Recent years have seen the Short Message Service (SMS) become a critical component of the security infrastructure, assisting with tasks including identity verification and second-factor authentication. At the same time, this messaging infrastructure has become dramatically more open and connected to public networks than ever before. However, the implications of this openness, the security practices of benign services, and the malicious misuse of this ecosystem are not well understood. In this article, we provide a comprehensive longitudinal study to answer these questions, analyzing over 900,000 text messages sent to public online SMS gateways over the course of 28 months. From this data, we uncover the geographical distribution of spam messages, study SMS as a transmission medium of malicious content, and find that changes in benign and malicious behaviors in the SMS ecosystem have been minimal during our collection period. The key takeaways of this research show many services sending sensitive security-based messages through an unencrypted medium, implementing low entropy solutions for one-use codes, and behaviors indicating that public gateways are primarily used for evading account creation policies that require verified phone numbers. This latter finding has significant implications for combating phone-verified account fraud and demonstrates that such evasion will continue to be difficult to detect and prevent.

Voice controlled interfaces have vastly improved the usability of many devices (e.g., headless IoT systems). Unfortunately, the lack of authentication for these interfaces has also introduced command injection vulnerabilities - whether via compromised IoT devices, television ads or simply malicious nearby neighbors, causing such devices to perform unauthenticated sensitive commands is relatively easy. We address these weaknesses with Two Microphone Authentication (2MA), which takes advantage of the presence of multiple ambient and personal devices operating in the same area. We develop an embodiment of 2MA that combines approximate localization through Direction of Arrival (DOA) techniques with Robust Audio Hashes (RSHs). Our results show that our 2MA system can localize a source to within a narrow physical cone ($<30^\circ $) with zero false positives, eliminate replay attacks and prevent the injection of inaudible/hidden commands. As such, we dramatically increase the difficulty for an adversary to carry out such attacks and demonstrate that 2MA is an effective means of authenticating and localizing voice commands.

Voice interfaces are increasingly becoming integrated into a variety of Internet of Things (IoT) devices. Such systems can dramatically simplify interactions between users and devices with limited displays. Unfortunately voice interfaces also create new opportunities for exploitation. Specifically any sound-emitting device within range of the system implementing the voice interface (e.g., a smart television, an Internet-connected appliance, etc) can potentially cause these systems to perform operations against the desires of their owners (e.g., unlock doors, make unauthorized purchases, etc). We address this problem by developing a technique to recognize fundamental differences in audio created by humans and electronic speakers. We identify sub-bass over-excitation, or the presence of significant low frequency signals that are outside of the range of human voices but inherent to the design of modern speakers, as a strong differentiator between these two sources. After identifying this phenomenon, we demonstrate its use in preventing adversarial requests, replayed audio, and hidden commands with a 100%/1.72% TPR/FPR in quiet environments. In so doing, we demonstrate that commands injected via nearby audio devices can be effectively removed by voice interfaces.

Text messages sent via the Short Message Service (SMS) have revolutionized interpersonal communication. Recent years have also seen this service become a critical component of the security infrastructure, assisting with tasks including identity verification and second-factor authentication. At the same time, this messaging infrastructure has become dramatically more open and connected to public networks than ever before. However, the implications of this openness, the security practices of benign services, and the malicious misuse of this ecosystem are not well understood. In this paper, we provide the first longitudinal study to answer these questions, analyzing nearly 400,000 text messages sent to public online SMS gateways over the course of 14 months. From this data, we are able to identify not only a range of services sending extremely sensitive plaintext data and implementing low entropy solutions for one-use codes, but also offer insights into the prevalence of SMS spam and behaviors indicating that public gateways are primarily used for evading account creation policies that require verified phone numbers. This latter finding has significant implications for research combatting phone-verified account fraud and demonstrates that such evasion will continue to be difficult to detect and prevent.

Text messaging is used by more people around the world than any other communications technology. As such, it presents a desirable medium for spammers. While this problem has been studied by many researchers over the years, the recent increase in legitimate bulk traffic (e.g., account verification, 2FA, etc.) has dramatically changed the mix of traffic seen in this space, reducing the effectiveness of previous spam classification efforts. This paper demonstrates the performance degradation of those detectors when used on a large-scale corpus of text messages containing both bulk and spam messages. Against our labeled dataset of text messages collected over 14 months, the precision and recall of past classifiers fall to 23.8% and 61.3% respectively. However, using our classification techniques and labeled clusters, precision and recall rise to 100% and 96.8%. We not only show that our collected dataset helps to correct many of the overtraining errors seen in previous studies, but also present insights into a number of current SMS spam campaigns.