone with a destination, a task to complete when it arrives, and what to do after it finishes that task. In those cases, you’d have to pick up the controller at the very instant that the mission is being communicated to the drone, which is unlikely. The fallback option would be using acoustic, radar, or other approaches simply to detect the drone’s location.

In all threat scenarios, the best approach to discover and hopefully prevent such an attack is to continuously monitor for anomalies. With continuous monitoring, you can establish typical user behavior in a certain spectrum. Suspicious activity then becomes more obvious, such as a lot of chatter going back and forth between two communications devices that were not in the spectrum previously. This long-term approach demands very reliable equipment that runs 24/7 without needing service or rebooting.

A machine learning approach, in which some artificial intelligence is implemented on the back end, is making such monitoring systems more effective. Spectrum monitoring software logs to a database like SQL, for example. You can see the frequencies that were active and the amplitudes and time of day when the communications occurred. Tools analyze that database over time to determine patterns. In the future, spectrum monitoring systems will be more intelligent, knowing when to mine or look for certain occurrences. They will automatically look for patterns and more easily discern when something unusual could foretell of a threat.

Learn how Keysight’s solutions can help you with your spectrum monitoring needs: