The Better Business Bureau ranked online e-commerce scams as the top consumer threat for 2022. Our measurements of real consumer devices confirm that e-commerce scams receive large traffic volumes, a total of 6.3M visits during seven months. In this work, we study e-commerce scams in depth and design a detection classifier that combines novel features that target salient characteristics of e-commerce scam websites and features for detecting malicious and scam domains proposed by prior work. In addition, we specify a method for automatically creating reliable ground-truth sets that are an order of magnitude larger than that of prior work. We use this data set to evaluate the classifier and achieve a high 0.973 F1-score (Prec: 0.988, Rec: 0.959). In a best-effort comparison, we demonstrate that our classifier outperforms the F-1 score of the prior art by 11% and that our novel features offer an F1-score boost of 4.3% over the features used in the prior art. In addition, we deploy our classifier in a real-world setting, analyze over 760K e-shops visited by real users, and identify 10% of those as e-commerce scams. We demonstrate that the classifier has a low False Positive rate in real-world settings and can protect over 176K users in one week.

Trauma is the physical, emotional, or psychological harm caused by deeply distressing experiences. Research with communities that may experience high rates of trauma has shown that digital technologies can create or exacerbate traumatic experiences. Via three vignettes, we discuss how considering the possible effects of trauma and traumatic stress reactions provides an explanatory lens with new insights into people’s technology experiences. Then, we present a framework—trauma-informed computing—in which we adapt and show how to apply six key principles of trauma-informed approaches to computing: safety, trust, peer support, collaboration, enablement, and intersectionality. Through specific examples, we describe how to apply trauma-informed computing in four areas of computing research and practice: user experience research & design, security & privacy, artificial intelligence & machine learning, and organizational culture in tech companies. We conclude by discussing how adopting trauma-informed computing will lead to benefits for all users, not only those experiencing trauma.

Stalkerware enables individuals to conduct covert surveillance on a targeted person’s device. Android devices are a particularly fertile ground for stalkerware, most of which spy on a single communication channel, sensor, or category of private data, though 27% of stalkerware surveil multiple of private data sources. We present Dosmelt, a system that enables stalkerware warnings that precisely characterize the types of surveillance conducted by Android stalkerware so that surveiled individuals can take appropriate mitigating action. Our methodology uses active learning in a semi-supervised learning setting to tackle this task at scale, which would otherwise require expert labeling of significant number of stalkerware apps. Dosmelt leverages the observation that stalkerware differs from other categories of spyware in its open advertising of its surveillance capabilities, which we detect on the basis of the titles and self-descriptions of stalkerware apps that are posted on Android app stores. Dosmelt achieves up to 96% AUC for stalkerware detection with a 91% Macro-F1 score of surveillance capability attribution for stalkerware apps. Dosmelt has detected hundreds of new stalkerware apps that we have added to the Stalkerware Threat List.

Users struggle to adhere to expert-recommended security and privacy practices. While prior work has studied initial adoption of such practices, little is known about the subsequent implementation and abandonment. We conducted an online survey (n=902) examining the adoption and abandonment of 30 commonly recommended practices. Security practices were more widely adopted than privacy and identity theft protection practices. Manual and fully automatic practices were more widely adopted than practices requiring recurring user interaction. Participants' gender, education, technical background, and prior negative experience are correlated with their levels of adoption. Furthermore, practices were abandoned when they were perceived as low-value, inconvenient, or when users overrode them with subjective judgment. We discuss how security, privacy, and identity theft protection recommendations and tools can be better aligned with user needs.

Technology increasingly facilitates interpersonal attacks such as stalking, abuse, and other forms of harassment. While prior studies have examined the ecosystem of software designed for stalking, there exists an unstudied, larger landscape of apps-what we call creepware-used for interpersonal attacks. In this paper, we initiate a study of creepware using access to a dataset detailing the mobile apps installed on over 50 million Android devices. We develop a new algorithm, CreepRank, that uses the principle of guilt by association to help surface previously unknown examples of creepware, which we then characterize through a combination of quantitative and qualitative methods. We discovered apps used for harassment, impersonation, fraud, information theft, concealment, and even apps that purport to defend victims against such threats. As a result of our work, the Google Play Store has already removed hundreds of apps for policy violations. More broadly, our findings and techniques improve understanding of the creepware ecosystem, and will inform future efforts that aim to mitigate interpersonal attacks.

