Recently, there has been a worldwide surge in SMS phishing, aka smishing. However, the lack of open-access updated datasets makes it challenging for researchers to study this global issue. Mobile network operators and government agencies provide users special SMS spam reporting services. Though, these services are regional and users are largely unaware. So, users often turn to public forums such as Twitter or Reddit to report and discuss smishing. This paper presents a novel methodological approach to collect an updated smishing dataset and measure the infrastructure, targets, and strategies employed by attackers to lure victims. We programmatically collect users' smishing reports from five public forums, collating over 64.5k smishing image attachments and reports, which include 28.6k sender IDs and 25.9k URLs criminals abuse to conduct smishing campaigns across 66 languages. We unveil the exploited infrastructure ranging from mobile network operators to domains. We categorize smishing texts into seven scam types and explain lures criminals use to deceive victims into providing sensitive/financial information. Through a case study using real time measurements on a random sample of Twitter posts, we showcase how to uncover Android malware spread via smishing. We suggest effective mitigation approaches to curb this widespread cybercrime.

Smishing or SMS phishing is a recent update to email-based phishing. This modern scam hinges upon the trust that users have in their bank or online service to steal users’ personal details. While recent work examines these texts and the URLs sent, no work has empirically determined what happens after scammers obtain this credit card information. Card-not-present (CNP) fraud—where stolen card details are used to make purchases online without physical access to the card—has become a growing concern. While some investigate this indirectly using forum posts, the unavailability of credit card transaction data makes it tricky to study empirically. As smishing continues to rise, so does CNP fraud, resulting in more losses borne by consumers. To this end, we perform a proof-of-concept experiment towards understanding how criminals abuse stolen credit card details brought in from smishing. We collaborate with a mobile network operator and a financial institution to access live smishing URLs and test credit cards. We provide test credit cards to twelve different smishing URLs and observe 36 authorization attempts across 17 different online merchants. We analyze the ISO transaction messages to uncover scammers’ transaction patterns and their cash-out mechanisms. Our insights into scammer behavior could help stakeholders develop effective mitigations to tackle CNP fraud towards eliminating the profitability of smishing.

SMS scams have surged over the recent years. However, little empirical research has been done to understand this rising threat due to the lack of an updated dataset. In the UK, mobile network operators run a firewall to block illicit messages. To this end, we collaborate with a major UK mobile network operator, which provides us with 3.58m SMS messages flagged by their firewall. These messages originated from over 42k unique sender IDs and were sent to 2.23m mobile numbers between December 2023 and February 2024. This is the first research to examine the current threats in the SMS ecosystem and categorize illicit SMS messages into eight sectors, including spam. We present the distribution of SMS messages successfully blocked by the mobile network operator's firewall and those that successfully evade detection.

Our perception of reality is under constant threat from ever-improving video manipulation techniques, including deep-fakes and generative AI. Therefore, proving authenticity of videos is increasingly important, especially in legal and news contexts. However, it is very challenging to prove it based on post-factum video content analysis.

Enterprises increasingly use public cloud services for critical business needs. However, Internet protocols force clouds to contend with a lack of control, reducing the speed at which clouds can respond to network problems, the range of solutions they can provide, and deployment resilience. To overcome this limitation, we present PAINTER, a system that takes control over which ingress routes are available and which are chosen to the cloud by leveraging edge proxies. PAINTER efficiently advertises BGP prefixes, exposing more concurrent routes than existing solutions to improve latency and resilience. Compared to existing solutions, PAINTER reduces path inflation by 75% while using a third of the prefixes of other solutions, avoids 20% more path failures, and chooses ingresses from the edge at finer time (RTT) and traffic (per-flow) granularities, enhancing our agility.

Demonstrating veracity of videos is a longstanding problem that has recently become more urgent and acute. It is extremely hard to accurately detect manipulated videos using content analysis, especially in the face of subtle, yet effective, manipulations, such as frame rate changes or skin tone adjustments.

