Mobile apps include privacy settings that allow their users to configure how their data should be shared. These settings, however, are often hard to locate and hard to understand by the users, even in popular apps, such as Facebook. More seriously, they are often set to share user data by default, exposing her privacy without proper consent. In this paper, we report the first systematic study on the problem, which is made possible through an in-depth analysis of user perception of the privacy settings. More specifically, we first conduct two user studies (involving nearly one thousand users) to understand privacy settings from the user's perspective, and identify these hard-to-find settings. Then we select 14 features that uniquely characterize such hidden privacy settings and utilize a novel technique called semantics- based UI tracing to extract them from a given app. On top of these features, a classifier is trained to automatically discover the hidden privacy settings, which together with other innovations, has been implemented into a tool called Hound. Over our labeled data set, the tool achieves an accuracy of 93.54%. Further running it on 100,000 latest apps from both Google Play and third-party markets, we find that over a third (36.29%) of the privacy settings identified from these apps are “hidden”. Looking into these settings, we observe that they become hard to discover and hard to understand primarily due to the problematic categorization on the apps' user interfaces and/or confusing descriptions. Further importantly, though more privacy options have been offered to the user over time, also discovered is the persistence of their usability issue, which becomes even more serious, e.g., originally easy-to-find settings now harder to locate. And among all such hidden privacy settings, 82.16% are set to leak user privacy by default. We provide suggestions for improving the usability of these privacy settings at the end of our study.

Recent years have witnessed the rapid progress in deep learning (DP), which also brings their potential weaknesses to the spotlights of security and machine learning studies. With important discoveries made by adversarial learning research, surprisingly little attention, however, has been paid to the real-world adversarial techniques deployed by the cybercriminal to evade image-based detection. Unlike the adversarial examples that induce misclassification using nearly imperceivable perturbation, real-world adversarial images tend to be less optimal yet equally effective. As a first step to understand the threat, we report in the paper a study on adversarial promotional porn images (APPIs) that are extensively used in underground advertising. We show that the adversary today's strategically constructs the APPIs to evade explicit content detection while still preserving their sexual appeal, even though the distortions and noise introduced are clearly observable to humans. To understand such real-world adversarial images and the underground business behind them, we develop a novel DP-based methodology called Male`na, which focuses on the regions of an image where sexual content is least obfuscated and therefore visible to the target audience of a promotion. Using this technique, we have discovered over 4,000 APPIs from 4,042,690 images crawled from popular social media, and further brought to light the unique techniques they use to evade popular explicit content detectors (e.g., Google Cloud Vision API, Yahoo Open NSFW model), and the reason that these techniques work. Also studied are the ecosystem of such illicit promotions, including the obfuscated contacts advertised through those images, compromised accounts used to disseminate them, and large APPI campaigns involving thousands of images. Another interesting finding is the apparent attempt made by cybercriminals to steal others' images for their advertising. The study highlights the importance of the research on real-world adversarial learning and makes the first step towards mitigating the threats it poses.

The popularity of cloud hosting services also brings in new security chal- lenges: it has been reported that these services are increasingly utilized by miscreants for their malicious online activities. Mitigating this emerging threat, posed by such “bad repositories” (simply Bar), is challenging due to the different hosting strategy to traditional hosting service, the lack of direct observations of the repositories by those outside the cloud, the reluctance of the cloud provider to scan its customers’ repositories without their consent, and the unique evasion strategies employed by the adversary. In this paper, we took the first step toward understanding and detecting this emerging threat. Using a small set of “seeds” (i.e., confirmed Bars), we identified a set of collective features from the websites they serve (e.g., attempts to hide Bars), which uniquely characterize the Bars. These features were utilized to build a scanner that detected over 600 Bars on leading cloud platforms like Amazon, Google, and 150 K sites, including popular ones like , using them. Highlights of our study include the pivotal roles played by these repositories on malicious infrastructures and other important discoveries include how the adversary exploited legitimate cloud repositories and why the adversary uses Bars in the first place that has never been reported. These findings bring such malicious services to the spotlight and contribute to a better understanding and ultimately eliminating this new threat.

