论文标题
在基于RSRP的功能上应用机器学习以进行虚假的基站检测
Applying Machine Learning on RSRP-based Features for False Base Station Detection
论文作者
论文摘要
错误的基站 - IMSI捕手，黄貂鱼 - 是模仿合法基站的设备，是恶意活动的一部分，例如未经授权的监视或通讯破坏性。使用3GPP标准化测量报告在网络侧检测它们是一项有希望的技术。当攻击者使用非法物理细胞标识符（PCI）操作虚假的基站时，应用预定的检测规则效果很好，当一个更具智能的攻击者操作虚假的基站时，使用一个合法的PCIS，通过扫描邻里获得的合法PCIS时，检测将产生错误的负面因素。在本文中，我们展示了如何应用机器学习（ML）来减轻这种虚假负面。我们通过使用NS-3 LTE模块在模拟设置中进行实验来证明我们的方法。我们根据收到的参考信号（RSRP）提出了三个可靠的ML特征（Col，Dist，XY），其中包含的测量报告和细胞位置中包含。我们评估了四个ML模型（回归聚类，异常检测森林，自动编码器和RCGAN），并表明其中一些在检测中也具有很高的检测精度，即使错误的基站使用合法的PCI。在我们的12个单元格的布局的实验中，其中一个细胞充当移动的假单元，最佳模型在75-95 \％之间以0.5 \％的false阳性来检测到虚假位置的75-95 \％。
False base stations -- IMSI catchers, Stingrays -- are devices that impersonate legitimate base stations, as a part of malicious activities like unauthorized surveillance or communication sabotage. Detecting them on the network side using 3GPP standardized measurement reports is a promising technique. While applying predetermined detection rules works well when an attacker operates a false base station with an illegitimate Physical Cell Identifiers (PCI), the detection will produce false negatives when a more resourceful attacker operates the false base station with one of the legitimate PCIs obtained by scanning the neighborhood first. In this paper, we show how Machine Learning (ML) can be applied to alleviate such false negatives. We demonstrate our approach by conducting experiments in a simulation setup using the ns-3 LTE module. We propose three robust ML features (COL, DIST, XY) based on Reference Signal Received Power (RSRP) contained in measurement reports and cell locations. We evaluate four ML models (Regression Clustering, Anomaly Detection Forest, Autoencoder, and RCGAN) and show that several of them have a high precision in detection even when the false base station is using a legitimate PCI. In our experiments with a layout of 12 cells, where one cell acts as a moving false cell, between 75-95\% of the false positions are detected by the best model at a cost of 0.5\% false positives.
