Reviewerly/exHarmony huggingface dataset:
GLICE: Combining Graph Neural Networks and Program Slicing to Improve Software Vulnerability Detection

This paper introduces the GLICE (Graph Neural Network with program slice) model for static code analysis to detect vulnerabilities in source code. GLICE combines inter-procedural program slicing with a Graph Neural Network. It builds upon and extends prior work that applies program slicing (as in the SySeVR model) and Graph Neural Networks (as in the FUNDED model) for vulnerability detection. We apply GLICE on a data set of C/C++ code samples with out-of-bounds write (CWE-787) and out-of-bounds read (CWE-125) butter overflow vulnerabilities. We perform experiments with GLICE to evaluate trade-offs in the depth of the inter-procedural analysis, and to compare GLICE with prior models by evaluating the effectiveness for vulnerability detection and the usage of resources. Our experimental results show that detection accuracy of GLICE improves up to 13% when compared to FUNDED, while the time required to train the GLICE model is about 9 times smaller. GLICE allows configuring the depth of the interprocedural analysis. Our experimental results show that increasing the depth will improve detection, which however requires more computing resources. This allows a user of GLICE to steer the trade-off between detection accuracy and computational efficiency.
Tales from the Git: Automating the detection of secrets on code and assessing developers’ passwords choices

Typical users are known to use and reuse weak passwords. Yet, as cybersecurity concerns continue to rise, understanding the password practices of software developers becomes increasingly important. In this work, we examine developers’ passwords on public repositories. Our dedicated crawler collected millions of passwords from public GitHub repositories; however, our focus is on their unique characteristics. To this end, this is the first study investigating the developer traits in password selection across different programming languages and contexts, e.g. email and database. Despite the fact that developers may have carelessly leaked their code on public repositories, our findings indicate that they tend to use significantly more secure passwords, regardless of the underlying programming language and context. Nevertheless, when the context allows, they often resort to similar password selection criteria as typical users. The public availability of such information in a cleartext format indicates that there is still much room for improvement and that further targeted awareness campaigns are necessary.
Multi-Factor Credential Hashing for Asymmetric Brute-Force Attack Resistance

Since the introduction of bcrypt in 1999, adaptive password hashing functions, whereby brute-force resistance increases symmetrically with computational difficulty for legitimate users, have been our most powerful post-breach countermeasure against credential disclosure. Unfortunately, the relatively low tolerance of users to added latency places an upper bound on the deployment of this technique in most applications. In this paper, we present a multi-factor credential hashing function (MFCHF) that incorporates the additional entropy of multi-factor authentication into password hashes to provide asymmetric resistance to brute-force attacks. MFCHF provides full backward compatibility with existing authentication software (e.g., Google Authenticator) and hardware (e.g., YubiKeys), with support for common usability features like factor recovery. The result is a 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">48</sup> times increase in the difficulty of cracking hashed credentials, with little added latency or usability impact.
“As Usual, I Needed Assistance of a Seeing Person”: Experiences and Challenges of People with Disabilities and Authentication Methods

According to the World Health organization, about 16% of the world’s population live with a disability. While they could benefit from digital products and services, users with disabilities often face severe accessibility issues: tasks can only be completed with difficulty, a considerable investment of time, or with assistance of technologies or other people. Further, to access these products and services, they need to authenticate. The accessibility of authentication methods for users with disabilities has not been studied in depth. We use an accessible study design to conduct 13 semi-structured interviews with people with physical, hearing, visual, cognitive, or multiple impairments to better understand the accessibility issues they face when using knowledge- or token-based, and biometric authentication. Our qualitative content analysis shows that none of the commonly available authentication methods is fully accessible to participants, causing them to abandon services or develop workarounds that reduce their own security and privacy. Our results also reveal the role of assistive technologies and human assistants in the authentication experience of users with disabilities. We conclude by encouraging fellow researchers and practitioners to reflect on assisted access when designing security mechanisms, to include people with disabilities using accessible study designs, and to keep in mind that accessible security is about more than usability – to further benefit users without disabilities as well.
How well does GPT phish people? An investigation involving cognitive biases and feedback

