Collegiate student-athletes train and compete in a dense data ecology where information about their bodies and performances circulates among coaches, staff, and fans. To understand how student-athletes themselves engage with this data, we conducted interviews with 20 student-athletes, identifying four modes of engagement: 1) performance-directive, executing training and targeting improvement; 2) reflective-monitoring, assessing the body’s reaction to training and daily load; 3) coach-mediated, receiving insights through staff expertise; and 4) selective-disengagement, intentionally stepping back to protect confidence or avoid overload. These findings fill a gap left open by three related areas of research: SportsHCI, collegiate athletics, and personal data engagement. Each mode entails reasons, practices, and trade-offs. Student-athletes draw on different combinations of these modes as they respond to training demands, coaching oversight, and their own well-being. Our findings highlight how an evolving data ecology creates opportunities and pressures, requiring student-athletes to balance performance with protecting their state of mind.

As extended reality (XR) systems become increasingly immersive and sensor-rich, they enable the collection of behavioral signals such as eye and body telemetry. These signals support personalized and responsive experiences and may also contain unique patterns that can be linked back to individuals. However, privacy mechanisms that naively pair unimodal mechanisms (e.g., independently apply privacy mechanisms for eye and body privatization) are often ineffective at preventing re-identification in practice. In this work, we systematically evaluate real-time privacy mechanisms for XR, both individually and in pair, across eye and body modalities. We assess privacy through re-identification rates and evaluate utility using numerical performance thresholds derived from existing literature to ensure real-time interaction requirements are met. We evaluated four eye and ten body mechanisms across multiple datasets, comprising up to 407 participants. Our results show that when carefully paired, multimodal mechanisms reduce re-identification rate from 80.3% to 26.3% in casual XR applications (e.g., VRChat and Job Simulator) and from 84.8% to 26.1 % in competitive XR applications (e.g., Beat Saber and Synth Riders), all while maintaining acceptable performance based on established thresholds. To facilitate adoption, we additionally release XR Privacy SDK, an open-source toolkit enabling developers to integrate the privacy mechanisms into XR applications for real-time use. These findings underscore the potential of modality-specific and context-aware privacy strategies for protecting behavioral data in XR environments.

Embedded devices play a major role in supporting critical infrastructure, but lack many of the security pro-tections of sophisticated systems. Determining whether these devices are compromised is, therefore, a challenge. In this work, we describe a novel control flow verification methodology via electromagnetic (EM) emanations. We design a framework that incorporates signal processing and training to detect subtle control flow deviations as small as three clock cycles, the minimum required to execute a return with malicious activity on modern embedded hardware. Our methodology leverages basic block detection, enabling the discovery of these subtle control flow deviations that escape conventional detection approaches. We evaluate our framework's ability to detect insertion and modification control flow attacks on six different case studies of real-world critical operations and two processors featuring different architectures. Our results show 96.6% detection accuracy across all tested programs and attacks. Finally, we show the transferability of our methodology to different instances of our evaluated processors, reaching up to 98.7% convergence with our baseline models while requiring a third of the collected EM samples compared to standard retraining. In doing so, we reveal the feasibility of fine-grained EM-based control flow monitoring for low-power microcontrollers.

Plausible deniability (PD) allows at-risk users to deny the existence of their sensitive data stored on storage devices. This is critical to protect the privacy and the personal safety of users, as adversaries might force users to decrypt their devices, risking the disclosure of sensitive data that could endanger their lives and liberty. In this work, we show how current PD systems built on flash memory fail to obscure distinguishable data layouts created when hidden data is written. This deficiency makes them vulnerable to coercive adversaries who can capture single or multiple data snapshots of storage devices for scrutiny. To defend against this threat, we propose MUTE, a perMUTation-based PD systEm designed for flash memory. Building upon widely-adopted full disk encryption (FDE) mechanisms that provide device-level data encryption, MUTE modifies the distribution of initialization vectors (IV s) for encryption blocks within FDE, translating the hidden data into a permutation derived from the IV. Unlike other PD solutions, MUTE allows for storing hidden data without requiring the reduction of storage capacity. Moreover, it preserves the plausible deni-ability of the hidden data in a provably secure manner by maintaining the original logic of data operations on the flash memory without changing the data layout. We implement MUTE in the flash translation layer (FTL) of flash-based SSDs using FEMU, a widely-used emulator supporting flash mem-ory research. Our evaluation with various micro-benchmarks and real-world workloads demonstrates that MUTE provides practical write and read throughputs of 23.4 MB/s and 15.7 MB/s and a capacity of 25.3 GB for hidden data in a 512 GB SSD, comparable with existing PD systems. MUTE achieves strong PD guarantees for flash-based devices against coercive adversaries, outperforming current PD systems. Index Terms-Plausible Deniability, Data Layout, Flash-based SSDs

Sharing high-quality research data specifically for reuse in future work helps the scientific community progress by enabling researchers to build upon existing work and explore new research questions without duplicating data collection efforts. Because current discussions about research artifacts in Computer Security focus on reproducibility and availability of source code, the reusability of data is unclear. We examine data sharing practices in Computer Security and Measurement to provide resources and recommendations for sharing reusable data. Our study covers five years (2019–2023) and seven conferences in Computer Security and Measurement, identifying 948 papers that create a dataset as one of their contributions. We analyze the 265 accessible datasets, evaluating their under-standability and level of reuse. Our findings reveal inconsistent practices in data sharing structure and documentation, causing some datasets to not be shared effectively. Additionally, reuse of datasets is low, especially in fields where the nature of the data does not lend itself to reuse. Based on our findings, we offer data-driven recommendations and resources for improving data sharing practices in our community. Furthermore, we encourage authors to be intentional about their data sharing goals and align their sharing strategies with those goals.

Telecommunication networks form the backbone of our connected society, enabling global voice and data communication. Yet, beneath their seamless operation lies a complex interplay of signaling protocols, legacy systems, and evolving technologies that have, over the decades, exposed both opportunities for innovation and significant security challenges. While these networks have changed over the past 50 years and complexity has migrated outwards to devices, one enduring challenge has been a lack of accessibility. In this talk, we will discuss some of our recent efforts to better understand functionality on mobile devices by examining command sets [3] and processors [2]. We also look at security assessment techniques and how they can expose vulnerabilities in core cellular infrastructure. A central focus of this talk is the application of fuzz testing-a powerful technique for discovering implementation flaws and specification ambiguities. Fuzzing involves generating random or mutated inputs and injecting them into target systems to observe unusual behaviors or crashes. In the context of telecom networks, a fuzzing campaign can target the message handling logic, permuting fields such as the Protocol Discriminator or Bearer Capabilities, causing ASN.1 decoders in some devices to fail, leading to denial-of-service or potentially more severe consequences [1]. Through case studies and demonstrations, the talk will reveal how seemingly innocuous fields in call setup messages can destabilize network components or user devices, while also examining how the analysis of messages rather than examining device behavior can lead to the detection of rogue elements [4] in a network.

The latest developments in computer hardware, sensor technologies, and artificial intelligence can make virtual reality (VR) and virtual spaces an important part of human everyday life. Eye tracking offers not only a hands-free way of interaction but also the possibility of a deeper understanding of human visual attention and cognitive processes in VR. Despite these possibilities, eye-tracking data also reveal users’ privacy-sensitive attributes when combined with the information about the presented stimulus. To address all, this survey first covers major works in eye tracking, VR, and privacy areas between 2012 and 2022. While eye tracking in VR part covers the computational eye-tracking pipeline from pupil detection and gaze estimation to offline data analysis, for privacy and security, we focus on eye-based authentication as well as computational methods to preserve the privacy of individuals and their eye-tracking data in VR. Later, we outline three main directions by focusing on privacy. In summary, this survey presents an extensive literature review of the utmost possibilities of eye tracking in VR and their privacy implications.

Ransomware attacks have become increasingly frequent and high-profile, resulting in billions of dollars in data and operational losses annually. Current mechanisms typically deploy defenses in vulnerable operating systems, making them susceptible to advanced adversaries capable of compromising the OS. While implementing defense mechanisms within storage devices can address this vulnerability, they lack detection accuracy due to their inability to access data semantics, such as file system metadata. Moreover, these methods only expose block-level interfaces without file-level information, limiting the usability and practicality of data recovery management. Therefore, we develop SrFTL , a novel ransomware defense framework that allows leveraging data semantics for accurate ransomware detection and effective file-level data recovery against data compromise. Specifically, SrFTL employs defense enforcement within the flash translation layer (FTL) of SSDs. Then, SrFTL combines the secure enclave with the modified FTL through a secure channel to enable flexible ransomware defenses within the enclave. Finally, SrFTL deploys ransomware classification and data recovery defenses in the enclave, providing high detection accuracy and low-cost data recovery. Our evaluation demonstrates that SrFTL achieves zero false positives and negatives when detecting our collected real-world ransomware samples and benign applications, outperforming current FTL-level solutions (e.g., MimosaFTL). Moreover, SrFTL introduces on average a trivial performance overhead of 1.5% compared with a regular SSD. Finally, evaluating against multiple real-world ransomware samples, SrFTL enables fast data recovery with an average time of 9.3 seconds. SrFTL thus bridges the semantic gap between the FTL and OS-level file information to stop ransomware while maintaining the integrity and authenticity of employed defenses.

