With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

中文翻译：

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Betalogger：基于智能手机传感器的侧信道攻击检测和使用语言建模和密集多层神经网络的文本推理

随着过去几年智能手机技术的进步，智能手机的使用量由于其便携性和执行许多日常生活任务的能力而大幅增加。因此，智能手机已成为黑客进行网络攻击的最有价值的目标之一，因为智能手机可能包含个人的敏感数据。智能手机嵌入了高精度传感器。本文提出BetaLogger，一个基于 Android 的应用程序，强调使用智能手机硬件传感器（加速度计、磁力计和陀螺仪）泄露智能手机用户隐私的问题。BetaLogger使用语言建模和密集多层神经网络 (DMNN) 有效地推断智能手机键盘上键入的文本（长或短）。BetaLogger由两个主要阶段组成：在第一阶段，将文本推理向量作为 DMNN 模型的输入，以预测包含字母表的目标标签，在第二阶段，序列生成器模块生成形状为 a 的输出序列连续句。结果表明BetaLogger生成高度准确的短句和长句，与传统的机器学习算法和最先进的研究相比，它有效地提高了推理率。