Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine-checking Multi-Round Proofs of Shuffle: Terelius-Wikstrom and Bayer-Groth

Shufﬂes are used in electronic voting in much the same way physical ballot boxes are used in paper systems: (encrypted) ballots are input into the shufﬂe and (encrypted) ballots are output in a random order, thereby breaking the link between voter identities and ballots. To guarantee that no ballots are added, omitted or altered, zero-knowledge proofs, called proofs of shufﬂe, are used to provide publicly veriﬁable transcripts that prove that the outputs are a re-encrypted per-mutation of the inputs. The most prominent proofs of shufﬂe, in practice, are those due to Terelius and Wikström (TW), and Bayer and Groth (BG). TW is simpler whereas BG is more efﬁcient, both in terms of bandwidth and computation. Security for the simpler (TW) proof of shufﬂe has already been machine-checked but several prominent vendors insist on using the more complicated BG proof of shufﬂe. Here, we machine-check the security of the Bayer-Groth proof of shufﬂe via the Coq proof-assistant. We then extract the veri-ﬁer (software) required to check the transcripts produced by Bayer-Groth implementations and use it to check transcripts from the Swiss Post evoting system under development for national elections in Switzerland.
Subverting Website Fingerprinting Defenses with Robust Traffic Representation

Anonymity networks, e.g., Tor, are vulnerable to various website fingerprinting (WF) attacks, which allows attackers to perceive user privacy on these networks. However, the defenses developed recently can effectively interfere with WF attacks, e.g., by simply injecting dummy packets. In this paper, we propose a novel WF attack called Robust Fingerprinting (RF), which enables an attacker to fingerprint the Tor traffic under various defenses. Specifically, we develop a robust traffic representation method that generates Traffic Aggregation Matrix (TAM) to fully capture key informative features leaked from Tor traces. By utilizing TAM, an attacker can train a CNN-based classifier that learns common high-level traffic features uncovered by different defenses. We conduct extensive experiments with public real-world datasets to compare RF with state-of-the-art (SOTA) WF attacks. The closed-and open-world evaluation results demonstrate that RF significantly outperforms the SOTA attacks. In particular, RF can effectively fingerprint Tor traffic under the SOTA defenses with an average accuracy improvement of 8.9% over the best existing attack (i.e., Tik-Tok).
Side-Channel Attacks on Optane Persistent Memory

There is a constant evolution of technology for cloud environments, including the development of new memory storage technology, such as persistent memory. The newly-released Intel Optane persistent memory provides high-performance, persistent, and byte-addressable access for storage-class applications in data centers. While Optane’s direct data management is fast and efficient, it is unclear whether it comes with undesirable security implications. This is problematic, as cloud tenants are physically co-located on the same hardware. In this paper, we present the first side-channel security analysis of Intel Optane persistent memory. We reverse-engineer the internal cache hierarchy, cache sizes, associativity, replacement policies, and wear-leveling mechanism of the Optane memory. Based on this reverse-engineering, we construct four new attack primitives on Optane’s internal components. We then present four case studies using these attack primitives. First, we present local covert channels based on Optane’s internal caching. Second, we demonstrate a keystroke side-channel attack on a remote user via Intel’s Optane-optimized key-value store, pmemkv . Third, we study a fully remote covert channel through pmemkv . Fourth, we present our Note Board attack, also through pmemkv , enabling two parties to store and exchange messages covertly across long time gaps and even power cycles of the server. Finally, we discuss mitigations against our attacks.
Exploring Privacy and Incentives Considerations in Adoption of COVID-19 Contact Tracing Apps