The popularity of cloud hosting services also brings in new security challenges: it has been reported that these services are increasingly utilized by miscreants for their malicious online activities. Mitigating this emerging threat, posed by such "bad repositories" (simply Bar), is challenging due to the different hosting strategy to traditional hosting service, the lack of direct observations of the repositories by those outside the cloud, the reluctance of the cloud provider to scan its customers' repositories without their consent, and the unique evasion strategies employed by the adversary. In this paper, we took the first step toward understanding and detecting this emerging threat. Using a small set of "seeds" (i.e., confirmed Bars), we identified a set of collective features from the websites they serve (e.g., attempts to hide Bars), which uniquely characterize the Bars. These features were utilized to build a scanner that detected over 600 Bars on leading cloud platforms like Amazon, Google, and 150K sites, including popular ones like groupon.com, using them. Highlights of our study include the pivotal roles played by these repositories on malicious infrastructures and other important discoveries include how the adversary exploited legitimate cloud repositories and why the adversary uses Bars in the first place that has never been reported. These findings bring such malicious services to the spotlight and contribute to a better understanding and ultimately eliminating this new threat.

To adapt to the rapidly evolving landscape of cyber threats, security professionals are actively exchanging Indicators of Compromise (IOC) (e.g., malware signatures, botnet IPs) through public sources (e.g. blogs, forums, tweets, etc.). Such information, often presented in articles, posts, white papers etc., can be converted into a machine-readable OpenIOC format for automatic analysis and quick deployment to various security mechanisms like an intrusion detection system. With hundreds of thousands of sources in the wild, the IOC data are produced at a high volume and velocity today, which becomes increasingly hard to manage by humans. Efforts to automatically gather such information from unstructured text, however, is impeded by the limitations of today's Natural Language Processing (NLP) techniques, which cannot meet the high standard (in terms of accuracy and coverage) expected from the IOCs that could serve as direct input to a defense system. In this paper, we present iACE, an innovation solution for fully automated IOC extraction. Our approach is based upon the observation that the IOCs in technical articles are often described in a predictable way: being connected to a set of context terms (e.g., "download") through stable grammatical relations. Leveraging this observation, iACE is designed to automatically locate a putative IOC token (e.g., a zip file) and its context (e.g., "malware", "download") within the sentences in a technical article, and further analyze their relations through a novel application of graph mining techniques. Once the grammatical connection between the tokens is found to be in line with the way that the IOC is commonly presented, these tokens are extracted to generate an OpenIOC item that describes not only the indicator (e.g., a malicious zip file) but also its context (e.g., download from an external source). Running on 71,000 articles collected from 45 leading technical blogs, this new approach demonstrates a remarkable performance: it generated 900K OpenIOC items with a precision of 95% and a coverage over 90%, which is way beyond what the state-of-the-art NLP technique and industry IOC tool can achieve, at a speed of thousands of articles per hour. Further, by correlating the IOCs mined from the articles published over a 13-year span, our study sheds new light on the links across hundreds of seemingly unrelated attack instances, particularly their shared infrastructure resources, as well as the impacts of such open-source threat intelligence on security protection and evolution of attack strategies.

Promotional infection is an attack in which the adversary exploits a website's weakness to inject illicit advertising content. Detection of such an infection is challenging due to its similarity to legitimate advertising activities. An interesting observation we make in our research is that such an attack almost always incurs a great semantic gap between the infected domain (e.g., a university site) and the content it promotes (e.g., selling cheap viagra). Exploiting this gap, we developed a semantic-based technique, called Semantic Inconsistency Search (SEISE), for efficient and accurate detection of the promotional injections on sponsored top-level domains (sTLD) with explicit semantic meanings. Our approach utilizes Natural Language Processing (NLP) to identify the bad terms (those related to illicit activities like fake drug selling, etc.) most irrelevant to an sTLD's semantics. These terms, which we call irrelevant bad terms (IBTs), are used to query search engines under the sTLD for suspicious domains. Through a semantic analysis on the results page returned by the search engines, SEISE is able to detect those truly infected sites and automatically collect new IBTs from the titles/URLs/snippets of their search result items for finding new infections. Running on 403 sTLDs with an initial 30 seed IBTs, SEISE analyzed 100K fully qualified domain names (FQDN), and along the way automatically gathered nearly 600 IBTs. In the end, our approach detected 11K infected FQDN with a false detection rate of 1.5% and over 90% coverage. Our study shows that by effective detection of infected sTLDs, the bar to promotion infections can be substantially raised, since other non-sTLD vulnerable domains typically have much lower Alexa ranks and are therefore much less attractive for underground advertising. Our findings further bring to light the stunning impacts of such promotional attacks, which compromise FQDNs under 3% of .edu, .gov domains and over one thousand gov.cn domains, including those of leading universities such as stanford.edu, mit.edu, princeton.edu, havard.edu and government institutes such as nsf.gov and nih.gov. We further demonstrate the potential to extend our current technique to protect generic domains such as .com and .org.

