Voice assistants (VAs) such as Siri and Google Now have become an increasingly popular human-machine interaction method and have made various systems voice controllable. Prior work on attacking voice assistants shows that the hidden voice commands that are incomprehensible to people can control the VAs. Hidden voice commands, though "hidden", are nonetheless audible. In this work, we design a completely inaudible attack, DolphinAttack, that modulates voice commands on ultrasonic carriers to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated low-frequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the voice assistants. We validate DolphinAttack on popular voice assistants, including Siri, Google Now, S Voice, HiVoice, Cortana, Alexa, etc. By injecting a sequence of inaudible voice commands, we show a few proof-of-concept attacks, which include activating Siri to initiate a FaceTime call on iPhone, activating Google Now to turn on the airplane mode, and even manipulating the navigation system in an Audi automobile. We propose hardware and software defense solutions. We validate that it is feasible to detect DolphinAttack by classifying the audios using supported vector machine (SVM), and suggest to re-design voice assistants to be resilient to inaudible voice command attacks.

With the increasing popularity of voice-based applications, acoustic eavesdropping has become a serious threat to users’ privacy. While on smartphones the access to microphones needs an explicit user permission, acoustic eavesdropping attacks can rely on motion sensors (such as accelerometer and gyroscope), which access is unrestricted. However, previous instances of such attacks can only recognize a limited set of pre-trained words or phrases. In this paper, we present AccEar, an accelerometer-based acoustic eavesdropping attack that can reconstruct any audio played on the smartphone’s loudspeaker with unconstrained vocabulary. We show that an attacker can employ a conditional Generative Adversarial Network (cGAN) to reconstruct high-fidelity audio from low-frequency accelerometer signals. The presented cGAN model learns to recreate high-frequency components of the user’s voice from low-frequency accelerometer signals through spectrogram enhancement. We assess the feasibility and effectiveness of AccEar attack in a thorough set of experiments using audio from 16 public personalities. As shown by the results in both objective and subjective evaluations, AccEar successfully reconstructs user speeches from accelerometer signals in different scenarios including varying sampling rate, audio volume, device model, etc.

Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become common in recent years, incurring huge losses in businesses, governments, financial institutes, health providers, etc. To defeat these attacks, malware classification is employed, which plays an essential role in anti-virus products. However, existing works that employ either static analysis or dynamic analysis have major weaknesses in complicated reverse engineering and time-consuming tasks. In this paper, we propose a visualized malware classification framework called VisMal, which provides highly efficient categorization with acceptable accuracy. VisMal converts malware samples into images and then applies a contrast-limited adaptive histogram equalization algorithm to enhance the similarity between malware image regions in the same family. We provided a proof-of-concept implementation and carried out an extensive evaluation to verify the performance of our framework. The evaluation results indicate that VisMal can classify a malware sample within 4.0 ms and have an average accuracy of 96.0%. Moreover, VisMal provides security engineers with a simple visualization approach to further validate its performance.

As acoustic communication systems become increasingly common in our daily life, eavesdropping brings severe security and privacy risks. Current methods of acoustic eavesdropping either provide low resolution due to the use of sub-6 GHz frequencies, work only for limited words based on classification approaches, or cannot work through-wall because of the use of optical sensors. In this article, we present milliEar , a mmWave acoustic eavesdropping system that leverages the high-resolution of mmWave FMCW ranging and generative machine learning models to not only extract vibrations but to reconstruct the audio. milliEar combines speaker vibration estimation with conditional generative adversarial networks to eavesdrop and recover high-quality audios (i.e., with no vocabulary constraints). We implement and evaluate milliEar using off-the-shelf mmWave radars deployed in different scenarios and settings. Evaluation results clearly show that milliEar can accurately reconstruct the audio even at different distances, angles, and through the wall with different insulator materials. In addition, our subjective and objective evaluations demonstrate that the reconstructed audio has a strong similarity with the original audio.