In response to the growing sophistication of censorship methods deployed by governments worldwide, the existence of open-source censorship measurement platforms has increased. Analyzing censorship data is challenging due to the data’s large size, diversity, and variability, requiring a comprehensive understanding of the data collection process and applying established data analysis techniques for thorough information extraction. In this work, we develop a framework that is applicable across all major censorship datasets to continually identify changes in censorship data trends and reveal potentially unreported censorship. Our framework consists of control charts and the Mann-Kendall trend detection test, originating from statistical process control theory, and we implement it on Censored Planet, GFWatch, the Open Observatory of Network Interference (OONI), and Tor data from Russia, Myanmar, China, Iran, Türkiye, and Pakistan from January 2021 through March 2023. Our study confirms results from prior studies and also identifies new events that we validate through media reports. Our correlation analysis reveals minimal similarities between censorship datasets. However, because our framework is applicable across all major censorship datasets, it significantly reduces the manual effort required to employ multiple datasets, which we further demonstrate by applying it to four additional Internet outage-related datasets. Our work thus provides a tool for continuously monitoring censorship activity and acts as a basis for developing more systematic, holistic, and in-depth analysis techniques for censorship data.

Cellular network infrastructure serves as the backbone of modern mobile wireless communication. As such, cellular cores must be proactively secured against external threats to ensure reliable service. Compromised base station attacks against the core are a rising threat to cellular networks, while user device inputs have long been considered as an attack vector; despite this, few techniques exist to comprehensively test RAN-Core interfaces against malicious input. In this work, we devise a fuzzing framework that performantly fuzzes cellular interfaces accessible from a base station or user device, overcoming several challenges in fuzzing specific to LTE/5G network components. We also introduce ASNFuzzGen, a tool that compiles ASN.1 specifications into structure-aware fuzzing modules, thereby facilitating effective fuzzing exploration of complex cellular protocols. We run fuzzing campaigns against seven open-source and commercial cores and discover 119 vulnerabilities, with 93 CVEs assigned. Our results reveal common implementation mistakes across several cores that lead to vulnerabilities, and the successful coordination of patches for these vulnerabilities across several vendors demonstrates the practical impact ASNFuzzGen has on hardening user-exposed cellular systems.

Continuous location sharing (CLS) applications are widely used for safety and social convenience. However, these applications have privacy concerns that can be used for control and harm. To understand user concerns, we performed the largest user study of CLS application usage performed to date, with 1500 of 3000 users indicating they use CLS applications and 896 of these users completing surveys. From survey responses, we conducted 23 interviews with participants who had uncomfortable experiences. With these interviews, we perform thematic analysis grounded by sociological frameworks of power dynamics and social exchange theory. We observe that CLS application users face discomfort related to three primary categories that build on each other: (1) overstepped boundaries, (2) continued discomfort, and (3) lifestyle-impacting behaviors. With this foundational understanding, we suggest features that aim to reduce relationship imbalances that CLS applications enable. Our resulting study demonstrates that CLS applications contribute to interpersonal discomfort, highlighting the need for design changes.

Audio deepfakes represent a rising threat to trust in our daily communications. In response to this, the research community has developed a wide array of detection techniques aimed at preventing such attacks from deceiving users. Unfortunately, the creation of these defenses has generally overlooked the most important element of the system - the user themselves. As such, it is not clear whether current mechanisms augment, hinder, or simply contradict human classification of deepfakes. In this paper, we perform the first large-scale user study on deepfake detection. We recruit over 1,200 users and present them with samples from the three most widely-cited deepfake datasets. We then quantitatively compare performance and qualitatively conduct thematic analysis to motivate and understand the reasoning behind user decisions and differences from machine classifications. Our results show that users correctly classify human audio at significantly higher rates than machine learning models, and rely on linguistic features and intuition when performing classification. However, users are also regularly misled by pre-conceptions about the capabilities of generated audio (e.g., that accents and background sounds are indicative of humans). Finally, machine learning models suffer from significantly higher false positive rates, and experience false negatives that humans correctly classify when issues of quality or robotic characteristics are reported. By analyzing user behavior across multiple deepfake datasets, our study demonstrates the need to more tightly compare user and machine learning performance, and to target the latter towards areas where humans are less likely to successfully identify threats.

Industry is increasingly adopting private 5G networks to securely manage their wireless devices in retail, manufacturing, natural resources, and healthcare. As with most technology sectors, open-source software is well poised to form the foundation of deployments, whether it is deployed directly or as part of well-maintained proprietary offerings. This paper seeks to examine the use of cryptography and secure randomness in open-source cellular cores. We design a set of 13 CodeQL static program analysis rules for cores written in both C/C++ and Go and apply them to 7 open-source cellular cores implementing 4G and 5G functionality. We identify two significant security vulnerabilities, including predictable generation of TMSIs and improper verification of TLS certificates, with each vulnerability affecting multiple cores. In identifying these flaws, we hope to correct implementations to fix downstream deployments and derivative proprietary projects.

Underwater data centers (UDCs) hold promise as next-generation data storage due to their energy efficiency and environmental sustainability benefits. While the natural cooling properties of water save power, the isolated aquatic environment and long-range sound propagation characteristics in water create unique vulnerabilities which differ from those of on-land data centers. Our research discovers the unique vulnerabilities of fault-tolerant storage devices, resource allocation software, and distributed file systems to acoustic injection attacks in UDCs. With a realistic testbed approximating UDC server operations, we empirically characterize the capabilities of acoustic injection underwater and find that an attacker can reduce fault-tolerant RAID 5 storage system throughput by 17% up to 100%. Our closed-water analyses reveal that an attacker can (i) cause unresponsiveness and automatic node removal in a distributed filesystem with only 2.4 minutes of sustained acoustic injection, (ii) induce a distributed database’s latency to increase by up to 92.7% to reduce system reliability, and (iii) induce load-balance managers to redirect up to 74% of resources to a target server to cause overload or force resource colocation. Furthermore, we perform open-water experiments in a lake and find that an attacker can cause controlled throughput degradation at the maximum allowable distance of 6.35 m using a commercial speaker. We also investigate and discuss the effectiveness of standard defenses against acoustic injection attacks. Finally, we formulate a novel machine learning-based detection system that reaches 0% False Positive Rate and 98.2% True Positive Rate trained on our dataset of profiled hard disk drives under 30-second FIO benchmark execution. With this work, we aim to help manufacturers proactively protect UDCs against acoustic injection attacks and ensure the security of subsea computing infrastructures.

5G technology transitions the cellular network core from specialized hardware into software-based cloud-native network functions (NFs). As part of this change, the 3GPP defines an access control policy to protect NFs from one another and third-party network applications. A manual review of this policy by the 3GPP identified an over-privilege flaw that exposes cryptographic keys to all NFs. Unfortunately, such a manual review is difficult due to ambiguous documentation. In this paper, we use static program analysis to extract NF functionality from four 5G core implementations and compare that functionality to what is permissible by the 3GPP policy. We discover two previously unknown instances of over-privilege that can lead denial-of-service and extract sensitive data. We have reported our findings to the GSMA, who has confirmed the significance of these policy flaws.

Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks [14]. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as prediction error, and black-box threat models. We propose that for interactive VR applications, it is essential to consider user-centric notions of utility and a variety of threat models. We develop a methodology to evaluate real-time privacy mechanisms for interactive VR applications that incorporate subjective user experience and task performance metrics. We evaluate selected privacy mechanisms using this methodology and find that re-identification accuracy can be decreased to as low as 14% while maintaining a high usability score and reasonable task performance. Finally, we elucidate three threat scenarios (black-box, black-box with exemplars, and white-box) and assess how well the different privacy mechanisms hold up to these adversarial scenarios. This work advances the state of the art in VR privacy by providing a methodology for end-to-end assessment of the risk of re-identification attacks and potential mitigating solutions. f.

Reproducibility is crucial to the advancement of science; it strengthens confidence in seemingly contradictory results and expands the boundaries of known discoveries. Computer Security has the natural benefit of creating artifacts that should facilitate computational reproducibility, the ability for others to use someone else's code and data to independently recreate results, in a relatively straightforward fashion. While the Security community has recently increased its attention on reproducibility, an independent and comprehensive measurement of the current state of reproducibility has not been conducted. In this paper, we perform the first such study, targeting reproducible artifacts generated specifically by papers on machine learning security (one of the most popular areas in academic research) published in Tier 1 security conferences over the past ten years (2013-2022). We perform our measurement study of indirect and direct reproducibility over nearly 750 papers, their codebases, and datasets. Our analysis shows that there is no statistically significant difference between the availability of artifacts before and after the introduction of Artifact Evaluation Committees in Tier 1 conferences. However, based on three years of results, artifacts that pass through this process work at a higher rate than those that do not. From our collected findings, we offer data-driven suggestions for improving reproducibility in our community, including five common problems observed in our study. In so doing, we demonstrate that significant progress still needs to be made in computational reproducibility in Computer Security research.

The growing worldwide attention toward large-scale subsea data centers has garnered substantial interest from commercial entities which have built and deployed underwater prototypes since 2015. These data centers utilize hard disk drives (HDDs) as a cost-effective method of data storage. However, researchers have demonstrated that acoustic waves can affect the availability and integrity of HDDs and applications that rely on them. These studies are all conducted in air on commercial laptops, hence their applicability and implications in submerged environments remain unexplored. In this position paper, we investigate potential vulnerabilities of storage devices deployed in underwater data centers and subsea storage platforms against targeted acoustic attacks. Based on our initial investigation of a simplified scenario, a victim HDD deployed in an enclosed submerged container is especially vulnerable to those acoustic attacks, which at frequencies ranging from 300 Hz 1300 Hz can result in up to 100% throughput loss and application crashes. Based on these findings, we argue that further study is necessary to assess underwater storage system security and develop effective defenses against overlooked acoustic attacks.