Stealing of sensitive information from apps is always considered to be one of the most critical threats to Android security. Recent studies show that this can happen even to the apps without explicit implementation flaws, through exploiting some design weaknesses of the operating system, e.g., Shared communication channels such as Bluetooth, and side channels such as memory and network-data usages. In all these attacks, a malicious app needs to run side-by-side with the target app (the victim) to collect its runtime information. Examples include recording phone conversations from the phone app, gathering WebMD's data usages to infer the disease condition the user looks at, etc. This runtime-information-gathering (RIG) threat is realistic and serious, as demonstrated by prior research and our new findings, which reveal that the malware monitoring popular Android-based home security systems can figure out when the house is empty and the user is not looking at surveillance cameras, and even turn off the alarm delivered to her phone. To defend against this new category of attacks, we propose a novel technique that changes neither the operating system nor the target apps, and provides immediate protection as soon as an ordinary app (with only normal and dangerous permissions) is installed. This new approach, called App Guardian, thwarts a malicious app's runtime monitoring attempt by pausing all suspicious background processes when the target app (called principal) is running in the foreground, and resuming them after the app stops and its runtime environment is cleaned up. Our technique leverages a unique feature of Android, on which third-party apps running in the background are often considered to be disposable and can be stopped anytime with only a minor performance and utility implication. We further limit such an impact by only focusing on a small set of suspicious background apps, which are identified by their behaviors inferred from their side channels (e.g., Thread names, CPU scheduling and kernel time). App Guardian is also carefully designed to choose the right moments to start and end the protection procedure, and effectively protect itself against malicious apps. Our experimental studies show that this new technique defeated all known RIG attacks, with small impacts on the utility of legitimate apps and the performance of the OS. Most importantly, the idea underlying our approach, including app-level protection, side-channel based defense and lightweight response, not only significantly raises the bar for the RIG attacks and the research on this subject but can also inspire the follow-up effort on new detection systems practically deployable in the fragmented Android ecosystem.

Android is a fast evolving system, with new updates coming out one after another. These updates often completely overhaul a running system, replacing and adding tens of thousands of files across Android's complex architecture, in the presence of critical user data and applications (apps for short). To avoid accidental damages to such data and existing apps, the upgrade process involves complicated program logic, whose security implications, however, are less known. In this paper, we report the first systematic study on the Android updating mechanism, focusing on its Package Management Service (PMS). Our research brought to light a new type of security-critical vulnerabilities, called Pileup flaws, through which a malicious app can strategically declare a set of privileges and attributes on a low-version operating system (OS) and wait until it is upgraded to escalate its privileges on the new system. Specifically, we found that by exploiting the Pileup vulnerabilities, the app can not only acquire a set of newly added system and signature permissions but also determine their settings (e.g., protection levels), and it can further substitute for new system apps, contaminate their data (e.g., cache, cookies of Android default browser) to steal sensitive user information or change security configurations, and prevent installation of critical system services. We systematically analyzed the source code of PMS using a program verification tool and confirmed the presence of those security flaws on all Android official versions and over 3000 customized versions. Our research also identified hundreds of exploit opportunities the adversary can leverage over thousands of devices across different device manufacturers, carriers and countries. To mitigate this threat without endangering user data and apps during an upgrade, we also developed a new detection service, called SecUP, which deploys a scanner on the user's device to capture the malicious apps designed to exploit Pileup vulnerabilities, based upon the vulnerability-related information automatically collected from newly released Android OS images.