Reviewerly/exHarmony huggingface dataset:
Effective Machine Learning-based Access Control Administration through Unlearning

With the rapid and increasing complexity of computer systems and software, there is a need for more effective, scalable, and secure access control methods. Machine learning (ML) has gained popularity in complementing manually crafted authorisation policies in such environments. However, given the dynamic and constantly evolving nature of software and access control systems, the administration of the latter presents a significant security challenge. This paper examines the administration problem of Machine Learning-based Access Control (MLBAC) systems through Machine Unlearning as a lightweight and secure method. More specifically, we explore this problem through exact and approximate unlearning and evaluate its impact using real-world data. We demonstrate the effectiveness of Machine Unlearning in both reverting policies and addressing potential vulnerabilities that may emerge during the model’s lifecycle. Compared to alternative options such as retraining from scratch, our approach reduces deployment and verification costs, making it a promising solution for MLBAC administration.
Fake it till you Detect it: Continual Anomaly Detection in Multivariate Time-Series using Generative AI

Anomaly detection in Multivariate Time-Series (MTS) data plays an important role in multiple domains, especially in cybersecurity, for the detection of unknown attacks. DC- VAE is a recent approach we have proposed for anomaly detection in network measurement multivariate data, which uses Variational Auto Encoders (VAEs) and Dilated Convolutional Neural Networks (DCNNs) to model complex and high-dimensional MTS data. However, detecting anomalies using VAEs can result in performance degradation and even catastrophic forgetting when trained on dynamic and evolving network measurements, particularly in the event of concept drifts. We extend DC- VAE to a continual learning setup, leveraging the generative AI properties of the underlying models to deal with continually evolving data. We introduce GenDeX, an approach to Generative AI-based anomaly detection which compresses the patterns extracted from past measurements into a generative model that can synthesize MTS data out of input Gaussian noise, mimicking the characteristics of the MTS data used for training. GenDeX relies on a Deep Generative Replay paradigm to realize continual learning, combining synthesized past MTS measurements with new observations to update the detection model. Using a large-scale, multi-dimensional network monitoring dataset collected from an operational mobile Internet Service Provider (ISP), we showcase the functionality of DC-VAE in the event of concept drifts, and study in-depth its generative characteristics, assessing GenDeX synthetically generated MTS examples. GenDeX enables DC- VAE adapting to continually evolving data, overcoming the limitations of catastrophic forgetting.
Digital Drift and the Evolution of a Large Cybercrime Forum

Cybercrime forum datasets are large and complex. Prior research uses aggregated time series data to create a picture of the whole dataset, or focuses on a smaller sample of cross sectional data, often for a specific subcommunity or crime time. This paper uses the longitudinal time series aspect of cybercrime forums to measure and observe the evolution of forums at a macro scale. Applying the digital drift theoretical framework, borrowed from criminology, we find a large amount of churn on the forum, with only a small proportion of users continuing long-term engagement. Measurements show a continual shift in forum activity, with yearbased cohorts moving from starting in hacking discussions, towards starting in general discussions, and later towards e-whoring boards. The group of members who are active on the forum for over 12 months, typically have their last post in the marketplace, while other members, who are active for shorter periods of time, have their last post in hacking-related boards. Overall, we see an increasing trend towards financially-driven cybercrime, at both the user and forum level. Users post more in financially-related boards over time, and forum activity has trended away from gaming/social activity, trending towards more activity in market-related boards.
SMART Credentials in the Multi-queue of Slackness (or Secure Management of Anonymous Reputation Traits without Global Halting)

Anonymous credentials encourage online communication without fear of surveillance, but may invite misbehavior like hate speech. Previous updatable anonymous credentials keep a chronological queue of authenticated sessions and a global pointer to the last chunk of judged sessions. This design allows efficient authentication for proving over only a subset of sessions. However, complications in subjective evaluation often introduce hard-to-judge sessions, which halt all users since sessions that come after the global pointer cannot be redeemed, eventually exceeding the queue size that limits the creation of bad sessions. Such a global-halting loophole may also make judgments overly harsh and hasty.We propose SMART (slack management of anonymous reputation traits), maintaining multiple queues so the server could issue interim judgments many times before finalization. Such slackness removes the binary judgment of old methods and mitigates the global-halting issue. Prior schemes only allow score upgrades (WPES ’14) or require proving against a global session list since the last checkpoint (S&P ’22). Our authentication time is linear in the number of queues or sessions in a designated queue for immediate revocation.
Forward Pass: On the Security Implications of Email Forwarding Mechanism and Policy