Audio Rogue Base Stations (ARBSs) allow an adversary to intercept cellular calls. These devices represent a substantial escalation in the threat posed by traditional rogue base stations, which only collect device identity information. This paper presents the first technique for detecting call eavesdropping via an ARBS. Our system, which we call LeopardSeal, uses distance bounding over the call audio channel to determine whether or not extra wireless hops (and therefore increased audio delay) that are characteristic of ARBSs are present during a call. We implement a proof of concept ARBS using open-source guides and perform a measurement study across the United States. We demonstrate the ability to detect all attacks (with zero false positives) due to a statistically significant difference in round trip times between benign and attack call audio (i.e., t-test: p ≪ 0.01) due to the large cost of additional wireless hops. Through this effort, we demonstrate the ability to robustly detect these eavesdropping devices.

Our research discovers how the rolling shutter and movable lens structures widely found in smartphone cameras modulate structure-borne sounds onto camera images, creating a point-of-view (POV) optical-acoustic side channel for acoustic eavesdropping. The movement of smartphone camera hardware leaks acoustic information because images unwittingly modulate ambient sound as imperceptible distortions. Our experiments find that the side channel is further amplified by intrinsic behaviors of Complementary Metal-oxide–Semiconductor (CMOS) rolling shutters and movable lenses such as in Optical Image Stabilization (OIS) and Auto Focus (AF). Our paper characterizes the limits of acoustic information leakage caused by structure-borne sound that perturbs the POV of smartphone cameras. In contrast with traditional optical-acoustic eavesdropping on vibrating objects, this side channel requires no line of sight and no object within the camera’s field of view (images of a ceiling suffice). Our experiments test the limits of this side channel with a novel signal processing pipeline that extracts and recognizes the leaked acoustic information. Our evaluation with 10 smartphones on a spoken digit dataset reports 80.66%, 91.28%, and 99.67% accuracies on recognizing 10 spoken digits, 20 speakers, and 2 genders respectively. We further systematically discuss the possible defense strategies and implementations. By modeling, measuring, and demonstrating the limits of acoustic eavesdropping from smartphone camera image streams, our contributions explain the physics-based causality and possible ways to reduce the threat on current and future devices.

Bluetooth is overwhelmingly the protocol of choice for personal area networking, and the Bluetooth Classic standard has been in continuous use for over 20 years. Bluetooth devices make themselves Discoverable to communicate, but best practice to protect privacy is to ensure that devices remain in Non-Discoverable mode. This paper demonstrates the futility of protecting devices by making them Non-Discoverable. We introduce the Blue’s Clues attack, which presents the first direct, non-disruptive approach to fully extracting the permanent, unique Bluetooth MAC identifier from targeted devices in Non-Discoverable mode. We also demonstrate that we can fully characterize device capabilities and retrieve identifiers, some of which we discover often contain identifying information about the device owner. We demonstrate Blue’s Clues using a software-defined radio and mounting the attack over the air against both our own devices and, with institutional approval, throughout a public building. We find that a wide variety of Bluetooth devices can be uniquely identified in less than 10 seconds on average, with affected devices ranging from smartphones and headphones to gas pump skimmers and nanny-cams, spanning all versions of the Bluetooth Classic standard. While we provide potential mitigation against attacks, Blue’s Clues forces a reassessment of over 20 years of best practices for protecting devices against discovery.

Confidential computing aims to secure the code and data in use by providing a Trusted Execution Environment (TEE) for applications using hardware features such as Intel SGX. Timing and cache side-channel attacks, however, are often outside the scope of the threat model, although once exploited they are able to break all the default security guarantees enforced by hardware. Unfortunately, tools detecting potential side-channel vulnerabilities within applications are limited and usually ignore the strong attack model and the unique programming model imposed by Intel SGX. This article proposes a precise side-channel analysis tool, ENCIDER, detecting both timing and cache side-channel vulnerabilities within SGX applications via inferring potential timing observation points and incorporating the SGX programming model into analysis. ENCIDER uses dynamic symbolic execution to decompose the side-channel requirement based on the bounded non-interference property and implements byte-level information flow tracking via API modeling. We have applied ENCIDER to 4 real-world SGX applications, 2 SGX crypto libraries, and 3 widely-used crypto libraries, and found 29 timing side channels and 73 code and data cache side channels. We also compare ENCIDER with three state-of-the-art side channel analysis tools using their benchmarks. ENCIDER does not only report most of the bugs with 20%-50% run time improvement and 65%-92% memory usage improvement, but also detects 9 missing bugs from these tools. We have reported our findings to the corresponding parties, e.g., Intel and ARM, who have confirmed most of the vulnerabilities detected.

Virtual and mixed-reality (XR) technology has advanced significantly in the last few years and will enable the future of work, education, socialization, and entertainment. Eye-tracking data is required for supporting novel modes of interaction, animating virtual avatars, and implementing rendering or streaming optimizations. While eye tracking enables many beneficial applications in XR, it also introduces a risk to privacy by enabling re-identification of users. We applied privacy definitions of k-anonymity and plausible deniability (PD) to datasets of eye-tracking samples and evaluated them against the state-of-the-art differential privacy (DP) approach. Two VR datasets were processed to reduce identification rates while minimizing the impact on the performance of trained machine-learning models. Our results suggest that both PD and DP mechanisms produced practical privacy-utility trade-offs with respect to re-identification and activity classification accuracy, while k-anonymity performed best at retaining utility for gaze prediction.

Eye-tracking is a critical source of information for understanding human behavior and developing future mixed-reality technology. Eye-tracking enables applications that classify user activity or predict user intent. However, eye-tracking datasets collected during common virtual reality tasks have also been shown to enable unique user identification, which creates a privacy risk. In this paper, we focus on the problem of user re-identification from eye-tracking features. We adapt standardized privacy definitions of k-anonymity and plausible deniability to protect datasets of eye-tracking features, and evaluate performance against re-identification by a standard biometric identification model on seven VR datasets. Our results demonstrate that re-identification goes down to chance levels for the privatized datasets, even as utility is preserved to levels higher than 72% accuracy in document type classification.

The Android operating system is currently the most popular mobile operating system in the world. Android is based on Linux and therefore inherits its features including its Inter-Process Communication (IPC) mechanisms. These mechanisms are used by processes to communicate with one another and are extensively used in Android. While Android-specific IPC mechanisms have been studied extensively, Unix domain sockets have not been examined comprehensively, despite playing a crucial role in the IPC of highly privileged system daemons. In this paper, we propose Sausage, an efficient novel static analysis framework to study the security properties of these sockets. Sausage considers access control policies implemented in the Android security model, as well as authentication checks implemented by the daemon binaries. It is a fully static analysis framework, specifically designed to analyze Unix domain socket usage in Android system daemons, at scale. We use this framework to analyze 200 Android images across eight popular smartphone vendors spanning Android versions 7–9. As a result, we uncover multiple access control misconfigurations and insecure authentication checks. Our notable findings include a permission bypass in highly privileged Qualcomm system daemons and an unprotected socket that allows an untrusted app to set the scheduling priority of other processes running on the system, despite the implementation of mandatory SELinux policies. Ultimately, the results of our analysis are worrisome; all vendors except the Android Open Source Project (AOSP) have access control issues, allowing an untrusted app to communicate to highly privileged daemons through Unix domain sockets introduced by hardware manufacturer or vendor customization.

The emergence of language between artificial agents is a recent focus of computational linguistics, as it offers a synthetic substrate for reasoning about human language evolution.From the perspective of cognitive science, sophisticated categorization in humans is thought to enable reasoning about novel observations, and thus compose old information to describe new phenomena.Unfortunately, the literature to date has not managed to isolate the effect of categorization power in artificial agents on their inter-communication ability, particularly on novel, unseen objects.In this work, we propose the use of disentangled representations from representation learning to quantify the categorization power of agents, enabling a differential analysis between combinations of heterogeneous systems, e.g., pairs of agents which learn to communicate despite mismatched concept realization.Through this approach, we observe that agent heterogeneity can cut signaling accuracy by up to 40%, despite encouraging compositionality in the artificial language.We conclude that the reasoning process of agents plays a key role in their communication, with unexpected benefits arising from their mixing, such as better language compositionality.

Universal Serial Bus (USB) is the de facto protocol supported by peripherals and mobile devices, such as USB thumb drives and smart phones. For many devices, USB Type-C ports are the primary interface for charging, file transfer, audio, video, etc. Accordingly, attackers have exploited different vulnerabilities within USB stacks, compromising host machines via BadUSB attacks or jailbreaking iPhones from USB connections. While there exist fuzzing frameworks dedicated to USB vulnerability discovery, all of them focus on USB host stacks and ignore USB gadget stacks, which enable all the features within modern peripherals and smart devices. In this paper, we propose FuzzUSB, the first fuzzing framework for the USB gadget stack within commodity OS kernels, leveraging static analysis, symbolic execution, and stateful fuzzing. FuzzUSB combines static analysis and symbolic execution to extract internal state machines from USB gadget drivers, and uses them to achieve state-guided fuzzing through multi-channel inputs. We have implemented FuzzUSB upon the syzkaller kernel fuzzer and applied it to the most recent mainline Linux, Android, and FreeBSD kernels. As a result, we have found 34 previously unknown bugs within the Linux and Android kernels, and opened 8 CVEs. Furthermore, compared to the baseline, FuzzUSB has also demonstrated different improvements, including $ 3\times$ higher code coverage, $ 50\times$ improved bug-finding efficiency for Linux USB gadget stacks, $ 2\times$ higher code coverage for FreeBSD USB gadget stacks, and reproducing known bugs that could not be detected by the baseline fuzzers. We believe FuzzUSB provides developers a powerful tool to thwart USB-related vulnerabilities within modern devices and complete the current USB fuzzing scope.