Understanding users' perceptions of suspected computer-security problems can help us tailor technology to better protect users. To this end, we conducted a field study of users' perceptions using 189,272 problem descriptions sent to the customer-support desk of a large anti-virus vendor from 2015 to 2018. Using qualitative methods, we analyzed 650 problem descriptions to study the security issues users faced and the symptoms that led users to their own diagnoses. Subsequently, we investigated to what extent and for what types of issues user diagnoses matched those of experts. We found, for example, that users and experts were likely to agree for most issues, but not for attacks (e.g., malware infections), for which they agreed only in 44% of the cases. Our findings inform several user-security improvements, including how to automate interactions with users to resolve issues and to better communicate issues to users.

In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organizing distributed teachers to jointly select a compact while informative training subset from data hosted by the teachers to make a learner learn better. The challenges arise from three perspectives. First, the state-of-the-art pool-based super teaching method applies mixed-integer non-linear programming (MINLP) which does not scale well to very large data sets. Second, it is desirable to restrict data access of the teachers to only their own data during the collaboration stage to mitigate privacy leaks. Finally, the teaching collaboration should be communication-efficient since large communication overheads can cause synchronization delays between teachers. To address these challenges, we formulate the collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk. We theoretically demonstrate the necessity of collaboration between teachers for improving the learner's learning. Furthermore, we show that the proposed method enjoys a similar property as the Oracle property of adaptive Lasso. Empirical study illustrates that our teaching method can deliver significantly more accurate teaching results with high speed, while the non-collaborative MINLP-based super teaching becomes prohibitively expensive to compute.

Finding patterns in graphs has become a vital challenge in many domains from biological systems, network security, to finance (e.g., finding money laundering rings of bankers and business owners). While there is significant interest in graph databases and querying techniques, less research has focused on helping analysts make sense of underlying patterns within a group of subgraph results. Visualizing graph query results is challenging, requiring effective summarization of a large number of subgraphs, each having potentially shared node-values, rich node features, and flexible structure across queries. We present VIGOR, a novel interactive visual analytics system, for exploring and making sense of query results. VIGOR uses multiple coordinated views, leveraging different data representations and organizations to streamline analysts sensemaking process. VIGOR contributes: (1) an exemplar-based interaction technique, where an analyst starts with a specific result and relaxes constraints to find other similar results or starts with only the structure (i.e., without node value constraints), and adds constraints to narrow in on specific results; and (2) a novel feature-aware subgraph result summarization. Through a collaboration with Symantec, we demonstrate how VIGOR helps tackle real-world problems through the discovery of security blindspots in a cybersecurity dataset with over 11,000 incidents. We also evaluate VIGOR with a within-subjects study, demonstrating VIGOR's ease of use over a leading graph database management system, and its ability to help analysts understand their results at higher speed and make fewer errors.

Cybersecurity analysts are often presented suspicious machine activity that does not conclusively indicate compromise, resulting in undetected incidents or costly investigations into the most appropriate remediation actions. There are many reasons for this: deficiencies in the number and quality of security products that are deployed, poor configuration of those security products, and incomplete reporting of product-security telemetry. Managed Security Service Providers (MSSP's), which are tasked with detecting security incidents on behalf of multiple customers, are confronted with these data quality issues, but also possess a wealth of cross-product security data that enables innovative solutions. We use MSSP data to develop Virtual Product, which addresses the aforementioned data challenges by predicting what security events would have been triggered by a security product if it had been present. This benefits the analysts by providing more context into existing security incidents (albeit probabilistic) and by making questionable security incidents more conclusive. We achieve up to 99% AUC in predicting the incidents that some products would have detected had they been present.