Mobile Health (mHealth) apps, such as COVID-19 contact tracing and other health-promoting technologies, help support personal and public health e ff orts in response to the pandemic and other health concerns. However, due to the sensitive data handled by mHealth apps, and their potential e ff ect on people’s lives, their widespread adoption demands trust in a multitude of aspects of their design. In this work, we report on a series of conjoint analyses (N = 1,521) to investigate how COVID-19 contact tracing apps can be better designed and marketed to improve adoption. Specifically, with a novel design of randomization on top of a conjoint analysis, we investigate people’s privacy considerations relative to other attributes when they are contemplating contact-tracing app adoption. We further explore how their adoption considerations are influenced by deployment factors such as o ff ering extrinsic incentives (money, healthcare) and user factors such as receptiveness to contact-tracing apps and sociodemographics. Our results, which we contextualize and synthesize with prior work, o ff er insight into the most desired digital contact-tracing products (e.g., app features) and how they should be deployed (e.g., with incentives) and targeted to di ff erent user groups who have heterogeneous preferences.
A Bug's Life: Analyzing the Lifecycle and Mitigation Process of Content Security Policy Bugs

The constantly evolving Web exerts a chronic pressure on the development and maintenance of the Content Security Policy (CSP), which stands as one of the primary security policies to mitigate attacks such as cross-site scripting. Indeed, to attain comprehensiveness, the policy must account for virtually every newly introduced browser feature, and every existing browser feature must be scrutinized upon extension of CSP functionality. Unfortunately, this undertaking’s complexity has already led to critical implementational shortcomings, resulting in the security subversion of all CSP-employing websites. In this paper, we present the first systematic analysis of CSP bug lifecycles, shedding new light on bug root causes. As such, we leverage our automated framework, B UG H OG , to evaluate the reproducibility of publicly disclosed bug proofs of concept in over 100 , 000 browser revisions. By considering the entire source code revision history since the introduction of CSP for Chromium and Firefox, we identified 123 unique introducing and fixing revisions for 75 CSP bugs. Our analysis shows that inconsistent handling of bugs led to the early public disclosure of three, and that the lifetime of several others could have been considerably decreased through adequate bug sharing between vendors. Finally, we propose solutions to improve current bug handling and response practices.
How to Cover up Anomalous Accesses to Electronic Health Records

Illegitimate access detection systems in hospital logs perform post hoc detection instead of runtime access restriction to allow widespread access in emergencies. We study the effectiveness of adversarial machine learning strategies against such detection systems on a large-scale dataset consisting of a year of access logs at a major hospital. We study a range of graph-based anomaly detection systems, including heuristic-based and Graph Neural Network (GNN)-based models. We find that evasion attacks , in which covering accesses (that is, accesses made to disguise a target access ) are injected during evaluation period of the target access, can successfully fool the detection system. We also show that such evasion attacks can transfer among different detection algorithms. On the other hand, we find that poisoning attacks , in which adversaries inject covering accesses during the training phase of the model, do not effectively mislead the trained detection system unless the attacker is given unrealistic capabilities such as injecting over 10,000 accesses or imposing a high weight on the covering accesses in the training algorithm. To examine the generalizability of the results, we also apply our attack against a state-of-the-art detection model on the LANL network lateral movement dataset, and observe similar conclusions.
Understand Users' Privacy Perception and Decision of V2X Communication in Connected Autonomous Vehicles

Connected autonomous vehicles (CAVs) offer opportunities to improve road safety and enhance traffic efficiency. Vehicle-to-everything (V2X) communication allows CAVs to communicate with any entity that may affect, or may be affected by, the vehicles. The implementation of V2X in CAVs is in-separable from sharing and receiving a wide variety of data. Nevertheless, the public is not necessarily aware of such ubiquitous data exchange or does not understand their implications. We conducted an online study ( N = 595) examining drivers’ privacy perceptions and decisions of four V2X application scenarios. Participants perceived more benefits but fewer risks of data sharing in the V2X scenarios where data collection is critical for driving than otherwise. They also showed more willingness to share data in those scenarios. In addition, we found that participants’ awareness of privacy risks (priming) and their experience on driving assistance and connectivity functions impacted their data-sharing decisions. Qualitative data confirmed that benefits, especially safety, come first, indicating a privacy-safety tradeoff. Moreover, factors such as misconceptions and novel expectations about CAV data collection and use moderated participants’ privacy decisions. We discuss implications of the obtained results to inform CAV privacy design and development.
"It's the Equivalent of Feeling Like You're in Jail": Lessons from Firsthand and Secondhand Accounts of IoT-Enabled Intimate Partner Abuse