Stalkerware is a form of malware that allows for the abusive monitoring of intimate partners. Primarily deployed on information-rich mobile platforms, these malicious applications allow for collecting information about a victim’s actions and behaviors, including location data, call audio, text messages, photos, and other personal details. While stalkerware has received increased attention from the security community, the ways in which stalkerware authors monetize their efforts have not been explored in depth. This paper represents the first large-scale technical analysis of monetization within the stalkerware ecosystem. We analyze the code base of 6,432 applications collected by the Coalition Against Stalkerware to determine their monetization strategies. We find that while far fewer stalkerware apps use ad libraries than normal apps, 99% of those that do use Google AdMob. We also find that payment services range from traditional in-app billing to cryptocurrency. Finally, we demonstrate that Google’s recent change to their Terms of Service (ToS) did not eliminate these applications, but instead caused a shift to other payment processors, while the apps can still be found on the Play Store; we verify through emulation that these apps often operate in blatant contravention of the ToS. Through this analysis, we find that the heterogeneity of markets and payment processors means that while point solutions can have impact on monetization, a multi-pronged solution involving multiple stakeholders is necessary to mitigate the financial incentive for developing stalkerware.

Secure deletion in flash-based storage is crucial for data security. However, existing secure deletion schemes for flash memory suffer from performance degradation and reliability issues and cannot provide secure deletion guarantees. Although emerging nonvolatile memory (NVM) allows in-place updates and provides high performance, it is unable to fully replace flash memory and, thus, cannot solve the secure deletion problem. In this article, we propose NVM-assisted secure deletion scheme for flash memory (NASA), a stale-free storage system that combines NVM and flash memory to provide immediate secure deletion without significant performance degradation in SSDs. NASA uses block erasure to provide secure deletion guarantees for flash memory and exploits NVM to conceal time-consuming erasure operations. We demonstrate that unless the unique characteristics of NVM are considered, schemes that merely implement existing approaches to secure deletion will end up with stale data replicas within their storage media. Moreover, we evaluate NASA with different real-world workloads and demonstrate that NASA increases the average latency by 0.01% compared to LRU and decreases 2.1% average latency over the FIFO caching policy. NASA is a novel storage system that provides strong secure deletion guarantees with high performance.

The number of Internet of Things (IoT) has reached 7 billion globally in early 2018 and are nearly ubiquitous in daily life. Knowing whether or not these devices are safe and secure to use is becoming critical. IoT devices usually implement communication protocols such as USB and Bluetooth within firmware to allow a wide range of functionality. Thus analyzing firmware using domain knowledge from these protocols is vital to understand device behavior, detect implementation bugs, and identify malicious components. Unfortunately, due to the complexity of these protocols, there is usually no formal specification available that can help automate the firmware analysis; as a result significant manual effort is currently required to study these protocols and to reverse engineer the device firmware. In this paper, we propose a new firmware analysis methodology using symbolic execution called ProXray, which can learn a protocol model from known firmware, and apply the model to recognize the protocol relevant fields and detect functionality within unknown firmware automatically. After the training phase, ProXray can fully automate the firmware analysis process while supporting user's queries in the form of protocol relevant constraints. We have applied ProXray to the USB and the Bluetooth protocols by learning protocol constraint models from firmware that implement these protocols. We are then able to map protocol fields and identify USB functionality automatically within all 6 unknown USB firmware while achieving more than an order of magnitude speedup in reaching protocol relevant targets in unknown Bluetooth firmware. Our model achieved high coverage of the USB and Bluetooth specifications for several important protocol fields. ProXray provides a new method to apply domain knowledge to firmware analysis automatically.

Eye-tracking technology is being increasingly integrated into mixed reality devices. Although critical applications are being enabled, there are significant possibilities for violating user privacy expectations. We show that there is an appreciable risk of unique user identification even under natural viewing conditions in virtual reality. This identification would allow an app to connect a user's personal ID with their work ID without needing their consent, for example. To mitigate such risks we propose a framework that incorporates gatekeeping via the design of the application programming interface and via software-implemented privacy mechanisms. Our results indicate that these mechanisms can reduce the rate of identification from as much as 85% to as low as 30%. The impact of introducing these mechanisms is less than 1.5° error in gaze position for gaze prediction. Gaze data streams can thus be made private while still allowing for gaze prediction, for example, during foveated rendering. Our approach is the first to support privacy-by-design in the flow of eye-tracking data within mixed reality use cases.

A protocol for two-party secure function evaluation (2P-SFE) aims to allow the parties to learn the output of function f of their private inputs, while leaking nothing more. In a sense, such a protocol realizes a trusted oracle that computes f and returns the result to both parties. There have been tremendous strides in efficiency over the past ten years, yet 2P-SFE protocols remain impractical for most real-time, online computations, particularly on modestly provisioned devices. Intel's Software Guard Extensions (SGX) provides hardware-protected execution environments, called enclaves, that may be viewed as trusted computation oracles. While SGX provides native CPU speed for secure computation, previous side-channel and micro-architecture attacks have demonstrated how security guarantees of enclaves can be compromised. In this paper, we explore a balanced approach to 2P-SFE on SGX-enabled processors by constructing a protocol for evaluating f relative to a partitioning of f. This approach alleviates the burden of trust on the enclave by allowing the protocol designer to choose which components should be evaluated within the enclave, and which via standard cryptographic techniques. We describe SGX-enabled SFE protocols (modeling the enclave as an oracle), and formalize the strongest-possible notion of 2P-SFE for our setting. We prove our protocol meets this notion when properly realized. We implement the protocol and apply it to two practical problems: privacy-preserving queries to a database, and a version of Dijkstra's algorithm for privacy-preserving navigation. Our evaluation shows that our SGX-enabled SFE scheme enjoys a 38x increase in performance over garbled-circuit-based SFE. Finally, we justify modeling of the enclave as an oracle by implementing protections against known side-channels.

In July 2018, over a decade after the DES encryption algorithm was retired, 3DES was also officially deprecated. While previous work suggests a successful deprecation of DES, with fewer than 1% of observed SSL/TLS handshakes using some form of DES up until 2018, such work tends to be limited in scope and does not necessarily capture the true persistence of DES across the entire TLS ecosystem. In this paper, we actively investigate online support for DES and DES-derivative ciphers by querying IP addresses responsive to port 443 connection attempts. To achieve this, we design and implement our own Internet scanning tool built upon ZMap and attempt to negotiate handshakes exclusively using DES ciphers. In total, we have scanned over 31 million unique IP addresses and found that nearly half of them can still successfully establish an HTTPS connection using at least one DES cipher. Moreover, we also find that many servers still support DES40 (which can be broken in seconds) and anon ciphers (which offer no certificate verification and are vulnerable to man-in-the-middle attacks). Our investigation demonstrates the biases and misunderstandings in previous weak cipher studies within the TLS ecosystem, and discloses the severity of this problem by targeting DES-based cipher suites.

With close to native performance, Linux containers are becoming the de facto platform for cloud computing. While various solutions have been proposed to secure applications and containers in the cloud environment by leveraging Intel SGX, most cloud operators do not yet offer SGX as a service. This is likely due to a number of security, scalability, and usability concerns coming from both cloud providers and users. Cloud operators worry about the security guarantees of unofficial SDKs, limited support for remote attestation within containers, limited physical memory for the Enclave Page Cache (EPC) making it difficult to support hundreds of enclaves, and potential DoS attacks against EPC by malicious users. Meanwhile, end users need to worry about careful program partitioning to reduce the TCB and adapting legacy applications to use SGX. We note that most of these concerns are the result of an incomplete infrastructure, from the OS to the application layer. We address these concerns with lxcsgx, which allows SGX applications to run inside containers while also: enabling SGX remote attestation for containerized applications, enforcing EPC memory usage control on a per-container basis, providing a general software TPM using SGX to augment legacy applications, and supporting partitioning with a GCC plugin. We then retrofit Nginx/OpenSSL and Memcached using the software TPM and SGX partitioning to defend against known and potential attacks. Thanks to the small EPC footprint of each enclave, we are able to run up to 100 containerized Memcached instances without EPC swapping. Our evaluation shows the overhead introduced by lxcsgx is less than 6.9% for simple SGX applications, 9.5% for Nginx/OpenSSL, and 20.9% for containerized Memcached.

Modern computer peripherals are diverse in their capabilities and functionality, ranging from keyboards and printers to smartphones and external GPUs. In recent years, peripherals increasingly connect over a small number of standardized communication protocols, including USB, Bluetooth, and NFC. The host operating system is responsible for managing these devices; however, malicious peripherals can request additional functionality from the OS resulting in system compromise, or can craft data packets to exploit vulnerabilities within OS software stacks. Defenses against malicious peripherals to date only partially cover the peripheral attack surface and are limited to specific protocols (e.g., USB). In this paper, we propose Linux (e)BPF Modules (LBM), a general security framework that provides a unified API for enforcing protection against malicious peripherals within the Linux kernel. LBM leverages the eBPF packet filtering mechanism for performance and extensibility and we provide a high-level language to facilitate the development of powerful filtering functionality. We demonstrate how LBM can provide host protection against malicious USB, Bluetooth, and NFC devices; we also instantiate and unify existing defenses under the LBM framework. Our evaluation shows that the overhead introduced by LBM is within 1 μs per packet in most cases, application and system overhead is negligible, and LBM outperforms other state-of-the-art solutions. To our knowledge, LBM is the first security framework designed to provide comprehensive protection against malicious peripherals within the Linux kernel.

