(Note: This is a fictionalized account of how a journalist may have covered the paper, “Ghostbuster: Detecting the Presence of Hidden
Eavesdroppers” by Anadi Chaman et. al. None of the quotations below are true and must be interpreted as fiction only.)
University of Illinois at Urbana-Champaign
In these days of data breaches and digital spying, the threat of malicious individuals eavesdropping on us is all too real. In recent years, concerns about attackers exploiting the wireless medium have increased, primarily due to the large amount of information wireless signals can leak about the environment.
“In recent years, a few groups have shown that wireless transmissions can be used to infer the location and motion of humans,” says the lead author of the study. “What’s concerning is that these attacks do not require the eavesdropping devices to actively transmit any signals at all, which makes it very difficult to design countermeasures. We have tried to take first steps in that direction through our work by building a system that can successfully identify these passive eavesdroppers.”
In a reference to the popular film series, the researchers call their system Ghostbuster. To identify passive wireless eavesdroppers, the researchers leverage the RF signals all wireless devices leak through their antennas.
“All wireless devices leak wireless signals at the center frequency of their operation,” explains the lead author. “The relative strength of the leakage and its frequency depend on the hardware being used. In most cases, this leakage is extremely weak and is usually undetectable in the presence of noise. We observe the wireless signal over a large time window of 1 second and compute a sample average to detect the presence or absence of the eavesdropper. As noise in electronics is zero mean, computing the average significantly reduces the noise floor and allows us to detect the RF leakage signal.”
In the presence of other wireless signals, the captured signal exhibits artifacts that must be accounted for before the leakage can be detected reliably. The researchers use multiple antennas to spatially separate the signal leakage from actual wireless transmissions, and run a custom algorithm to remove the artifacts before computing the sample average.
“Ghostbuster is able to detect passive eavesdroppers with 95% accuracy even when they are placed up to 5 meters away from the transmitters,” says the principal investigator of the project. “This range can be increased by increasing the signal duration over which the signal averaging is performed.”
The team says multiple extensions to their work are possible. For instance, a current limitation is the system’s inability in differentiating between an eavesdropper’s leakage and that of a legitimate receiver. In addition, the signal averaging operation leads to significant computational overheads, and the team is considering more efficient implementations for future work.
“Although our work is at an early stage right now, we hope our preliminary results will inspire other groups to also work towards making the wireless networks of tomorrow more secure and private,” says the co-principal investigator.
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