Victim-survivors of intimate partner violence (IPV) are facing a new technological threat: Abusers are leveraging IoT devices such as smart thermostats, hidden cameras, and GPS trackers to spy on and harass victim-survivors. Though prior work provides a foundation of what IoT devices can be involved in intimate partner violence, we lack a detailed understanding of the factors which contribute to this IoT abuse , the strategies victim-survivors use to mitigate IoT abuse, and the barriers they face along the way. Without this information, it is challenging to design effective solutions to stop IoT abuse. To fill this gap, we interviewed 20 participants with first-hand or secondhand experience with IoT abuse. Our inter-views captured 39 varied instances of IoT abuse, from surveillance with hidden GPS trackers to harassment with smart thermostats and light bulbs. They also surfaced 21 key barriers victim-survivors face while coping with IoT abuse. For instance, victim-survivors struggle to find proof of the IoT abuse they experience, which makes mitigations challenging. Even with proof, victim-survivors face barriers mitigating the abuse; for example, mitigation is all but impossible for victim-survivors living with an abusive partner. Our findings pinpoint several solutions to combat IoT abuse, including increased transparency of IoT devices, updated IoT access control protocols, and raising awareness of IoT abuse.
SAFER: Efficient and Error-Tolerant Binary Instrumentation

Recent advances in binary instrumentation have been focused on performance. By statically transforming the code to avoid additional runtime operations, systems such as Egalito and RetroWrite achieve near zero overheads. The safety of these static transformations relies on several assumptions: (a) error-free and complete disassembly, (b) exclusive use of position-independentcode,and(c)codepointeridentification thatisfree of both false positives and false negatives. Violations of these assumptions can cause an instrumented program to crash, or worse, experience delayed failures that corrupt data or compromise security. Many earlier binary instrumentation techniques (e.g., DynamoRio, Pin, and BinCFI) minimized such assumptions, but the price to be paid is a much higher overhead, especially for indirect-call-intensive (e.g., C++) applications. Thus, an open research question is whether the safety benefits of the earlier works can be combined with the performance benefits of recent works. We answer this question in the affirmative by presenting a new instrumentation technique that (a) tolerates the use of position-dependent code and common disassembly and static analysis errors, and (b) detects assumption violations at runtime before they can lead to undefined behavior. Our approach provides a fail-crash primitive for graceful shutdown or recovery. We achieve safe instrumentation without sacrificing performance, introducing a low overhead of about ∼ 2%.
"To Do This Properly, You Need More Resources": The Hidden Costs of Introducing Simulated Phishing Campaigns

Many organizations use phishing simulation campaigns to raise and measure their employees’ security awareness. They can create their own campaigns, or buy phishing-as-a-service from commercial providers; however, the evaluations of the effectiveness in reducing the vulnerability to such attacks have produced mixed results. Recently, researchers have pointed out “hidden costs” – such as reduced productivity and employee trust. What has not been investigated is the cost involved in preparing an organization for a simulated phishing campaign. We present the first case study of an organization going through the process of selecting and purchasing a phishing simulation. We document and analyze the effort of different stakeholders involved, and present reflection from semi-structured interviews with 6 key actors at the end of the pro-curement process. Our data analysis shows that procuring such simulations can require significant effort from different stakeholders – in our case, at least 50,000C in person hours – and many hidden intangible costs. Evaluating if a product or service meets training requirements, is acceptable to employees, and preparing the technical infrastructure and operational processes for running such a product all require significant time and effort. The prevailing perception that phishing simulation campaigns are a quick and low-cost solu-tion to providing security training to employees thus needs to be challenged.
HYDROSCAN: Airborne laser mapping of hydrological features and resources