Access to credit can provide capital crucial to both businesses and individuals. Unfortunately, for large parts of the developing world, access to credit is not available because customers often lack the traditional data used by lenders to make such decisions (e.g., verifiable payroll statements, property ownership documents). Emerging online credit services address this need through the use of non-traditional creditworthiness data, which many believe to include user geolocation and social network information. While such systems both potentially expand credit availability and improve usability through instant evaluation, their security and privacy practices remain opaque. In this paper, we perform the first comprehensive security analysis of the emerging online credit space. To provide improved transparency, we select 51 representative companies across the industry, analyze their privacy policies and compare them to the sensitive data types mobile applications actually gather. We then evaluate the configuration of connections between mobile apps and their supporting servers to determine whether they securely handle such data. Our analysis demonstrates significant security and privacy issues across this burgeoning industry, including the gathering of previously undisclosed data types and widespread mis-configuration of encryption. We conclude by discussing our efforts to work with partners in and around the industry to improve these issues.

In cooperative spectrum sensing, multiple sensors work together to perform tasks such as localizing a target transmitter. However, the exchange of spectrum measurements leads to exposure of the physical location of participating sensors. Furthermore, in some cases, the sensitive characteristics of all participants can be revealed through the compromise of any one sensor. Accordingly, without guarantees about how data will be handled, there is little reason for such devices to work together. In this work, we protect the location of sensors cooperating in spectrum sensing by processing measurements within attestable containers, or enclaves. We use the enclave as a building block for new privacy-preserving particle filter protocols. We instantiate this enclave using Intel Software Guard Extensions (SGX) and investigate how the inclusion of enclaves impacts sensor privacy, carefully enumerating the different threats present in centralized and decentralized architectures. We show that enclave-based particle filter protocols incur minimal overhead (adding 16 milliseconds of processing to the measurement processing function versus unprotected computation), whereas cryptographically-based approaches suffer from multiple orders of magnitude greater costs. Our work demonstrates that enclaves can be effectively deployed in a decentralized architecture while dramatically improving current data handling techniques.

Mobile devices are more connected than ever before through the use of multiple wireless protocols, including the 2G, 3G, and 4G cellular standards. To manage and interact with cellular networks, phones use dedicated and highly proprietary baseband processors running custom, closed-source firmware. Despite the increasing complexity of modern cellular standards, there is no reference implementation, leading individual baseband manufacturers to create their own in-house versions. The proprietary nature of baseband firmware combined with the complexity of standards has created a barrier for researchers to comprehensively audit the security of these implementations. To address this, we present SpikerXG, an extensible, baseband testing platform that employs firmware instrumentation to intelligently target protocol messages.

When two or more parties need to compute a common result while safeguarding their sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled circuits. The traditional enabler of SMC is cryptography, but the significant number of cryptographic operations required results in these techniques being impractical for most real-time, online computations. Trusted execution environments (TEEs) provide hardware-enforced isolation of code and data in use, making them promising candidates for making SMC more tractable. This paper revisits the history of improvements to SMC over the years and considers the possibility of coupling trusted hardware with SMC. This paper also addresses three open challenges: (1) defeating malicious adversaries, (2) mobile-friendly TEE-supported SMC, and (3) a more general coupling of trusted hardware and privacy-preserving computation.

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.

This panel will explore how security topics are integrated into academic programs and future directions for improvements. It will address how early in time security should be introduced in programs like computer science and software engineering; and identify the critical takeaways that each graduating student should learn. We will try to separate out the important, practical concepts from the purely academic ones. We will also consider how well security programs translate to industry-focused needs: do students emerge with an understanding that is both deep and broad enough to be useful? In general, we will try to identify the pitfalls of current security education and how we can move forward as an academic community, in tandem with industry and government.

Data breaches represent a significant threat to organizations. While the general problem of protecting data has received much attention, one large (and growing) class has not - data that must be kept due to mandatory retention laws. Such data is often of little use to an organization, is rarely accessed, and represents a significant potential liability, yet cannot be discarded. Protecting such data entails an unusual combination of practical constraints (such as providing verification to a party that may be unknown) and thus requires functionality that is not well addressed by traditional cryptographic primitives. We propose to mitigate the risk to such data through a new system called Dragchute, which creates a time window during which locked data cannot be accessed by anyone. Based on a verifiable non-interactive, non-parallelizable, time-delay key escrow mechanism, Dragchute is novel in that it requires that no cryptographic material capable of providing early access to the data be retained, yet provides verification for multiple properties. We define a base construction for Dragchute, show possible extensions that help meet additional verification requirements, and characterize its performance. Our results show that Dragchute systems offer verifiable, customizable, computational protection against data exposure for encryption costs similar to traditional methods (e.g., less than 6% overhead compared to AEAD). We thus show that Dragchute systems provide a critical new means for protecting data that must be retained long term due to mandatory retention laws.

The global telephone network is relied upon by billions every day. Central to its operation is the Signaling System 7 (SS7) protocol, which is used for setting up calls, managing mobility, and facilitating many other network services. This protocol was originally built on the assumption that only a small number of trusted parties would be able to directly communicate with its core infrastructure. As a result, SS7 - as a feature - allows all parties with core access to redirect and intercept calls for any subscriber anywhere in the world. Unfortunately, increased interconnectivity with the SS7 network has led to a growing number of illicit call redirection attacks. We address such attacks with Sonar, a system that detects the presence of SS7 redirection attacks by securely measuring call audio round-trip times between telephony devices. This approach works because redirection attacks force calls to travel longer physical distances than usual, thereby creating longer end-to-end delay. We design and implement a distance bounding-inspired protocol that allows us to securely characterize the round-trip time between the two endpoints. We then use custom hardware deployed in 10 locations across the United States and a redirection testbed to characterize how distance affects round trip time in phone networks. We develop a model using this testbed and show Sonar is able to detect 70.9% of redirected calls between call endpoints of varying attacker proximity (300-7100 miles) with low false positive rates (0.3%). Finally, we ethically perform actual SS7 redirection attacks on our own devices with the help of an industry partner to demonstrate that Sonar detects 100% of such redirections in a real network (with no false positives). As such, we demonstrate that telephone users can reliably detect SS7 redirection attacks and protect the integrity of their calls.

USB-based attacks have increased in complexity in recent years. Modern attacks now incorporate a wide range of attack vectors, from social engineering to signal injection. To address these challenges, the security community has responded with a growing set of fragmented defenses. In this work, we survey and categorize USB attacks and defenses, unifying observations from both peer-reviewed research and industry. Our systematization extracts offensive and defensive primitives that operate across layers of communication within the USB ecosystem. Based on our taxonomy, we discover that USB attacks often abuse the trust-by-default nature of the ecosystem, and transcend different layers within a software stack; none of the existing defenses provide a complete solution, and solutions expanding multiple layers are most effective. We then develop the first formal verification of the recently released USB Type-C Authentication specification, and uncover fundamental flaws in the specification's design. Based on the findings from our systematization, we observe that while the spec has successfully pinpointed an urgent need to solve the USB security problem, its flaws render these goals unattainable. We conclude by outlining future research directions to ensure a safer computing experience with USB.

Smartphones are a critical device in modern society. With the amount of personal data present on many smartphones, protecting their integrity is crucial. The Android operating system employs multiple layers of security to ensure that the system is resistant to local and remote threats. To achieve this it uses a combination of discretionary and mandatory access control policies. Attackers when attempting to escalate to a higher privilege level must operate within these policies and potentially exploit their flaws. The flow an attacker (and conversely a defender) follows can be modeled as attack graph. In this paper, we explore how attack graphs could be automatically generated from SEAndroid policy files to aid defenders and attackers with auditing system security posture.

The USB protocol has become ubiquitous, supporting devices from high-powered computing devices to small embedded devices and control systems. USB's greatest feature, its openness and expandability, is also its weakness, and attacks such as BadUSB exploit the unconstrained functionality afforded to these devices as a vector for compromise. Fundamentally, it is virtually impossible to know whether a USB device is benign or malicious. This work introduces FirmUSB, a USB-specific firmware analysis framework that uses domain knowledge of the USB protocol to examine firmware images and determine the activity that they can produce. Embedded USB devices use microcontrollers that have not been well studied by the binary analysis community, and our work demonstrates how lifters into popular intermediate representations for analysis can be built, as well as the challenges of doing so. We develop targeting algorithms and use domain knowledge to speed up these processes by a factor of 7 compared to unconstrained fully symbolic execution. We also successfully find malicious activity in embedded 8051 firmwares without the use of source code. Finally, we provide insights into the challenges of symbolic analysis on embedded architectures and provide guidance on improving tools to better handle this important class of devices.