The critical role played by email has led to a range of extension protocols (e.g., SPF, DKIM, DMARC) designed to protect against the spoofing of email sender domains. These protocols are complex as is, but are further complicated by automated email forwarding — used by individual users to manage multiple accounts and by mailing lists to redistribute messages. In this paper, we explore how such email forwarding and its implementations can break the implicit assumptions in widely deployed anti-spoofing protocols. Using large-scale empirical measurements of 20 email forwarding services (16 leading email providers and four popular mailing list services), we identify a range of security issues rooted in forwarding behavior and show how they can be combined to reliably evade existing anti-spoofing controls. We further show how these issues allow attackers to not only deliver spoofed email messages to prominent email providers (e.g., Gmail, Microsoft Outlook, and Zoho), but also reliably spoof email on behalf of tens of thousands of popular domains including sensitive domains used by organizations in government (e.g., state.gov), finance (e.g., transunion.com), law (e.g., perkinscoie.com) and news (e.g., washingtonpost.com) among others.
SoK: Data Sovereignty

Society appears to be on the verge of recognizing the need for control over sensitive data in modern web applications. Recently, many systems claim to give control to individuals, promising the preeminent goal of data sovereignty. However, despite recent attention, research and industry efforts are fragmented and lack a holistic system overview. In this paper, we provide the first transecting systematization of data sovereignty by drawing from a dispersed body of knowledge. We clarify the field by identifying its three main areas: (i) decentralized identity, (ii) decentralized access control and (iii) policy-compliant decentralized computation. We find that literature lacks a cohesive set of formal definitions. Each area is considered in isolation, and priorities in industry and academia are not aligned due to a lack of clarity regarding user control. To solve this issue, we propose formal definitions for each sub-area. By highlighting that data sovereignty transcends the domain of decentralized identity, we aim to guide future works to embrace a broader perspective on user control. In each section, we augment our definition with security and privacy properties, discuss the state of the art and proceed to identify open challenges. We conclude by highlighting synergies between areas, emphasizing the real-world benefit obtained by further developing data sovereign systems.
Proof-of-Learning is Currently More Broken Than You Think

Proof-of-Learning (PoL) proposes that a model owner logs training checkpoints to establish a proof of having expended the computation necessary for training. The authors of PoL forego cryptographic approaches and trade rigorous security guarantees for scalability to deep learning. They empirically argued the benefit of this approach by showing how spoofing—computing a proof for a stolen model—is as expensive as obtaining the proof honestly by training the model. However, recent work has provided a counter-example and thus has invalidated this observation.In this work we demonstrate, first, that while it is true that current PoL verification is not robust to adversaries, recent work has largely underestimated this lack of robustness. This is because existing spoofing strategies are either unreproducible or target weakened instantiations of PoL—meaning they are easily thwarted by changing hyperparameters of the verification. Instead, we introduce the first spoofing strategies that can be reproduced across different configurations of the PoL verification and can be done for a fraction of the cost of previous spoofing strategies. This is possible because we identify key vulnerabilities of PoL and systematically analyze the underlying assumptions needed for robust verification of a proof. On the theoretical side, we show how realizing these assumptions reduces to open problems in learning theory. We conclude that one cannot develop a provably robust PoL verification mechanism without further understanding of optimization in deep learning.
faulTPM: Exposing AMD fTPMs’ Deepest Secrets

Trusted Platform Modules (TPMs) constitute an integral building block of modern security features. Moreover, as Windows 11 made a TPM 2.0 mandatory, they are subject to an ever-increasing academic challenge. While discrete TPMs (dTPMs) – as found in higher-end systems – have been susceptible to attacks on their exposed communication interface, more common firmware TPMs (fTPMs) are immune to this attack vector as they do not communicate with the CPU via an exposed bus.In this paper, we analyze a new class of attacks against fTPMs: Attacking their Trusted Execution Environment (TEE) can lead to a full TPM state compromise. We experimentally verify this attack by compromising the AMD Secure Processor (AMD-SP), which constitutes the TEE for AMD’s fTPMs. In contrast to previous dTPM sniffing attacks, this vulnerability exposes the complete internal TPM state of the fTPM. It allows us to extract any cryptographic material stored or sealed by the fTPM regardless of authentication mechanisms such as Platform Configuration Register (PCR) validation or passphrases with anti-hammering protection. First, we demonstrate the impact of our findings by – to the best of our knowledge – enabling the first attack against Full Disk Encryption (FDE) solutions backed by an fTPM. Furthermore, we lay out how any application relying solely on the security properties of the TPM – like Bitlocker’s TPM-only protector – can be defeated by an attacker with 2-3 hours of physical access to the target device. Lastly, we analyze the impact of our attack on FDE solutions protected by a TPM and PIN strategy. While a naive implementation also leaves the disk completely unprotected, we find that BitLocker’s FDE implementation withholds some protection depending on the complexity of the used PIN. Our results show that when an fTPM’s internal state is compromised, a TPM and PIN strategy for FDE is less secure than TPM-less protection with a reasonable passphrase.
Revisiting OAuth 2.0 Compliance: A Two-Year Follow-Up Study