The central task of a Security Incident and Event Manager (SIEM) or Managed Security Service Provider (MSSP) is to detect security incidents on the basis of tens of thousands of event types coming from many kinds of security products. We present Smoke Detector, which processes trillions of security events with the Random Walk with Restart (RWR) algorithm, inferring high order relationships between known security incidents and imperfect secondary security events (smoke) to find undiscovered security incidents (fire). By finding previously undetected incidents, Smoke Detector's RWR algorithm is able to increase the MSSP's critical incident count by 19% with a 1.3% FP rate.

We study a dataset of billions of program binary files that appeared on 100 million computers over the course of 12 months, discovering that 94% of these files were present on a single machine. Though malware polymorphism is one cause for the large number of singleton files, additional factors also contribute to polymorphism, given that the ratio of benign to malicious singleton files is 80:1. The huge number of benign singletons makes it challenging to reliably identify the minority of malicious singletons. We present a large-scale study of the properties, characteristics, and distribution of benign and malicious singleton files. We leverage the insights from this study to build a classifier based purely on static features to identify 92% of the remaining malicious singletons at a 1.4% percent false positive rate, despite heavy use of obfuscation and packing techniques by most malicious singleton files that we make no attempt to de-obfuscate. Finally, we demonstrate robustness of our classifier to important classes of automated evasion attacks.

Understanding how to group a set of binary files into the piece of software they belong to is highly desirable for software profiling, malware detection, or enterprise audits, among many other applications. Unfortunately, it is also extremely challenging: there is absolutely no uniformity in the ways different applications rely on different files, in how binaries are signed, or in the versioning schemes used across different pieces of software. In this paper, we show that, by combining information gleaned from a large number of endpoints (millions of computers), we can accomplish large-scale application identification automatically and reliably. Our approach relies on collecting metadata on billions of files every day, summarizing it into much smaller "sketches", and performing approximate k-nearest neighbor clustering on non-metric space representations derived from these sketches. We design and implement our proposed system using Apache Spark, show that it can process billions of files in a matter of hours, and thus could be used for daily processing. We further show our system manages to successfully identify which files belong to which application with very high precision, and adequate recall.

The increasing sophistication of malicious software calls for new defensive techniques that are harder to evade, and are capable of protecting users against novel threats. We present AESOP, a scalable algorithm that identifies malicious executable files by applying Aesop's moral that "a man is known by the company he keeps." We use a large dataset voluntarily contributed by the members of Norton Community Watch, consisting of partial lists of the files that exist on their machines, to identify close relationships between files that often appear together on machines. AESOP leverages locality-sensitive hashing to measure the strength of these inter-file relationships to construct a graph, on which it performs large scale inference by propagating information from the labeled files (as benign or malicious) to the preponderance of unlabeled files. AESOP attained early labeling of 99% of benign files and 79% of malicious files, over a week before they are labeled by the state-of-the-art techniques, with a 0.9961 true positive rate at flagging malware, at 0.0001 false positive rate.

Binary instrumentation allows users to inject new code into programs without requiring source code, symbols, or debugging information. Instrumenting a binary requires structural modifications such as moving code, adding new code, and overwriting existing code; these modifications may unintentionally change the program's semantics. Binary instrumenters attempt to preserve the intended semantics of the program by further transforming the code to compensate for these structural modifications. Current instrumenters may fail to correctly preserve program semantics or impose significant unnecessary compensation cost because they lack a formal model of the impact of their structural modifications on program semantics. These weaknesses are particularly acute when instrumenting highly optimized or malicious code, making current instrumenters less useful as tools in the security or high-performance domains. We present a formal specification of how the structural modifications used by instrumentation affect a binary's visible behavior, and have adapted the Dyninst binary instrumenter to use this specification, thereby guaranteeing correct instrumentation while greatly reducing compensation costs. When compared against the fastest widely used instrumenters our technique imposed 46% less overhead; furthermore, we can successfully instrument highly defensive binaries that are specifically looking for code patching and instrumentation.