Detecting and explaining the nature of attacks in distributed web services is often difficult -- determining the nature of suspicious activity requires following the trail of an attacker through a chain of heterogeneous software components including load balancers, proxies, worker nodes, and storage services. Unfortunately, existing forensic solutions cannot provide the necessary context to link events across complex workflows, particularly in instances where application layer semantics (e.g., SQL queries, RPCs) are needed to understand the attack. In this work, we present a transparent provenance-based approach for auditing web services through the introduction of Network Provenance Functions (NPFs). NPFs are a distributed architecture for capturing detailed data provenance for web service components, leveraging the key insight that mediation of an application's protocols can be used to infer its activities without requiring invasive instrumentation or developer cooperation. We design and implement NPF with consideration for the complexity of modern cloud-based web services, and evaluate our architecture against a variety of applications including DVDStore, RUBiS, and WikiBench to show that our system imposes as little as 9.3% average end-to-end overhead on connections for realistic workloads. Finally, we consider several scenarios in which our system can be used to concisely explain attacks. NPF thus enables the hassle-free deployment of semantically rich provenance-based auditing for complex applications workflows in the Cloud.

The basis for all IPv4 network communication is the address resolution protocol (ARP), which maps an IP address to a device's media access control identifier. ARP has long been recognized as vulnerable to spoofing and other attacks, and past proposals to secure the protocol have often involved in modifying the basic protocol. Similarly, neighbor discovery protocol (NDP) is the basis for all IPv6 network communication, yet suffers from the same vulnerabilities as ARP. This paper introduces arpsec, a secure ARP/RARP protocol suite which does not require protocol modification, enables continual verification of the identity of the target (respondent) machine by introducing an address binding repository derived using a formal logic that bases additions to a host's ARP cache on a set of operational rules and properties, utilizes the trusted platform module (TPM), a commodity component now present in the vast majority of modern computers, to augment the logic-prover-derived assurance when needed, with TPM-facilitated attestations of system state achieved at viably low-processing cost, and supports IPv6 NDP (ndpsec) by extension of our previous work. Using commodity TPMs as our attestation base, we show that arpsec incurs an overhead ranging from 7% to 15.4% over the standard Linux ARP implementation, a comparable overhead against the standard Linux NDP implementation, and provides a first step towards a formally secure and trustworthy networking stack for both IPv4 and IPv6.

Mobile money, also known as branchless banking, leverages ubiquitous cellular networks to bring much-needed financial services to the unbanked in the developing world. These services are often deployed as smartphone apps, and although marketed as secure, these applications are often not regulated as strictly as traditional banks, leaving doubt about the truth of such claims. In this article, we evaluate these claims and perform the first in-depth measurement analysis of branchless banking applications. We first perform an automated analysis of all 46 known Android mobile money apps across the 246 known mobile money providers from 2015. We then perform a comprehensive manual teardown of the registration, login, and transaction procedures of a diverse 15% of these apps. We uncover pervasive vulnerabilities spanning botched certification validation, do-it-yourself cryptography, and other forms of information leakage that allow an attacker to impersonate legitimate users, modify transactions, and steal financial records. These findings show that the majority of these apps fail to provide the protections needed by financial services. In an expanded re-evaluation one year later, we find that these systems have only marginally improved their security. Additionally, we document our experiences working in this sector for future researchers and provide recommendations to improve the security of this critical ecosystem. Finally, through inspection of providers’ terms of service, we also discover that liability for these problems unfairly rests on the shoulders of the customer, threatening to erode trust in branchless banking and hinder efforts for global financial inclusion.

Provenance is an increasingly important tool for understanding and even actively preventing system intrusion, but the excessive storage burden imposed by automatic provenance collection threatens to undermine its value in practice. This situation is made worse by the fact that the majority of this metadata is unlikely to be of interest to an administrator, instead describing system noise or other background activities that are not germane to the forensic investigation. To date, storing data provenance in perpetuity was a necessary concession in even the most advanced provenance tracking systems in order to ensure the completeness of the provenance record for future analyses. In this work, we overcome this obstacle by proposing a policy-based approach to provenance filtering , leveraging the confinement properties provided by Mandatory Access Control (MAC) systems in order to identify and isolate subdomains of system activity for which to collect provenance. We introduce the notion of minimal completeness for provenance graphs, and design and implement a system that provides this property by exclusively collecting provenance for the trusted computing base of a target application. In evaluation, we discover that, while the efficacy of our approach is domain dependent, storage costs can be reduced by as much as 89% in critical scenarios such as provenance tracking in cloud computing data centers. To the best of our knowledge, this is the first policy-based provenance monitor to appear in the literature.

Defenders of enterprise networks have a critical need to quickly identify the root causes of malware and data leakage. Increasingly, USB storage devices are the media of choice for data exfiltration, malware propagation, and even cyber-warfare. We observe that a critical aspect of explaining and preventing such attacks is understanding the provenance of data (i.e., the lineage of data from its creation to current state) on USB devices as a means of ensuring their safe usage. Unfortunately, provenance tracking is not offered by even sophisticated modern devices. This work presents ProvUSB, an architecture for fine-grained provenance collection and tracking on smart USB devices. ProvUSB maintains data provenance by recording reads and writes at the block layer and reliably identifying hosts editing those blocks through attestation over the USB channel. Our evaluation finds that ProvUSB imposes a one-time 850 ms overhead during USB enumeration, but approaches nearly-bare-metal runtime performance (90% of throughput) on larger files during normal execution, and less than 0.1% storage overhead for provenance in real-world workloads. ProvUSB thus provides essential new techniques in the defense of computer systems and USB storage devices.

Recent developments in secure computation have led to significant improvementsin efficiency and functionality. These efforts created compilers that form thebackbone of practical secure computation research. Unfortunately, many of theartifacts that are being used to demonstrate new research for secure computationare incomplete, incorrect, or unstable, leading to demonstrably erroneousresults and inefficiencies - extending even to the most recently developedcompiler systems. This is a problem because it hampers research and underminesfeasibility tests when other researchers attempt to use these tools. We addressthese problems and present Frigate, a principled compiler and fast circuitinterpreter for secure computation. To ensure correctness we apply bestpractices for compiler design and development, including the use of standarddata structures, helpful negative results, and structured validation testing. Our systematic validation tests include checks on the internal compiler state, combinations of operators, and edge cases based on widely used techniques anderrors we have observed in other work. This produces a compiler that buildscorrect circuits, is efficient and extensible. Frigate creates circuits withgate counts comparable to previous work, but does so with compile time speedupsas high as 447x compared with the best results from previous work in circuit compilers. By creating avalidated tool, our compiler will allow future secure computationimplementations to be developed quickly and correctly.

Ransomware is a growing threat that encrypts auser's files and holds the decryption key until a ransom ispaid by the victim. This type of malware is responsible fortens of millions of dollars in extortion annually. Worse still, developing new variants is trivial, facilitating the evasion of manyantivirus and intrusion detection systems. In this work, we presentCryptoDrop, an early-warning detection system that alerts a userduring suspicious file activity. Using a set of behavior indicators, CryptoDrop can halt a process that appears to be tampering witha large amount of the user's data. Furthermore, by combininga set of indicators common to ransomware, the system can beparameterized for rapid detection with low false positives. Ourexperimental analysis of CryptoDrop stops ransomware fromexecuting with a median loss of only 10 files (out of nearly5,100 available files). Our results show that careful analysis ofransomware behavior can produce an effective detection systemthat significantly mitigates the amount of victim data loss.

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.

Garbled circuits provide a powerful tool for jointly evaluating functions while preserving the privacy of each user’s inputs. While recent research has made the use of this primitive more practical, such solutions generally assume that participants are symmetrically provisioned with massive computing resources. In reality, most people on the planet only have access to the comparatively sparse computational resources associated with their mobile phones, and those willing and able to pay for access to public cloud computing infrastructure cannot be assured that their data will remain unexposed. We address this problem by creating a new SFE protocol that allows mobile devices to securely outsource the majority of computation required to evaluate a garbled circuit. Our protocol, which builds on the most efficient garbled circuit evaluation techniques, includes a new outsourced oblivious transfer primitive that requires significantly less bandwidth and computation than standard OT primitives and outsourced input validation techniques that force the cloud to prove that it is executing all protocols correctly. After showing that our extensions are secure in the malicious model, we conduct an extensive performance evaluation for a number of standard SFE test applications as well as a privacy-preserving navigation application designed specifically for the mobile use-case. Our system reduces execution time by 98.92% and bandwidth by 99.95% for the edit distance problem of size 128 compared to non-outsourced evaluation. These results show that even the least capable devices are capable of using large garbled circuits for secure computation.

Abstract Secure multiparty computation (SMC) offers a technique to preserve functionality and data privacy in mobile applications. Current protocols that make this costly cryptographic construction feasible on mobile devices securely outsource the bulk of the computation to a cloud provider. However, these outsourcing techniques are built on specific secure computation assumptions and tools, and applying new SMC ideas to the outsourced setting requires the protocols to be completely rebuilt and proven secure. In this work, we develop a generic technique for lifting any secure two‐party computation protocol into an outsourced two‐party SMC protocol. By augmenting the function being evaluated with auxiliary consistency checks and input values, we can create an outsourced protocol with low overhead cost. Our implementation and evaluation show that in the best case our outsourcing additions execute within the confidence intervals of two servers running the same computation and consume approximately the same bandwidth. In addition, the mobile device itself uses minimal bandwidth over a single round of communication. This work demonstrates that efficient outsourcing is possible with any underlying SMC scheme and provides an outsourcing protocol that is efficient and directly applicable to current and future SMC techniques. Copyright © 2016 John Wiley & Sons, Ltd.