Phishing scams have increased drastically over the years. Prior research has investigated various ways to prevent phishing email scams. However, little is known about human decisions against phishing emails that contain cognitive biases and are either crafted by humans or large-language models (LLMs). Also, less is known about how humans can be trained against such emails. This research aimed to address this literature gap by investigating the effectiveness of human-crafted phishing emails versus GPT3 crafted phishing emails (GPT-3 being an LLM). The study consisted of two between-subjects conditions (N = 30 per condition): human and GPT. Each condition contained three rounds with a total of 40 trials, and participants were required to mark the degree to which the presented email was genuine or phishing in each trial. The second round provided feedback to participants in both conditions. The results showed that human-crafted emails were more effective in phishing people compared to GPT-3 crafted emails even after training across different cognitive biases. However, humans felt more confident against human-crafted emails compared to GPT-3 crafted emails. We highlight the implications of these results for LLM crafted phishing attacks compared to human-crafted phishing attacks.
Masking Location Streams in the Presence of Colluding Service Providers

Many service providers rely on location data or streams to offer personalized services. However, reckless release of location streams can have serious privacy implications, especially with respect to sensitive zones, paths and time intervals. Unfortunately, current approaches by mobile platform providers to enhance privacy expose major shortcomings, especially with respect to releasing location information of frequently visited areas and in the presence of colluding service providers that are willing to combine local knowledge to retrieve more accurate information.This paper presents a practical approach to obfuscate location streams in the presence of the aforementioned challenges. Our solution combines multiple technologies and is validated through the development of a novel privacy-friendly location service in Android. It aims to realize a reasonable trade-off between privacy and utility concerns respectively raised by end-users and service providers. Thereby, sensitive locations, zones, paths, timestamps, and time intervals are highly configurable.
Watermarking Graph Neural Networks based on Backdoor Attacks

Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications.Building a powerful GNN model is not a trivial task, as it requires a large amount of training data, powerful computing resources, and human expertise.Moreover, with the development of adversarial attacks, e.g., model stealing attacks, GNNs raise challenges to model authentication.To avoid copyright infringement on GNNs, verifying the ownership of the GNN models is necessary.This paper presents a watermarking framework for GNNs for both graph and node classification tasks.We 1) design two strategies to generate watermarked data for the graph classification task and one for the node classification task, 2) embed the watermark into the host model through training to obtain the watermarked GNN model, and 3) verify the ownership of the suspicious model in a blackbox setting.The experiments show that our framework can verify the ownership of GNN models with a very high probability (up to 99%) for both tasks.We also explore our watermarking mechanism against an adaptive attacker with access to partial knowledge of the watermarked data.Finally, we experimentally show that our watermarking approach is robust against a state-of-the-art model extraction technique and four state-of-the-art defenses against backdoor attacks.
Fuzzing SGX Enclaves via Host Program Mutations

Intel Software Guard eXtension (SGX) is the cornerstone of Confidential Computing, enabling runtime code and data integrity and confidentiality via enclaves. Unfortunately, memory-unsafe and type-unsafe programming languages, such as C/C++, are commonly used to develop enclave implementations. As a result, a memory corruption or a data race within enclaves could lead to different attacks against the enclaves, such as Return-Of-Programming (ROP) and data leakage, breaking the hardware security guarantee provided by SGX. To automatically identify these issues in existing enclave implementations, in this paper, we propose FuzzSGX, an input and program mutation-based fuzzer for Intel SGX enclave implementations. FuzzSGX provides an enclave fuzzing runtime, FuzzSGX Runtime, a drop-in library for Intel SGX SDK, enabling code coverage and sanitization within enclaves. To explore the host app-enclave boundary, FuzzSGX conducts static analysis and symbolic execution on existing host apps and enclave implementations to generate promising fuzzing programs, fuzzing both ECALLs and OCALLs. We evaluate FuzzSGX using 30 popular SGX applications and enclave implementations and find 93 bugs among these SGX projects, including data races, null pointer dereferences, out-of-bound accesses, division-by-zero, etc. FuzzSGX achieves 3.2x higher code coverage and finds 48.2% more bugs by directly targeting the host appenclave boundary by using program mutations, compared to state-of-the-art fuzzers.
Assessing and Exploiting Domain Name Misinformation

Cloud providers’ support for network evasion techniques that misrepresent the server’s domain name is more prevalent than previously believed, which has serious implications for security and privacy due to the reliance on domain names in common security architectures. Domain fronting is one such evasive technique used by privacy enhancing technologies and malware to hide the domains they visit, and it uses shared hosting and HTTPS to present a benign domain to observers while signaling the target domain in the encrypted HTTP request. In this paper, we construct an ontology of domain name misinformation and detail a novel measurement methodology to identify support among cloud infrastructure providers. Despite several of the largest cloud providers having publicly stated that they no longer support domain fronting, our findings demonstrate a more complex environment with many exceptions.We also present a novel and straightforward attack that allows an adversary to man-in-the-middle all the victim’s encrypted traffic bound to a content delivery network that supports domain fronting, breaking the authenticity, confidentiality, and integrity guarantees expected by the victim when using HTTPS. By using dynamic linker hijacking to rewrite the HTTP Host field, our attack does not generate any artifacts that are visible to the victim or passive network monitoring solutions, and the attacker does not need a separate channel to exfiltrate data or perform command-and-control, which can be achieved by rewriting HTTP headers.
“Get a higher return on your savings!”: Comparing adverts for cryptocurrency investment scams across platforms

