How to find a GSMem malicious activity via an AI approach
Abstract
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).
Cite
@article{arxiv.1801.02440,
title = {How to find a GSMem malicious activity via an AI approach},
author = {WeiJun Zhu and ShaoHuan Ban and YongWen Fan},
journal= {arXiv preprint arXiv:1801.02440},
year = {2018}
}
Comments
4 pages, 4 figures. We'd like to thank arXiv for giving us a chance to correct some typos in previous version of this paper. For examples, the words "FNR" and "FPR" in Fig.4 are marked incorrectly. And they should be replaced with each other. In addition, the experimental result obtained by a BP neural network is another typo in Fig.4. In this version, we check them carefully and correct them