USB attacks are becoming more sophisticated. Rather than using USB devices solely as a delivery mechanism for host-side exploits, attackers are targeting the USB stack itself, embedding malicious code in device firmware to covertly request additional USB interfaces, providing unacknowledged and malicious functionality that lies outside the apparent purpose of the device. This allows for attacks such as BadUSB, where a USB storage device with malicious firmware is capable of covertly acting as a keyboard as well, allowing it to inject malicious scripts into the host machine. We observe that the root cause of such attacks is that the USB Stack exposes a set of unrestricted device privileges and note that the most reliable information about a device's capabilities comes from the end user's expectation of the device's functionality. We design and implement GoodUSB, a mediation architecture for the Linux USB Stack. We defend against BadUSB attacks by enforcing permissions based on user expectations of device functionality. GoodUSB includes a security image component to simplify use, and a honeypot mechanism for observing suspicious USB activities. GoodUSB introduces only 5.2% performance overhead compared to the unmodified Linux USB subsystem. It is an important step forward in defending against USB attacks and towards allowing the safe deployment of USB devices in the enterprise.

The basis for all IPv4 network communication is the Address Resolution Protocol (ARP), which maps an IP address to a device's Media Access Control (MAC) identifier. ARP has long been recognized as vulnerable to spoofing and other attacks, and past proposals to secure the protocol have often involved modifying the basic protocol.

Abstract In many democratic countries, Communications Assistance for Law Enforcement Act (CALEA) wiretaps are used by law enforcement agencies to perform investigations and gather evidence for legal procedures. However, existing CALEA wiretap implementations are often engineered with the assumption that wiretap operators are trustworthy and wiretap targets do not attempt to evade the wiretap. Although it may be possible to construct more robust wiretap architectures by reengineering significant portions of the telecommunications infrastructure, such efforts are prohibitively costly. This paper instead proposes a lightweight accountable wiretapping system for enabling secure audits of existing CALEA wiretapping systems. Our proposed system maintains a tamper-evident encrypted log over wiretap events, enforces access controls over wiretap records, and enables privacy-preserving aggregate queries and compliance checks. We demonstrate using campus-wide telephone trace data from a large university that our approach provides efficient auditing functionalities while incurring only modest overhead. Based on publicly available wiretap reporting statistics, we conservatively estimate that our architecture can support tamper-evident logging for all of the United States’ ongoing CALEA wiretaps using three commodity PCs.

Recent discoveries of widespread vulnerabilities in the SSL/TLS protocol stack, particular with regard to the verification of server certificates, has left the security of the Internet's communications in doubt. Newly proposed SSL trust enhancements address many of these vulnerabilities, but are slow to be deployed and do not solve the problem of securing existing software. In this work, we provide new mechanisms that offer immediate solutions to addressing vulnerabilities in legacy code. We introduce CertShim, a lightweight retrofit to SSL implementations that protects against SSL vulnerabilities, including those surveyed by Georgiev et. al., in a manner that is transparent to the application. We demonstrate CertShim's extensibility by adapting it to work with Convergence, DANE, and Client-Based Key Pinning. CertShim imposes just 20 ms overhead for an SSL verification call, and hooks the SSL dependencies of 94% of Ubuntu's most popular packages with no changes necessary to existing applications. This work significantly increases system-wide security of SSL communications in non-browser software, while simultaneously reducing the barriers to evaluating and adopting the myriad alternative proposals to the certificate authority system.

Two-party secure-function evaluation (SFE) has become significantly more feasible, even on resource-constrained devices, because of advances in server-aided computation systems. However, there are still bottlenecks, particularly in the input-validation stage of a computation. Moreover, SFE research has not yet devoted sufficient attention to the important problem of retaining state after a computation has been performed so that expensive processing does not have to be repeated if a similar computation is done again. This paper presents PartialGC, an SFE system that allows the reuse of encrypted values generated during a garbled-circuit computation. We show that using PartialGC can reduce computation time by as much as 96% and bandwidth by as much as 98% in comparison with previous outsourcing schemes for secure computation. We demonstrate the feasibility of our approach with two sets of experiments, one in which the garbled circuit is evaluated on a mobile device and one in which it is evaluated on a server. We also use PartialGC to build a privacy-preserving ``friend-finder'' application for Android. The reuse of previous inputs to allow stateful evaluation represents a new way of looking at SFE and further reduces computational barriers.

The certificate authority (CA) PKI system has been used for decades as a means of providing domain identity verification services throughout the Internet, but a growing body of evidence suggests that our trust in this system is misplaced. A recently proposed CA alternative, Convergence, extends the Network Perspectives system of multi-path probing to perform certificate verification. Unfortunately, adoption of Convergence and other SSL/TLS trust enhancements has been slow, in part because it is unknown how these systems perform against large workloads and realistic conditions.

As organizations become increasingly reliant on cloud computing for servicing their data storage requirements, the need to govern access control at finer granularities becomes particularly important. This challenge is increased by the lack of policy supporting data migration across geographic boundaries and through organizations with divergent regulatory policies. In this paper, we present an architecture for secure and distributed management of provenance, enabling its use in security-critical applications. Provenance, a metadata history detailing the derivation of an object, contains information that allows for expressive, policy-independent access control decisions. We consider how to manage and validate the metadata of a provenance-aware cloud system, and introduce protocols that allow for secure transfer of provenance metadata between end hosts and cloud authorities. Using these protocols, we develop a provenance-based access control mechanism for Cumulus cloud storage, capable of processing thousands of operations per second on a single deployment. Through the introduction of replicated components, we achieve overhead costs of just 14%, demonstrating that provenance-based access control is a practical and scalable solution for the cloud.

Traditionally, if someone did some act that required forgiveness, there were social norms in place for such forgiveness to happen. Over time, the act is also typically forgotten. And, should the person not be forgiven and the social pressure become too great, he had the option of moving to a new location for a fresh start. Yet with the Internet, these options are no longer available. Worse, activities which traditionally did not even require forgiveness are now impacting lives in unexpected ways, and are never forgotten. There are, however, technical approaches that could be applied to the problem, such as (1) controlling dissemination through new access control models or cryptographic approaches, (2) flooding the web with contrary information, (3) leading users to believe the information applies to someone else, (4) changing the semantics of what was written, and (5) finding a way to take advantage of the inconvenient information. In this paper we discuss the social act of forgiveness, and go into detail on the possible technical approaches to "forgetting" without deleting.

Data provenance---a record of the origin and evolution of data in a system---is a useful tool for forensic analysis. However, existing provenance collection mechanisms fail to achieve sufficient breadth or fidelity to provide a holistic view of a system's operation over time. We present Hi-Fi, a kernel-level provenance system which leverages the Linux Security Modules framework to collect high-fidelity whole-system provenance. We demonstrate that Hi-Fi is able to record a variety of malicious behavior within a compromised system. In addition, our benchmarks show the collection overhead from Hi-Fi to be less than 1% for most system calls and 3% in a representative workload, while simultaneously generating a system measurement that fully reflects system evolution. In this way, we show that we can collect broad, high-fidelity provenance data which is capable of supporting detailed forensic analysis.

Cloud services have become a cheap and popular means of computing. They allow users to synchronize data between devices and relieve low-powered devices from heavy computations. In response to the surge of smartphones and mobile devices, several cloud-based Web browsers have become commercially available. These "cloud browsers" assemble and render Web pages within the cloud, executing JavaScript code for the mobile client. This paper explores how the computational abilities of cloud browsers may be exploited through a Browser MapReduce (BMR) architecture for executing large, parallel tasks. We explore the computation and memory limits of four cloud browsers, and demonstrate the viability of BMR by implementing a client based on a reverse engineering of the Puffin cloud browser. We implement and test three canonical MapReduce applications (word count, distributed grep, and distributed sort). While we perform experiments on relatively small amounts of data (100 MB) for ethical considerations, our results strongly suggest that current cloud browsers are a viable source of arbitrary free computing at large scale.

The web is a primary means of information sharing for most organizations and people. Currently, a recipient of web content knows nothing about the environment in which that information was generated other than the specific server from whence it came (and even that information can be unreliable). In this paper, we develop and evaluate the Spork system that uses the Trusted Platform Module (TPM) to tie the web server integrity state to the web content delivered to browsers, thus allowing a client to verify that the origin of the content was functioning properly when the received content was generated and/or delivered. We discuss the design and implementation of the Spork service and its browser-side Firefox validation extension. In particular, we explore the challenges and solutions of scaling the delivery of mixed static and dynamic content to a large number of clients using exceptionally slow TPM hardware. We perform an in-depth empirical analysis of the Spork system within Apache web servers. This analysis shows Spork can deliver nearly 8,000 static or over 6,500 dynamic integrity-measured web objects per second. More broadly, we identify how TPM-based content web services can scale to large client loads with manageable overheads and deliver integrity-measured content with manageable overhead.

Portable storage devices, such as key-chain USB devices, are ubiquitous. These devices are often used with impunity, with users repeatedly using the same storage device in open computer laboratories, Internet cafes, and on office and home computers. Consequently, they are the target of malware that exploit the data present or use them as a means to propagate malicious software. This paper presents the Kells mobile storage system. Kells limits untrusted or unknown systems from accessing sensitive data by continuously validating the accessing host's integrity state. We explore the design and operation of Kells, and implement a proof-of-concept USB 2.0 storage device on experimental hardware. Our analysis of Kells is twofold. We first prove the security of device operation (within a freshness security parameter Δt) using the LS2 logic of secure systems. Second, we empirically evaluate the performance of Kells. These experiments indicate nominal overheads associated with host validation, showing a worst case throughput overhead of 1.22% for read operations and 2.78% for writes.