This work compares machine learning methods using supervised, semi-supervised and unsupervised learning, to classify advertisements for cryptocurrency related investment scams found in the online forum Bitcointalk, and the social media platform Reddit. We extract more than 24.2 million posts from Bitcointalk and use Reddit’s API to collect 2,108 submissions. We train and compare several multiclass text classification approaches and use the models with highest accuracy and F-measure to identify cryptocurrency investment scam advertisements found on both platforms. We discover around five percent of all posts collected on both sites are potential scams. We then use another text classifier to identify the scam actors involved in these investment scam advertisements. We also discover the lures used within these fraudulent adverts and find the main differences in luring techniques used between Bitcointalk and Reddit. We identify that the most prevalent lure type uses the financial principle, followed by the distraction principle in Bitcointalk, and by the authority principle in Reddit. Finally, we use subreddits as communities’ proxies and compare scam advertisements within them to identify whether pensioners are being specifically targeted by cryptocurrency scam adverts. Our results show that retirement subreddits do not contain a higher number of cryptocurrency investment scam adverts compared to other investment focused subreddits.
“Act natural!”: Exchanging Private Messages on Public Blockchains

Messengers have become an essential means of interpersonal interaction. Yet untraceable private communication remains an elusive goal, as most messengers hide content, but not communication patterns. The knowledge of communication patterns can by itself reveal too much, as happened, e. g., in the context of the Arab Spring. Subliminal channels in cryptographic systems enable untraceable private communication in plain sight. In this context, bulletin boards in the form of blockchains are a natural object for subliminal communication: accessing them is innocuous, as they rely on distributed access for verification and extension. At the same time, blockchain users generate hundreds of thousands of transactions per day that are individually signed and placed on the blockchain. Thus blockchains may serve as innocuous repository for publicly accessible cryptographic transactions where subliminal channels can be placed. In this paper, we propose a public-key subliminal channel using secret-recoverable splittable signature schemes on blockchains and prove that our construction is undetectable in the random oracle model under common cryptographic assumptions. Our approach is applicable to any secret-recoverable splittable signature scheme and introduces a constant overhead of a single signature per message. Such schemes are used by 98 of the top 100 cryptocurrencies. We also analyze the applicability of our approach to the Bitcoin, Monero, and RippleNet networks and present proof of concept implementations for Bitcoin and RippleNet.
Applying Neutralisation Theory to Better Understand Ransomware Offenders

The work presented in this paper investigates the crime of ransomware from the perspective of neutralisation theory. In particular, this research-in-progress paper aims to explore the feasibility of using neutralisation theory to better understand one of the key stakeholders in ransomware operations: the offenders. Individuals (including offenders) may employ techniques of neutralisation in order to justify their rule-breaking acts, and to diminish both the perceived consequences of their acts and the feeling of guilt. The focus of this work is on highly organised ransomware groups that not only conduct cyber attacks but also operate Ransomware-as-a-Service (RaaS) businesses. Secondary data was used in this research, including media interviews with alleged ransomware offenders. Data analysis is currently ongoing, but preliminary results show that ransomware offenders mainly use six neutralisation techniques to minimise the perceived impact and/or guilty feeling of their actions. These six neutralisation techniques are (1) denial of victim, (2) denial of injury, (3) claim of benefits, (4) claim of entitlement, (5) defence of necessity, and (6) claim of relative acceptability. The findings from this work can shed some light on the ransomware offending pathways, which in turn can be utilised to devise more effective countermeasures for combatting ransomware crime.
Lessons Learned: Building a Privacy-Preserving Entity Resolution Adaptation of PPJoin using End-to-End Homomorphic Encryption

