Discovering the “CyberForce” Behind Motion

When I first joined a robotics team, I loved watching our LEGO Mindstorms robot race across the board, turn at just the right moment, and complete missions with almost perfect timing. But a slight fear stayed in my mind: what would happen if a hacker interfered with the robot’s controls? Could a cyberattack change its motion and alter the way it moves, accelerates, or completes a task?

This curiosity became the starting point for my experiment at the intersection between physics and cybersecurity. I wanted to find out whether digital attacks could leave a physical trace, not just in a robot’s code but also in its motion, which I called this effect “CyberForce.”

In physics, a force changes an object’s motion: gravity pulls things downward, and friction slows them down. But in our digital age, robots are not influenced only by physical forces. They also depend on wireless communication, software commands, and sensor feedback. If a hacker disrupts those signals, that interference can act like an invisible hand tugging at the robot’s motion. By “CyberForce,” I do not mean a new physical force like gravity or friction. Instead, I use this term to describe the measurable effect that a cyberattack can have on a moving robotic system. To test this idea, I used a LEGO robot from the FIRST LEGO League (FLL) Superpowered season, in which robots complete energy-themed missions such as transporting, positioning, and releasing objects (FIRST LEGO League 2022).

In an isolated network testing environment, I ran two trials. In the normal run, the robot completed its missions without interference. In the attack run, I used a Kali Linux system to generate a Bluetooth-based disruption called an “L2PING flood,” which interfered with the robot’s control signals (Kimbo, n.d.). I then measured how far and how fast the robot moved under each condition and used those measurements to estimate its velocity, acceleration, and forward force produced. During the attack run, the robot noticeably slowed down, delayed its turns, and sometimes missed the mission targets entirely.

Figure 1 shows Position (cm) vs. Time2 (s2) based on data collection under both normal and cyberattack operations. The slope of the linearized graph representing position vs. time squared is 0.278 centimeters per second squared during normal operation. Given the equation

Δx = 0.5at2 + vt

where a is acceleration (the rate that velocity changes over time), ∆x is the position change, t is time, and v is the initial velocity, the robot’s acceleration during normal operation can be estimated as,

a_normal = 2 × 0.00278 m/s2 = 0.00556 m/s2.

Since force is calculated using Newton’s second law:

F_normal = m × a_normal

where m is the mass of the robot, which is 857 g, the estimated forward force during normal operation is 0.0048N, because

F_normal = 0.857 kg × 0.00556 m/s2 = 0.0048 N.

Using the same method, the estimated forward force during the cyberattack condition was much smaller:

F_attack = 0.857 kg × 0.00074 × 2 m/s2 = 0.00013 N.

Although these numbers are small, the difference between them was large enough to show that digital interference had a measurable physical effect on the robot’s motion. Under normal conditions, the robot moved smoothly and followed predictable paths. During the cyberattack, however, its acceleration dropped sharply: the estimated forward force fell from 0.0048 newtons to just 0.00013 newtons. That drop is what I called “CyberForce.” It was not a new physical force like friction or drag, but it behaved like a new kind of “negative force” opposed to motion that slowed the robot and changed the way it completed its task.

Inspired by a series of labs in my physics class, this small research project helped me see cybersecurity in a new way (Jackson, Dukerich, and Hestenes 2008). Cybersecurity is not only about protecting data but also about protecting how machines move, respond, and behave. In real-world medical or industrial robots, even a slight timing delay caused by a cyberattack could have serious consequences. By modeling “CyberForce” within a physics framework, I connected an abstract cybersecurity concept to something measurable and tangible. A hacker may not physically touch a robot, but this experiment showed that digital interference can still leave a physical mark.