The penetration of cellular networks worldwide and emergence of smart phones has led to a revolution in mobile content. Users consume diverse content when, for example, exchanging photos, playing games, browsing websites, and viewing multimedia. Current phone platforms provide protections for user privacy, the cellular radio, and the integrity of the OS itself. However, few offer protections to protect the content once it enters the phone. For example, MP3-based MMS or photo content placed on Android smart phones can be extracted and shared with impunity. In this paper, we explore the requirements and enforcement of digital rights management (DRM) policy on smart phones. An analysis of the Android market shows that DRM services should ensure: a) protected content is accessible only by authorized phones b) content is only accessible by provider-endorsed applications, and c) access is regulated by contextual constraints, e.g., used for a limited time, a maximum number of viewings, etc. The Porscha system developed in this work places content proxies and reference monitors within the Android middleware to enforce DRM policies embedded in received content. A pilot study controlling content obtained over SMS, MMS, and email illustrates the expressibility and enforcement of Porscha policies. Our experiments demonstrate that Porscha is expressive enough to articulate needed DRM policies and that their enforcement has limited impact on performance.

Portable storage devices, such as key-chain USB devices, are ubiquitous and used everywhere; users repeatedly use the same storage device in open computer laboratories, Internet cafes, and on office and home computers. Consequently, they are the target of malware that exploit the data present or use them as a means to propagate malicious software., e.g., Conficker and Agent.bz. We present the Kells mobile storage system, which limits untrusted or unknown systems from accessing sensitive data by continuously validating the accessing host's integrity state. We explore the design and operation of Kells, and implement a proof-of-concept USB 2.0 storage device of experimental hardware. Our experiments indicate nominal overheads associated with host validation, with a worst-case throughput overhead of 1.22% for reads and 2.78% for writes.

Storage is increasingly becoming a vector for data compromise. Solutions for protecting on-disk data confidentiality and integrity to date have been limited in their effectiveness. Providing authenticated encryption, or simultaneous encryption with integrity information, is important to protect data at rest. In this paper, we propose that disks augmented with non-volatile storage (e.g., hybrid hard disks) and cryptographic processors (e.g., FDE drives) may provide a solution for authenticated encryption, storing security metadata within the drive itself to eliminate dependences on other parts of the system. We augment the DiskSim simulator with a flash simulator to evaluate the costs associated with managing operational overheads. These experiments show that proper tuning of system parameters can eliminate many of the costs associated with managing security metadata, with less than a 2% decrease in IOPS versus regular disks.

As the Internet'sde factointerdomain routing protocol, the Border Gateway Protocol (BGP) is the glue that holds the disparate parts of the Internet together. A major limitation of BGP is its failure to adequately address security. Recent high-profile outages and security analyses clearly indicate that the Internet routing infrastructure is highly vulnerable. Moreover, the design of BGP and the ubiquity of its deployment have frustrated past efforts at securing interdomain routing. This paper considers the current vulnerabilities of the interdomain routing system and surveys both research and standardization efforts relating to BGP security. We explore the limitations and advantages of proposed security extensions to BGP, and explain why no solution has yet struck an adequate balance between comprehensive security and deployment cost.

Abstract Densely populated areas are increasingly filled with vulnerable wireless routers set up by unsophisticated users. In isolation, such routers appear to represent only a minor threat, but in aggregate, the threat can be much greater. We introduce the notion of malnets: networks of adversary‐controlled wireless routers targeted to a physical geography. Similar to Internet worms such as Slammer and Code‐Red, malnets are created by the recursive compromise of targeted devices. However, unlike their traditionally wired counterparts, malnet worms exploit only other routers that are within their transmission range. The malnet thus creates a parallel wireless infrastructure that is (a) completely under control of the adversary, and (b) spans a targeted physical area, creating a valuable infrastructure for a variety of virtual and physical attacks. We initially study the propagation characteristics of commercial routers and model inter‐router connectivity using publicly available war‐driving data. The resulting characterization is applied to well‐known epidemiological models to explore the success rates and speeds of malnet creation across cities such as New York, Atlanta, and Los Angles. Finally, we use a sampling of available exploits to demonstrate the construction of multi‐vector, multi‐platform worms capable of targeting wireless routers. Our analysis show that an adversary can potentially deploy a malnet of over 24,000 routers in Manhattan in less than 2,h. Through this work we show that malnets are not only feasible but can be efficiently deployed. Copyright © 2009 John Wiley & Sons, Ltd.

The Web is a primary means of information sharing for most organizations and people. Currently, a recipient of Web content knows nothing about the environment in which that information was generated other than the specific server from whence it came (and even that information can be unreliable). In this paper, we develop and evaluate the Spork system that uses the trusted platform module (TPM) to tie the Web server integrity state to the Web content delivered to browsers, thus allowing a client to verify that the origin of the content was functioning properly when the received content was generated and/or delivered. We discuss the design and implementation of the Spork service and its browser-side Firefox validation extension. In particular, we explore the challenges and solutions of scaling the delivery of mixed static and dynamic content using exceptionally slow TPM hardware. We perform an in-depth empirical analysis of the Spork system within Apache Web servers. This analysis shows Spork can deliver nearly 8,000 static or over 7,000 dynamic integrity-measured Web objects per-second. More broadly, we identify how TPM-based content Web services can scale with manageable overheads and deliver integrity-measured content with manageable overhead.

Structured peer-to-peer (P2P) systems have grown enormously because of their scalability, efficiency, and reliability. These systems assign a unique identifier to each user and object. However, current assignment schemes allow an adversary to carefully select user IDs and/or simultaneously obtain many pseudo-identities-ultimately leading to an ability to disrupt the P2P system in very targeted and dangerous ways. In this paper, we propose novel ID assignment protocols based on identity-based cryptography. This approach permits the acquisition of node IDs to be tightly regulated without many of the complexities and costs associated with traditional certificate solutions. We broadly consider the security requirements of ID assignment and present three protocols representing distinct threat and trust models. A detailed empirical study of the protocols is given. Our analysis shows that the cost of our identity-based protocols is nominal, and that the associated identity services can scale to millions of users using a limited number of servers.

Main memory contains transient information for all resident applications. However, if memory chip contents survives power-off, e.g., via freezing DRAM chips, sensitive data such as passwords and keys can be extracted. Main memory persistence will soon be the norm as recent advancements in MRAM and FeRAM position non-volatile memory technologies for widespread deployment in laptop, desktop, and embedded system main memory. Unfortunately, the same properties that provide energy efficiency, tolerance against power failure, and "instant-on'' power-up also subject systems to offline memory scanning. In this paper, we propose a memory encryption control unit (MECU) that provides memory confidentiality during system suspend and across reboots. The MECU encrypts all memory transfers between the processor-local level 2 cache and main memory to ensure plaintext data is never written to the persistent medium. The MECU design is outlined and performance and security trade-offs considered. We evaluate a MECU-enhanced architecture using the SimpleScalar hardware simulation framework on several hardware benchmarks. This analysis shows the majority of memory accesses are delayed by less than 1 ns, with higher access latencies (caused by resume state reconstruction) subsiding within 0.25 seconds of a system resume. In effect, the MECU provides zero-cost steady state memory confidentiality for non-volatile main memory.

Rootkits are now prevalent in the wild. Users affected by rootkits are subject to the abuse of their data and resources, often unknowingly. Suchmalware becomes even more dangerous when it is persistent-infected disk images allow the malware to exist across reboots and prevent patches or system repairs from being successfully applied. In this paper, we introduce rootkit-resistant disks (RRD) that label all immutable system binaries and configuration files at installation time. During normal operation, the disk controller inspects all write operations received from the host operating system and denies those made for labeled blocks. To upgrade, the host is booted into a safe state and system blocks can only be modified if a security token is attached to the disk controller. By enforcing immutability at the disk controller, we prevent a compromised operating system from infecting its on-disk image.

The Internet has grown tremendously in terms of the number of users who rely on it and the number of organizations that are connected to it. Characterizing how this growth affects its structure and topology is vitally important to determine the fundamental characteristics and limitations that must be handled, such as address space exhaustion; understanding the process of allocating and delegating address space can help to answer these questions. In this paper, we analyze BGP routing data to study the structure and growth of IPv4 address space allocation, fragmentation and usage. We explore the notion of delegation relationships among prefixes and use this information to construct an autonomous system (AS) delegation tree. We show that delegation in the Internet is not significantly correlated to the underlying topology or AS customer-provider relationships. We also analyze the fragmentation and usage of address space over a period of five years and examine prefixes that are delegated by organizations vs. those that are not delegated. We notice that the address space usage due to delegating prefixes is increasing at the same rate as the address space usage due to non-delegating prefixes. This indicates that fragmentation rate of the address space is actually almost a constant with respect to total address usage. Additionally, we show that most delegation is performed by a small number of organizations, which may aid in the implementation of a public-key infrastructure for the Internet.

The Border Gateway Protocol (BGP) is the de facto interdomain routing protocol on the Internet. While the serious vulnerabilities of BGP are well known, no security solution has been widely deployed. The lack of adoption is largely caused by a failure to find a balance between deployability, cost, and security. In this paper, we consider the design and performance of BGP path authentication constructions that limit resource costs by exploiting route stability. Based on a year-long study of BGP traffic and indirectly supported by findings within the networking community, we observe that routing paths are highly stable. This observation leads to comprehensive and efficient constructions for path authentication. We empirically analyze the resource consumption of the proposed constructions via trace-based simulations. This latter study indicates that our constructions can reduce validation costs by as much as 97.3% over existing proposals while requiring nominal storage resources. We conclude by considering operational issues related to incremental deployment of our solution.