A summary of the technology and integration of components for a generic airborne laser scanning (ALS) system is provided. At times the Optech ALTM 3100 is used as an example, as this is the sensor owned and operated by the Applied Geomatics Research Group (AGRG). ALS mapping systems are an integration of four main measuring systems: 1) a differential global position system (GPS) solution is used to fix the position of the aircraft platform; 2) an inertial measurement unit (IMU) is used to record the angular orientation of the sensor; 3) a LiDAR (light detection and ranging) system is used to emit and receive pulses of laser light; and 4) a scanning system redirects laser pulses orthogonally beneath the aircraft platform to collect of swath of data at ground level. ALS LiDAR sensors are most commonly configured to map earth surface terrain at a high spatial resolution; however, bathymetric depth mapping using dual laser wavelengths and water quality mapping using LiDAR fluorescence techniques are becoming more common place.
Password Guessing Using Random Forest

Passwords are the most widely used authentication method, and guessing attacks are the most effective method for pass-word strength evaluation. However, existing password guessing models are generally built on traditional statistics or deep learning, and there has been no research on password guessing that employs classical machine learning. To ﬁll this gap, this paper provides a brand new technical route for password guessing. More speciﬁcally, we re-encode the password characters and make it possible for a series of classical machine learning techniques that tackle multi-class classiﬁcation problems (such as random forest, boosting algorithms and their variants) to be used for password guessing. Further, we propose RFGuess , a random-forest based framework that characterizes the three most representative password guessing scenarios (i.e., trawling guessing, targeted guessing based on personally identiﬁable information (PII) and on users’ password reuse behaviors). Besides its theoretical signiﬁcance, this work is also of practical value. Experiments using 13 large real-world password datasets demonstrate that our random-forest based guessing models are effective: (1) RFGuess for trawling guessing scenarios, whose guessing success rates are comparable to its foremost counterparts; (2) RFGuess -PII for targeted guessing based on PII, which guesses 20% ∼ 28% of common users within 100 guesses, outperforming its foremost counterpart by 7% ∼ 13%; (3) RFGuess -Reuse for targeted guessing based on users’ password reuse/modiﬁcation behaviors, which performs the best or 2nd best among related models. We believe this work makes a substantial step toward introducing classical machine learning techniques into password guessing.
Extended Hell(o): A Comprehensive Large-Scale Study on Email Confidentiality and Integrity Mechanisms in the Wild

The core specifications of electronic mail as used today date back as early as the 1970s. At that time, security did not play a significant role in developing communication protocols. These shortcomings still manifest themselves today in the prevalence of phishing and the reliance on opportunistic encryption. Besides STARTTLS, various mechanisms such as SPF, DKIM, DMARC, DANE, and MTA-STS have been proposed. However, related work has shown that not all providers support them and that misconfigurations are common. In this work, we provide a comprehensive overview of the current state of email confidentiality and integrity measures, as well as the effectiveness of their deployment. On a positive note, support for incoming TLS connections has significantly increased over the years, with over 96% of reachable MXs in the top 10 million domains allowing for explicit TLS. Notably, 30% of presented certificates are invalid, though, with the majority of issues related to the presented hostnames. In light of this, all 47 providers we tested connect to hosts with expired, self-signed, non-matching certificates, making it trivial for at-tackers to intercept their connections. Our analysis also shows that still only around 40% of sites specify SPF, and even high-ranked providers like t-online.de do not enforce it. Similarly, while DNS lookups are performed for both DKIM and DANE, neither mechanism is validated or enforced by all providers. In addition, we show that MTA-STS is only slowly getting traction (six providers support it) and provide the first large-scale analysis into OPENPGPKEY and SMIMEA records. All in all, this still paints a grim yet slightly improving picture for the state of email security by late 2022.
We Really Need to Talk About Session Tickets: A Large-Scale Analysis of Cryptographic Dangers with TLS Session Tickets

