Dolphinattack modulates voice commands on ultrasonic carriers (e.g., f > 20 kHz) 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 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.

Wireless cameras are widely deployed in surveillance systems for security guarding. However, the privacy concerns associated with unauthorized videotaping, are drawing an increasing attention recently. Existing detection methods for unauthorized wireless cameras are either limited by their detection accuracy or requiring dedicated devices. In this paper, we propose DeWiCam , a lightweight and effective detection mechanism using smartphones. The basic idea of DeWiCam is to utilize the intrinsic traffic patterns of flows from wireless cameras. Compared with traditional traffic pattern analysis, DeWiCam is more challenging because it cannot access the encrypted information in the data packets. We implement DeWiCam on the Android platform and evaluate it with extensive experiments on 20 cameras. The evaluation results show that DeWiCam can detect cameras with an accuracy of 99% within 2.7 s.

With the proliferation of smartphones, children often use the same smartphones of their parents to play games or surf Internet, and can potentially access kid-unfriendly content from the Internet jungle. A successful parent patrol strategy has to be user-friendly and privacy-aware. The apps that require explicit actions from parents may not be effective when parents forget to enable them, and the ones that use built-in cameras to detect children may impose privacy violations. We propose iCare, which can identify child users automati- cally and seamlessly as users operate smartphones. In particular, iCare investigates the intrinsic differences of screen-touch pat- terns between child and adult users. We discover that users’ touch behaviors depend on a user’s age. Thus, iCare records the touch behaviors and extracts hand-geometry and finger dexterity features that capture the age information.

To improve road safety and driving experiences, autonomous vehicles have emerged recently, and they can sense their surroundings and navigate without human intervention. Although promising and improving safety features, the trustworthiness of these cars has to be examined before they can be widely adopted on the road. Unlike traditional network security, autonomous vehicles rely heavily on their sensory ability of their surroundings to make driving decision, which makes sensors an interface for attacks. Thus, in this project we examine the security of the sensors of autonomous vehicles, and investigate the trustworthiness of the eyes of the cars.

We design a key-free communication method for such devices in a smart home. In particular, we introduce the Home-limited Channel (HLC) that can be accessed only within a house yet inaccessible for an outside-house attacker. Utilizing HLCs, we propose a challenge-response mechanism to authenticate the communications between smart devices without key—HlcAuth. The advantages of HlcAuth are low cost, lightweight as well as key-free, and requiring no human intervention. lcAuth can defeat replay attacks, message-forgery attacks, and man-in-the-middle (MiTM) attacks, among others.

Fake base station (FBS) crime is one typical kind of wireless communication crime which has risen in recent years. The key to enforce the laws on regulating FBS based crime is not only to arrest but also to convict criminals efficiently. To fill in the gap of enforcing the laws on FBS crimes, we design FBSleuth, a FBS crime forensics framework utilizing radio frequency (RF) fingerprints, e.g., the unique characteristics of the FBS transmitters embedded in the electromagnetic signals. In essence, such fingerprints stem from the imperfections of hardware manufacturing and thus represent a consistent bond between an individual FBS device and its committed crime.