How to find a GSMem malicious activity via an AI approach
This addresses cybersecurity threats for systems vulnerable to GSMem attacks, but appears incremental as it applies standard AI techniques to a specific detection task.
The paper tackles the problem of detecting GSMem malicious activity by proposing an AI-based method that uses frequency and amplitude data to train models, achieving low false positive and false negative rates in simulated experiments.
This paper investigates the following problem: how to find a GSMem malicious activity effectively. To this end, this paper puts forward a new method based on Artificial Intelligence (AI). At first, we use a large quantity of data in terms of frequencies and amplitudes of some electromagnetic waves to train our models. And then, we input a given frequency and amplitude into the obtained models, predicting that whether a GSMem malicious activity occurs or not. The simulated experiments show that the new method is potential to detect a GSMem one, with low False Positive Rates (FPR) and low False Negative Rates (FNR).