OAuth 2.0 is a widely used authorization protocol that allows third-party access to an authorization service on behalf of a user.Like any security protocol, it requires careful implementation to ensure security.Previous research has thoroughly analyzed the security of the OAuth protocol, but popular deployments remain vulnerable due to incorrect or limited implementation of the standards.In our previous work, we introduced a tool called OAUCH to measure and improve compliance with the OAuth standards.We used the tool to measure the compliance of 100 OAuth implementations and created a unique overview of the state of practice within the OAuth ecosystem.This paper revisits these prior results and updates our measurements.We compare the latest results to the original baseline and identify changes in the ecosystem.Our analysis shows that IdPs have become more compliant in the past two years, but a substantial number still lack fundamental countermeasures.
Coverage and Secure Use Analysis of Content Security Policies via Clustering

Content Security Policy (CSP) is a standardized leading technique for protecting webpages against attacks such as Cross Site Scripting (XSS). However, it is often hard to properly deploy CSPs on webpages, and the deployed CSPs often contain security issues or errors. In this paper, we take the unsupervised clustering approach to analyze the security levels of the deployed CSPs from the directive coverage and secure use perspectives. To effectively protect a webpage, a deployed CSP should cover all types of resources needed on the webpage by using different directive names (or some default directive names if available), and should avoid using unsafe directive values which will allow harmful resources to be loaded into a webpage. We implemented a Google Chrome extension, designed policy features, designed a Contrastive Spectral Clustering (CSC) algorithm, and visited the Alexa top 100K websites to analyze the CSPs deployed on them. From the 13,317 homepages that deployed CSPs under the enforcement mode, we categorized their policies into 16 clusters with different characteristics. We found that 15 clusters are at the low level on the coverage and five clusters are at the low level on the secure use of directives; meanwhile, no cluster is at the high level on the coverage of directives, and nine clusters are at the high level on the secure use of directives. These results indicate that most deployed CSPs do not sufficiently protect webpages, and more importantly, clustering helps identify the corresponding common or different reasons from the directive coverage and secure use perspectives. In addition, by analyzing 110,718 subpages of the 13,317 CSP-deployed homepages, we found that most of them deployed the same CSP as in their homepages. Overall, our approach and results can be helpful for promoting the proper deployment of CSPs.
An Argument for Linguistic Expertise in Cyberthreat Analysis: LOLSec in Russian Language eCrime Landscape

In this position paper, we argue for a holistic perspective on threat analysis and other studies of state-sponsored or state-aligned eCrime groups. Specifically, we argue that understanding eCrime requires approaching it as a sociotechnical system and that studying such a system requires combining linguistic, regional, professional, and technical expertise. To illustrate it, we focus on the discourse of the Conti ransomware group in the context of the Russian invasion of Ukraine. We discuss the background of this group and their actions and argue that the technical approach alone can lose the important aspects specific to the cultural and linguistic context, such as language, slang and humor. We provide examples of how the discourse and threats from such groups can be easily misunderstood without appropriate linguistic and domain expertise.
Secure Key Management for Multi-Party Computation in MOZAIK

The immense growth of data from the proliferation of Internet of Things (IoT) devices presents opportunities and challenges for privacy engineering. On the one hand, this data can be harnessed for personalized services, cost savings, and environmental benefits. On the other hand, (new) legislation must be complied with and privacy risks arise from collecting and processing of such data. Distributed privacy-preserving analytics offers a promising solution, providing insights while also protecting privacy. However, this approach has new challenges and risks, such as key management and confidentiality. When designing a data marketplace which offers distributed privacy-preserving analytics, the key management comes with different threats, which require a solution adapted to the distributed architecture.In this context, the paper presents a comprehensive, end-to-end secure system called MOZAIK for privacy-preserving data collection, analysis, and sharing. The article focuses on the key management aspect of the secure multi-party computation (MPC) component in a distributed privacy-preserving analytics architecture and the specific challenges created by introducing MPC. The proposed solution involves temporary storage of (symmetric) key shares and public-key encryption schemes to ensure secure key management for privacy-preserving computation. Our solution has the potential to be applied in other MPC-based setups, making it a valuable addition to the field of privacy engineering. By addressing key management challenges and risks, MOZAIK enhances data protection while enabling valuable insights from IoT data.
Inside Residential IP Proxies: Lessons Learned from Large Measurement Campaigns