An increasing number of mobile devices are incorporating cameras, allowing users to record videos at any time, anywhere. This opens up a wide variety of applications, most notably security-critical ones, where videos are used as evidence or include sensitive content. Examples of such applications include (but are not limited to): (i) citizen journalists recording important events (e.g., protests), (ii) courts using videos as evidence, and (iii) electronic legal contract-signing platforms using videos to identify signing users [1].

An increasing number of mobile devices are incorporating cameras, allowing users to record videos at any time, anywhere. This opens up a wide variety of applications, most notably security-critical ones, where videos are used as evidence or include sensitive content. Examples of such applications include (but are not limited to): (i) citizen journalists recording important events (e.g., protests), (ii) courts using videos as evidence, and (iii) electronic legal contract-signing platforms using videos to identify signing users [1].

In addition to consumer workloads, public cloud providers host enterprise workloads such as video conferencing and AI+ML pipelines. Enterprise workloads can, at times, overwhelm the available ingress capacity on individual peering links. Traditional techniques to address this problem in the consumer setting do not always apply here, such as use of CDN caches in eyeball networks.

A legal contract is an agreement between two or more parties as to something that is to be done in the future. Forming contracts electronically is desirable since it is convenient. However, existing electronic contract platforms have a critical shortcoming. They do not provide strong evidence that a contract has been legally and validly created. More specifically, they do not provide strong evidence that an electronic signature is authentic, that there was mutual assent, and that the parties had an opportunity to read the contract. We present Tabellion, a system for forming legal contracts on mobile devices, such as smartphones and tablets, that addresses the above shortcoming. We define four secure primitives and use them in Tabellion to introduce self-evident contracts, the validity of which can be verified by independent inspectors. We show how these primitives can be implemented securely in the Trusted Execution Environment (TEE) of mobile devices as well as a secure enclave in a centralized server, all with a small Trusted Computing Base (TCB). Moreover, we demonstrate that it is feasible to build a fully functional contract platform on top of these primitives. We develop ~15,000 lines of code (LoC) for our prototype, only ~1,000 of which need to be trusted. Through analysis, prototype measurements, and a 30-person user study, we show that Tabellion is secure, achieves acceptable performance, and provides slightly better usability than the state-of-the-art electronic contract platform, DocuSign, for viewing and signing contracts.

We consider applying computer vision to video on cloud-backed mobile devices using Deep Neural Networks (DNNs). The computational demands of DNNs are high enough that, without careful resource management, such applications strain device battery, wireless data, and cloud cost budgets. We pose the corresponding resource management problem, which we call Approximate Model Scheduling, as one of serving a stream of heterogeneous (i.e., solving multiple classification problems) requests under resource constraints. We present the design and implementation of an optimizing compiler and runtime scheduler to address this problem. Going beyond traditional resource allocators, we allow each request to be served approximately, by systematically trading off DNN classification accuracy for resource use, and remotely, by reasoning about on-device/cloud execution trade-offs. To inform the resource allocator, we characterize how several common DNNs, when subjected to state-of-the art optimizations, trade off accuracy for resource use such as memory, computation, and energy. The heterogeneous streaming setting is a novel one for DNN execution, and we introduce two new and powerful DNN optimizations that exploit it. Using the challenging continuous mobile vision domain as a case study, we show that our techniques yield significant reductions in resource usage and perform effectively over a broad range of operating conditions.

Generations of computer programmers are taught to prefetch network objects in computer science classes. In practice, prefetching can be harmful to the user's wallet when she is on a limited or pay-per-byte cellular data plan. Many popular, professionally-written smartphone apps today prefetch large amounts of network data that the typical user may never use. We present Procrastinator, which automatically decides when to fetch each network object that an app requests. This decision is made based on whether the user is on Wi-Fi or cellular, how many bytes are remaining on the user's data plan, and whether the object is needed at the present time. Procrastinator does not require app developer effort, nor app source code, nor OS changes -- it modifies the app binary to trap specific system calls and inject custom code. Our system can achieve as little as no savings to 4X reduction in total bytes transferred by an app, depending on the user and the app. These savings for the data-poor user come with a 300ms median latency penalty on LTE.

