With the rapid development of the Internet of Things, more and more small devices are connected into the Internet for monitoring and control purposes. One such type of devices, smart plugs, have been extensively deployed worldwide in millions of homes for home automation. These smart plugs, however, would pose serious security problems if their vulnerabilities were not carefully investigated. Indeed, we discovered that some popular smart home plugs have severe security vulnerabilities which could be fixed but unfortunately are left open. In this paper, we case study a smart plug system of a known brand by exploiting its communication protocols and successfully launching four attacks: 1) device scanning attack; 2) brute force attack; 3) spoofing attack; and 4) firmware attack. Our real-world experimental results show that we can obtain the authentication credentials from the users by performing these attacks. We also present guidelines for securing smart plugs.

Law enforcement agencies need the ability to conduct electronic surveillance to combat crime, terrorism, or other malicious activities exploiting the Internet. However, the proliferation of anonymous communication systems on the Internet has posed significant challenges to providing such traceback capability. In this paper, we develop a new class of flow marking technique for invisible traceback based on direct sequence spread spectrum (DSSS), utilizing a pseudo-noise (PN) code. By interfering with a sender's traffic and marginally varying its rate, an investigator can embed a secret spread spectrum signal into the sender's traffic. The embedded signal is carried along with the traffic from the sender to the receiver, so the investigator can recognize the corresponding communication relationship, tracing the messages despite the use of anonymous networks. The secret PN code makes it difficult for others to detect the presence of such embedded signals, so the traceback, while available to investigators is, effectively invisible. We demonstrate a practical flow marking system which requires no training, and can achieve both high detection and low false positive rates. Using a combination of analytical modeling, simulations, and experiments on Tor (a popular Internet anonymous communication system), we demonstrate the effectiveness of the DSSS-basedflow marking technique.

In this paper, we introduce a novel computer vision based attack that automatically discloses inputs on a touch-enabled device while the attacker cannot see any text or popup in a video of the victim tapping on the touch screen. We carefully analyze the shadow formation around the fingertip, apply the optical flow, deformable part-based model (DPM), k-means clustering and other computer vision techniques to automatically locate the touched points. Planar homography is then applied to map the estimated touched points to a reference image of software keyboard keys. Recognition of passwords is extremely challenging given that no language model can be applied to correct estimated touched keys. Our threat model is that a webcam, smartphone or Google Glass is used for stealthy attack in scenarios such as conferences and similar gathering places. We address both cases of tapping with one finger and tapping with multiple fingers and two hands. Extensive experiments were performed to demonstrate the impact of this attack. The per-character (or per-digit) success rate is over 97% while the success rate of recognizing 4-character passcodes is more than 90%. Our work is the first to automatically and blindly recognize random passwords (or passcodes) typed on the touch screen of mobile devices with a very high success rate.

We address issues related to privacy protection in location-based services (LBS). Most existing research in this field either requires a trusted third-party (anonymizer) or uses oblivious protocols that are computationally and communicationally expensive. Our design of privacy-preserving techniques is principled on not requiring a trusted third-party while being highly efficient in terms of time and space complexities. The problem has two interesting and challenging characteristics: First, the degree of privacy protection and LBS accuracy depends on the context, such as population and road density, around a user's location. Second, an adversary may violate a user's location privacy in two ways: (i) based on the user's location information contained in the LBS query payload, and (ii) by inferring a user's geographical location based on its device's IP address. To address these challenges, we introduce CAP, a Context-Aware Privacy-preserving LBS system with integrated protection for data privacy and communication anonymity. We have implemented CAP and integrated it with Google Maps, a popular LBS system. Theoretical analysis and experimental results validate CAP's effectiveness on privacy protection, LBS accuracy, and communication Quality-of-Service.

2D barcodes have enjoyed a significant penetration rate in mobile applications. This is largely due to the extremely low barrier to adoption-almost every camera-enabled smartphone can scan 2D barcodes. As an alternative to NFC technology, 2D barcodes have been increasingly used for security-sensitive mobile applications including mobile payments and personal identification. However, the security of barcode-based communication in mobile applications has not been systematically studied. Due to the visual nature, 2D barcodes are subject to eavesdropping when they are displayed on the smartphone screens. On the other hand, the fundamental design principles of 2D barcodes make it difficult to add security features. In this paper, we propose SBVLC-a secure system for barcode-based visible light communication (VLC) between smartphones. We formally analyze the security of SBVLC based on geometric models and propose physical security enhancement mechanisms for barcode communication by manipulating screen view angles and leveraging user-induced motions. We then develop three secure data exchange schemes that encode information in barcode streams. These schemes are useful in many security-sensitive mobile applications including private information sharing, secure device pairing, and contactless payment. SBVLC is evaluated through extensive experiments on both Android and iOS smartphones.

Various low-latency anonymous communication systems such as Tor and Anonymizer have been designed to provide anonymity service for users. In order to hide the communication of users, most of the anonymity systems pack the application data into equal-sized cells (e.g., 512 B for Tor, a known real-world, circuit-based, low-latency anonymous communication network). Via extensive experiments on Tor, we found that the size of IP packets in the Tor network can be very dynamic because a cell is an application concept and the IP layer may repack cells. Based on this finding, we investigate a new cell-counting-based attack against Tor, which allows the attacker to confirm anonymous communication relationship among users very quickly. In this attack, by marginally varying the number of cells in the target traffic at the malicious exit onion router, the attacker can embed a secret signal into the variation of cell counter of the target traffic. The embedded signal will be carried along with the target traffic and arrive at the malicious entry onion router. Then, an accomplice of the attacker at the malicious entry onion router will detect the embedded signal based on the received cells and confirm the communication relationship among users. We have implemented this attack against Tor, and our experimental data validate its feasibility and effectiveness. There are several unique features of this attack. First, this attack is highly efficient and can confirm very short communication sessions with only tens of cells. Second, this attack is effective, and its detection rate approaches 100% with a very low false positive rate. Third, it is possible to implement the attack in a way that appears to be very difficult for honest participants to detect (e.g., using our hopping-based signal embedding).