Speech recognition (SR) systems such as Siri or Google Now have become an increasingly popular human-computer interaction method, and have turned various systems into voice controllable systems (VCS). Prior work on attacking VCS shows that the hidden voice commands that are incomprehensible to people can control the systems. 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 (e.g., f > 20 kHz) to achieve inaudibility. By leveraging the nonlinearity of the microphone circuits, the modulated lowfrequency audio commands can be successfully demodulated, recovered, and more importantly interpreted by the speech recognition systems. We validate DolphinAttack on popular speech recognition systems, including Siri, Google Now, Samsung S Voice, Huawei HiVoice, Cortana and Alexa. 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 switch the phone to 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 controllable systems to be resilient to inaudible voice command attacks.

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Background Growing evidence indicated that circRNAs played major roles in the progression of human cancer. Nevertheless, the molecular mechanism and effects of circTMCO3 in GC are still unclear. Methods First, qRT-PCR was used to evaluate the levels of circTMCO3 from GC tissues, GC cells, normal tissues and gastric epithelial cells. Then, the GC cells were transfected to analyze the proliferation, migration and invasion of GC cells by MTT, colony formation and transwell assays. Next, the expressions of miR-577 and RAB14 in GC tissues and cells were examined by qRT-PCR following transfection. The target interaction of circTMCO3-miR-577 and miR-577-RAB14 was explored by the dual-luciferase reporter and RNA pull-down assays. In the end, the growth and viability of GC cells were detected by MTT, colony formation and transwell assays, respectively, following the transfection of GC cells. Results In this research, we found circTMCO3 expressions are significantly up-regulated in GC tissues and cells compared with the normal tissues and gastric epithelial cells. We discovered that the knockdown of circTMCO3 remarkably inhibits the proliferation, migration and invasion of GC cells. Besides, through the prediction of binding sites between circTMCO3, miR-577 and RAB14, we discovered miR-577 is a target of circTMCO3 while RAB14 is a target gene of miR-577. Finally, the results demonstrate the overexpression of miR-577 and the silence of RAB14 could inhibit the effects of circTMCO3 on proliferation, migration and invasion in GC cells. Conclusion circTMCO3 accelerated the growth and migration of GC cells by regulating miR-577/RAB14 axis.

As the core component of the smart grid, advanced metering infrastructure (AMI) is responsible for automated billing, demand response, load forecasting, management, etc. The jamming attack poses a serious threat to the AMI communication networks, especially the neighborhood area network where wireless technologies are widely adopted to connect a tremendous amount of smart meters. An attacker can easily build a jammer using a software-defined radio and jam the wireless communications between smart meters and local controllers, causing failures of on-line monitoring and state estimation. Accurate jammer localization is the first step for defending AMIs against jamming attacks. In this paper, we propose JamCatcher, a mobile jammer localization scheme for defending the AMI. Unlike existing jammer localization schemes, which only consider stationary jammers and usually require a high density of anchor nodes, the proposed scheme utilizes a tracker and can localize a mobile jammer with sparse anchor nodes. The time delay of data transmission is also considered, and the jammer localization process is divided into two stages, i.e., far-field chasing stage and near-field capturing stage. Different localization algorithms are developed for each stage. The proposed method has been tested with data from both simulation and real-world experiment. The results demonstrate that JamCatcher outperforms existing jammer localization algorithms with a limited number of anchor nodes in the AMI scenario.