Many popular, professionally-written smartphone apps today prefetch large amounts of network data to improve performance. However, the typical user may not use all of this network data. When a user is on a limited or pay-per-byte cellular data plan, such as when roaming internationally, this prefetching behavior can cost her in overage fees on her cellular bill. This video demonstrates Procrastinator, which is a system that automatically decides when to fetch each network object that an app requests. This decision is made based on whether the user is on Wi-Fi or cellular, how many bytes are remaining on her data plan, and whether the object is needed at the present time. Procrastinator does not require app developer effort, nor app source code, nor OS changes -- it modifies the app binary to trap specific system calls and inject custom code. Our system can achieve as little as no savings to 4X reduction in total bytes transferred by an app, depending on the user and the app. These savings for the data-poor user come with a 300ms median latency penalty on LTE if the user goes to a part of the app where Procrastinator did not allow data to be prefetched. This video shows how main content on the primary page of apps is unaffected, and the delay that the user will typically experience if she goes to secondary pages in apps when she is running out of cellular data plan bytes.

The initial design of Apache Hadoop [1] was tightly focused on running massive, MapReduce jobs to process a web crawl. For increasingly diverse companies, Hadoop has become the data and computational agorá---the de facto place where data and computational resources are shared and accessed. This broad adoption and ubiquitous usage has stretched the initial design well beyond its intended target, exposing two key shortcomings: 1) tight coupling of a specific programming model with the resource management infrastructure, forcing developers to abuse the MapReduce programming model, and 2) centralized handling of jobs' control flow, which resulted in endless scalability concerns for the scheduler.

Generations of computer programmers are taught to prefetch network objects in computer science classes. In practice, prefetching can be harmful to the user's wallet when she is on a limited or pay-per-byte cellular data plan. Many popular, professionally-written smartphone apps today prefetch large amounts of network data that the typical user may never use. We present Procrastinator, which automatically decides when to fetch each network object that an app requests. This decision is made based on whether the user is on Wi-Fi or cellular, how many bytes are remaining on the user's data plan, and whether the object is needed at the present time. Procrastinator does not require developer effort, nor app source code, nor OS changes -- it modifies the app binary to trap specific system calls and inject custom code. Our system can achieve as little as no savings to 4X savings in bytes transferred, depending on the user and the app. In theory, we can achieve 17X savings, but we need to overcome additional technical challenges.

Visions of smart homes have long caught the attention of researchers and considerable effort has been put toward enabling home automation. However, these technologies have not been widely adopted despite being available for over three decades. To gain insight into this state of affairs, we conducted semi-structured home visits to 14 households with home automation. The long term experience, both positive and negative, of the households we interviewed illustrates four barriers that need to be addressed before home automation becomes amenable to broader adoption. These barriers are high cost of ownership, inflexibility, poor manageability, and difficulty achieving security. Our findings also provide several directions for further research, which include eliminating the need for structural changes for installing home automation, providing users with simple security primitives that they can confidently configure, and enabling composition of home devices.

Supporting interactive, multiplayer games on mobile phones over cellular networks is a difficult problem. It is particularly relevant now with the explosion of mostly single-player or turn-based games on mobile phones. The challenges stem from the highly variable performance of cellular networks and the need for scalability (not burdening the cellular infrastructure, nor any server resources that a game developer deploys). We have built a service for matchmaking in mobile games -- assigning players to games such that game settings are satisfied as well as latency requirements for an enjoyable game. This requires solving two problems. First, the service needs to know the cellular network latency between game players. Second, the service needs to quickly group players into viable game sessions. In this paper, we present the design of our service, results from our experiments on predicting cellular latency, and results from efficiently grouping players into games.

There are a lot of applications that run on modern mobile operating systems. Inevitably, some of these applications fail in the hands of users. Diagnosing a failure to identify the culprit, or merely reproducing that failure in the lab is difficult. To get insight into this problem, we interviewed developers of five mobile applications and analyzed hundreds of trouble tickets. We find that support for diagnosing unexpected application behavior is lacking across major mobile platforms. Even when developers implement heavy-weight logging during controlled trials, they do not discover many dependencies that are then stressed in the wild. They are also not well-equipped to understand how to monitor the large number of dependencies without impacting the phone's limited resources such as CPU and battery. Based on these findings, we argue for three fundamental changes to failure reporting on mobile phones. The first is spatial spreading, which exploits the large number of phones in the field by spreading the monitoring work across them. The second is statistical inference, which builds a conditional distribution model between application behavior and its dependencies in the presence of partial information. The third is adaptive sampling, which dynamically varies what each phone monitors, to adapt to both the varying population of phones and what is being learned about each failure. We propose a system called MobiBug that combines these three techniques to simplify the task of diagnosing mobile applications.