Entity resolution is the task of disambiguating records that refer to the same entity in the real world. In this work, we explore adapting one of the most efficient and accurate Jaccard-based entity resolution algorithms - PPJoin, to the private domain via end-to-end homomorphic encryption. Towards this, we present our precise adaptation: HE-PPJoin that details certain subtle data structure modifications and algorithmic additions needed for correctness and privacy. We implement HE-PPJoin by extending the PALISADE (now merged with OpenFHE) open-source, homomorphic encryption library and perform experiments to analyze its accuracy and incurred overhead. Furthermore, we directly compare HE-PPJoin against P4Join, an existing privacy-preserving variant of PPJoin, which uses hashing for raw content obfuscation (encryption), by demonstrating a rigorous analysis of the efficiency, accuracy, and privacy properties achieved by our adaptation as well as a characterization of those same attributes in P4Join. In building and designing HE-PPJoin, we faced numerous challenges that required making tradeoffs and analyzing possible alternatives. We have thus summarized and detailed all the lessons we have learned, presented throughout the paper, intended as motivating building blocks for future work in this direction.
Learning to Defend by Attacking (and Vice-Versa): Transfer of Learning in Cybersecurity Games

Designing cyber defense systems to account for cognitive biases in human decision making has demonstrated significant success in improving performance against human attackers. However, much of the attention in this area has focused on relatively simple accounts of biases in human attackers, and little is known about adversarial behavior or how defenses could be improved by disrupting attacker’s behavior. In this work, we present a novel model of human decision-making inspired by the cognitive faculties of Instance-Based Learning Theory, Theory of Mind, and Transfer of Learning. This model functions by learning from both roles in a security scenario: defender and attacker, and by making predictions of the opponent’s beliefs, intentions, and actions. The proposed model can better defend against attacks from a wide range of opponents compared to alternatives that attempt to perform optimally without accounting for human biases. Additionally, the proposed model performs better against a range of human-like behavior by explicitly modeling human transfer of learning, which has not yet been applied to cyber defense scenarios. Results from simulation experiments demonstrate the potential usefulness of cognitively inspired models of agents trained in attack and defense roles and how these insights could potentially be used in real-world cybersecurity.
TimeInspector: A Static Analysis Approach for Detecting Timing Attacks

We present a static analysis approach to detect malicious binaries that are capable of carrying out a timing attack. The proposed approach is based on a simple observation that the timing attacks typically operate by measuring the execution times of short sequences of instructions. Consequently, given a binary, we first construct the control flow graph of the binary and then determine the paths between the pairs of time readings, on which a suspiciously low number of instructions might be executed. In the presence of such a path, we mark the binary as potentially malicious and report all the suspicious paths identified. In the experiments, where a collection of benign and malicious binaries were used, the proposed approach correctly detected all the malicious binaries with an accuracy up to 99.5% and without any false negatives.
Detecting and Analyzing Mouse Tracking in the Wild

Nowadays, most websites collect personal information about their users in order to identify them and personalize their services.Among the tools used to that end, fingerprinting is one of the most advanced and precise methods, given the huge amount of features they can collect and combine to build a robust identifier of the user.Although many fingerprinting techniques have recently been studied in the literature, the use and prevalence of mouse tracking, a method that collects information about the computer pointer, is still unexplored in detail.In this work, we propose a new methodology to detect this tracking method and measure its actual usage on the top 80,000 most popular websites.Our results show that about 1.2% of the analyzed websites use some sort of mouse tracking, including some popular websites within the top-1k ranking.
Chex-Mix: Combining Homomorphic Encryption with Trusted Execution Environments for Oblivious Inference in the Cloud

Data, when coupled with state-of-the-art machine learning models, can enable remarkable applications. But, there exists an underlying tension: users wish to keep their data private, and model providers wish to protect their intellectual property. Homomorphic encryption (HE) and multi-party computation (MPC) techniques have been proposed as solutions to this problem; however, both techniques require model providers to fully trust the server performing the machine learning computation. This limits the scale of inference applications, since it prevents model providers from leveraging shared public cloud infrastructures.In this work, we present Chex-Mix, a solution to the problem of privacy-preserving machine learning between two mutually distrustful parties in an untrusted cloud setting. Chex-Mix relies on a combination of HE and trusted execution environments (TEEs), using HE to provide clients with confidentiality guarantees, and TEEs to provide model providers with confidentiality guarantees and protect the integrity of computation from malicious cloud adversaries. Unlike prior solutions to this problem, such as multi-key HE, single-key HE, MPC, or TEE-only techniques, our solution assumes that both the client and the cloud can be malicious, makes no collusion assumptions, and frees model providers from needing to maintain private online infrastructures. Our results show that Chex-Mix can execute at high efficiency, with low communication cost, while providing security guarantees unaddressed by prior work. Compared to a recent multi-key HE work that allows partial cloud offload, for example, Chex-Mix achieves a 3× lower communication cost and a 3× faster computation time.