Session tickets improve the performance of the TLS protocol. They allow abbreviating the handshake by using secrets from a previous session. To this end, the server encrypts the secrets using a Session Ticket Encryption Key (STEK) only know to the server, which the client stores as a ticket and sends back upon resumption. The standard leaves details such as data formats, encryption algorithms, and key management to the server implementation. TLS session tickets have been criticized by security experts, for undermining the security guarantees of TLS. An adversary, who can guess or compromise the STEK, can passively record and decrypt TLS sessions and may impersonate the server. Thus, weak implementations of this mechanism may completely undermine TLS security guarantees. We performed the first systematic large-scale analysis of the cryptographic pitfalls of session ticket implementations. (1) We determined the data formats and cryptographic algorithms used by 12 open-source implementations and designed online and offline tests to identify vulnerable implementations. (2) We performed several large-scale scans and collected session tickets for extended offline analyses. We found significant differences in session ticket implementations and critical security issues in the analyzed servers. Vulnerable servers used weak keys or repeating keystreams in the used tickets, allowing for session ticket decryption. Among others, our analysis revealed a widespread implementation flaw within the Amazon AWS ecosystem that allowed for passive traffic decryption for at least 1.9% of the Tranco Top 100k servers.
Sparsity Brings Vulnerabilities: Exploring New Metrics in Backdoor Attacks

Nowadays, using AI-based detectors to keep pace with the fast iterating of malware has attracted a great attention. However, most AI-based malware detectors use features with vast sparse subspaces to characterize applications, which brings significant vulnerabilities to the model. To exploit this sparsity-related vulnerability, we propose a clean-label backdoor attack consisting of a dissimilarity metric-based candidate selection and a variation ratio-based trigger construction. The proposed backdoor is veriﬁed on different datasets, including a Windows PE dataset, an Android dataset with numerical and boolean feature values, and a PDF dataset. The experimental results show that the attack can slash the accuracy on watermarked malware to nearly 0% even with the least number (0.01% of the class set) of watermarked goodwares compared to previous attacks. Problem space constraints are also considered with experiments in data-agnostic scenario and data-and-model-agnostic scenario , proving transferability between different datasets as well as deep neural networks and traditional classiﬁers. The attack is veriﬁed consistently powerful under the above scenarios. Moreover, eight existing defenses were tested with their effect left much to be desired. We demonstrated the reason and proposed a subspace compression strategy to boost models’ robustness, which also makes part of the previously failed defenses effective.
AURC: Detecting Errors in Program Code and Documentation

Error detection in program code and documentation is a critical problem in computer security. Previous studies have shown promising vulnerability discovery performance by extensive code or document-guided analysis. However, the state-of-the-arts have the following significant limitations: (i) They assume the documents are correct and treat the code that violates documents as bugs, thus cannot find documents’ defects and code’s bugs if APIs have defective documents or no documents. (ii) They utilize majority voting to judge the inconsistent code snippets and treat the deviants as bugs, thus cannot cope with situations where correct usage is minor or all use cases are wrong. In this paper, we present AURC, a static framework for detecting code bugs of incorrect return checks and document defects. We observe that three objects participate in the API invocation, the document, the caller (code that invokes API), and the callee (the source code of API). Mutual corroboration of these three objects eliminates the reliance on the above assumptions. AURC contains a context-sensitive backward analysis to process callees, a pre-trained model-based document classifier, and a container that collects conditions of if statements from callers. After cross-checking the results from callees, callers, and documents, AURC delivers them to the correctness inference module to infer the defective one. We evaluated AURC on ten popular codebases. AURC discovered 529 new bugs that can lead to security issues like heap buffer overflow and sensitive information leakage, and 224 new document defects. Maintainers acknowledge our findings and have accepted 222 code patches and 76 document patches.