We argue that heterogeneity is hindering technological innovation in the home---homes differ in terms of their devices and how those devices are connected and used. To abstract these differences, we propose to develop a home-wide operating system. A HomeOS can simplify application development and let users easily add functionality by installing new devices or applications. The development of such an OS is an inherently inter-disciplinary exercise. Not only must the abstractions meet the usual goals of being efficient and easy to program, but the underlying primitives must also match how users want to manage and secure their home. We describe the preliminary design of HomeOS and our experience with developing applications for it.

The latency between machines on the Internet can dramatically affect users' experience for many distributed applications. Particularly, in multiplayer online games, players seek to cluster themselves so that those in the same session have low latency to each other. A system that predicts latencies between machine pairs allows such matchmaking to consider many more machine pairs than can be probed in a scalable fashion while users are waiting. Using a far-reaching trace of latencies between players on over 3.5 million game consoles, we designed Htrae, a latency prediction system for game matchmaking scenarios. One novel feature of Htrae is its synthesis of geolocation with a network coordinate system. It uses geolocation to select reasonable initial network coordinates for new machines joining the system, allowing it to converge more quickly than standard network coordinate systems and produce substantially lower prediction error than state-of-the-art latency prediction systems. For instance, it produces 90th percentile errors less than half those of iPlane and Pyxida. Our design is general enough to make it a good fit for other latency-sensitive peer-to-peer applications besides game matchmaking.

By studying trouble tickets from small enterprise networks, we conclude that their operators need detailed fault diagnosis. That is, the diagnostic system should be able to diagnose not only generic faults (e.g., performance-related) but also application specific faults (e.g., error codes). It should also identify culprits at a fine granularity such as a process or firewall configuration. We build a system, called NetMedic, that enables detailed diagnosis by harnessing the rich information exposed by modern operating systems and applications. It formulates detailed diagnosis as an inference problem that more faithfully captures the behaviors and interactions of fine-grained network components such as processes. The primary challenge in solving this problem is inferring when a component might be impacting another. Our solution is based on an intuitive technique that uses the joint behavior of two components in the past to estimate the likelihood of them impacting one another in the present. We find that our deployed prototype is effective at diagnosing faults that we inject in a live environment. The faulty component is correctly identified as the most likely culprit in 80% of the cases and is almost always in the list of top five culprits.

We present a study of end-to-end web access failures in the Internet. Part of our characterization of failures is based on directly observable end-to-end information. We also present novel analyses that reveal aspects of end-to-end failures that would be hard to discern otherwise. First, we combine end-to-end failure observations across a large number of clients to classify failures as server-related or client-related. Second, we correlate failures attributed to a client or server with BGP churn for the corresponding IP address prefix(es), to shed light on the end-to-end impact of BGP instability.

There is a fair amount of evidence that mesh (static multihop wireless) networks are gaining popularity, both in the academic literature and in the commercial space. Nonetheless, none of the prior work has evaluated the feasibility of applications on mesh through the use of deployed networks and real user traffic. The state of the art is the use of deployed testbeds with synthetic traces consisting of random traffic patterns.In this paper, we evaluate the feasibility of a mesh network for an all-wireless office using traces of office users and an actual 21-node multi-radio mesh testbed in an office area. Unlike previous mesh studies that have examined routing design in detail, we examine how different office mesh design choices impact the performance of user traffic. From our traces of 11 users spanning over a month, we identify 3 one hour trace periods with different characteristics and evaluate network performance for them. In addition, we consider different user-server placement, different wireless hardware, different wireless settings and different routing metrics.We find that our captured traffic is significantly different from the synthetic workloads typically used in the prior work. Our trace capture and replay methodology allows us to directly quantify the feasibility of office meshes by measuring the additional delay experienced by individual transactions made by user applications. Performance on our mesh network depends on the routing metric chosen, the user-server placement and the traffic load period. The choice of wireless hardware and wireless settings has a significant impact on performance under heavy load and challenging placement. Ultimately we conclude that for our traces and deployed system, under most conditions, all-wireless office meshes are feasible. In most cases, individual transactions incur under 20ms of additional delay over the mesh network. We believe this is an acceptable delay for most applications where a wired network to every machine is not readily available. We argue that our results are scalable to a network of over 100 users.