Residential IP Proxy (RESIP) providers represent a growing threat when used for web scraping and other malicious activities.RESIPs enable their customers to hide behind a vast network of residential IP addresses to perpetrate their actions.This helps the customers to evade detection.Thanks to two new large datasets of RESIP connections, we reveal new insights into RESIP inner functioning and modus operandi.We present the similarities and differences of the ecosystems associated with four RESIP providers (geographic distribution, types, management and amount of machines used).Moreover, we display how two of the providers have striking similarities and we propose a specific detection method to identify them.Furthermore, we show how to build a list of suspicious /24 blocks of IP addresses and use it to mitigate the actions of malicious parties behind RESIPs.
Hexanonymity: a scalable geo-positioned data clustering algorithm for anonymisation purposes

Advances in sensors, trackers and positioning systems had led to the emergence of multiple locationbased services (LBS), resulting in multiple devices and users reporting their precise position and raising many privacy concerns. Anonymisation of geo-positioned data can provide a high level of privacy to the end users, but usually at the cost of introducing high levels of information loss on the location reported to the LBS. This paper presents Hexanonymity, a new algorithm for the anonymisation of geo-positioned data which introduces a limited amount of information loss while providing k-anonymity. Hexanonymity leverages the Uber H3 geo-indexing system, which subdivides the earth into hexagonal meshes. We take advantage of a property of hexagon meshes, where for any of them, the distance from its centre to the centre of the six surrounding hexagons is always the same. This property allows the algorithm to generate high-quality clusters of geo-positioned data points, introducing a limited information loss. This new algorithm improves the current state-of-the-art in terms of the quality of the anonymised data points while providing a similar level of privacy, with a percentage of anonymised locations reduced by only 0.503% when compared to Adaptive Interval Cloaking. Hexanonymity leverages geo-indexing systems to offer a scalable approach to the anonymisation of geo-positioned data in linear time, suitable for big data and real-time scenarios.
An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph

Over the years, open-source software systems have become prey to threat actors.Even highly-adopted software has been crippled by unforeseeable attacks, leaving millions of devices exposed.Even as open-source communities act quickly to patch the breach, code vulnerability screening should be an integral part of agile software development from the beginning.Unfortunately, current vulnerability screening techniques are ineffective at identifying novel vulnerabilities or providing developers with code vulnerability and classification.Furthermore, the datasets used for vulnerability learning often exhibit distribution shifts from the real-world testing distribution due to novel attack strategies deployed by adversaries and as a result, the machine learning model's performance may be hindered or biased.To address these issues, we propose a joint interpolated multitasked unbiased vulnerability classifier comprising a transformer "RoBERTa" and graph convolution neural network (GCN).We present a training process utilizing a semantic vulnerability graph (SVG) representation from source code, created by integrating edges from a sequential flow, control flow, and data flow, as well as a novel flow dubbed Poacher Flow (PF).Poacher flow edges reduce the gap between dynamic and static program analysis and handle complex long-range dependencies.Moreover, our approach reduces biases of classifiers regarding unbalanced datasets by integrating Focal Loss objective function along with SVG.Remarkably, experimental results show that our classifier outperforms state-of-the-art results on vulnerability detection with fewer false negatives and false positives.After testing our model across multiple datasets, it shows an improvement of at least 2.41% and 18.75% in the bestcase scenario.Evaluations using N-day program samples demonstrate that our proposed approach achieves a 93% accuracy and was able to detect 4, zero-day vulnerabilities from popular GitHub repositories.
Active Countermeasures for Email Fraud

As a major component of online crime, email-based fraud is a threat that causes substantial economic losses every year. To counteract these scammers, volunteers called scam-baiters play the roles of victims, reply to scammers, and try to waste their time and attention with long and unproductive conversations. To curb email fraud and magnify the effectiveness of scam-baiting, we developed and deployed an expandable scam-baiting mailserver that can conduct scam-baiting activities automatically. We implemented three reply strategies using three different models and conducted a one-month-long experiment during which we elicited 150 messages from 130 different scammers. We compare the performance of each strategy at attracting and holding the attention of scammers, finding tradeoffs between human-written and automatically-generated response strategies. We also demonstrate that scammers can be engaged concurrently by multiple servers deploying these strategies in a second experiment, which used two server instances to contact 92 different scammers over 12 days. We release both our platform and a dataset containing conversations between our automatic scam-baiters and real human scammers, to support future work in preventing online fraud.