Typically, each autonomous system (AS) tunes its local IS-IS or OSPF metrics without any coordination with other ASes. Such local optimizations can lead to sub-optimal end-to-end network performance, as suggested by the performance enhancements achieved by some overlay routing projects. We study the interaction of local IGP engineering in an ISP network with interdomain routing policies. Specifically, (a) how does hot-potato routing (the BGP policy of choosing the closest egress) influence the selection of IGP link metrics? and (b) how does traffic to neighboring ASes shift due to changes in the local AS's IGP link metrics? In our measurement study, we find that the hot-potato routing policy interacts significantly with IGP engineering -ignoring this interaction resulted in metrics sub-optimal by as much as 20% of link utilization. Further, the impact on neighboring ASes depends on peering locations and policies, and as much as 25% of traffic to a neighboring AS can shift the exit point. Such interdomain shifts can be detrimental to the performance of neighboring ASes. We rely on the actual measured network topology, IGP metrics, traffic matrix and delay bounds. Even though our results are specific to a single ISP, they show significant interaction between local IGP engineering and interdomain routing policies, and thus motivate further work on global network optimization and coordination among ISPs.

Recent work in network traffic matrix estimation has focused on generating router-to-router or PoP-to-PoP (Point-of-Presence) traffic matrices within an ISP backbone from network link load data. However, these estimation techniques have not considered the impact of inter-domain routing changes in BGP (Border Gateway Protocol) . BGP routing changes have the potential to introduce significant errors in estimated traffic matrices by causing traffic shifts between egress routers or PoPs within a single backbone network. We present a methodology to correlate BGP routing table changes with packet traces in order to analyze how BGP dynamics affect traffic fan-out within a large "tier-1" network. Despite an average of 133 BGP routing updates per minute, we find that BGP routing changes do not cause more than 0.03% of ingress traffic to shift between egress PoPs. This limited impact is mostly due to the relative stability of network prefixes that receive the majority of traffic -- 0.05% of BGP routing table changes affect intra-domain routes for prefixes that carry 80% of the traffic. Thus our work validates an important assumption underlying existing techniques for traffic matrix estimation in large IP networks.

The delivery of IP traffic through the Internet depends on the complex interactions between thousands of autonomous systems (AS) that exchange routing information using the border gateway protocol (BGP). This paper investigates the topological structure of the Internet in terms of customer-provider and peer-peer relationships between autonomous systems, as manifested in BGP routing policies. We describe a technique for inferring AS relationships by exploiting partial views of the AS graph available from different vantage points. Next we apply the technique to a collection of ten BGP routing tables to infer the relationships between neighboring autonomous systems. Based on these results, we analyze the hierarchical structure of the Internet and propose a five-level classification of AS. Our characterization differs from previous studies by focusing on the commercial relationships between autonomous systems rather than simply the connectivity between the nodes.

Mobile ad-hoc networking involves peer-to-peer communication in a network with a dynamically changing topology. Achieving energy efficient communication in such a network is more challenging than in cellular networks since there is no centralized arbiter such as a base station that can administer power management. We propose and evaluate a power control loop, similar to those commonly found in cellular CDMA networks, for ad-hoc wireless networks. We use a comprehensive simulation infrastructure consisting of group mobility, group communication and terrain blockage models. A major focus of research in ad-hoc wireless networking is to reduce energy consumption because the wireless devices are envisioned to have small batteries and be incapable of energy scavenging. We show that this power control loop reduces energy consumption per transmitted byte by 10-20%. Furthermore, we show that it increases overall throughput by 15%.