Reviewerly/exHarmony huggingface dataset:
RandCompile: Removing Forensic Gadgets from the Linux Kernel to Combat its Analysis

Recently proposed tools such as LogicMem, Katana, and AutoProfile enable a fine-grained inspection of the operating system’s memory. They provide insights that were previously only available for Linux machines specifically instrumented for cooperation with virtual machine introspection frameworks. An overly controlling cloud operator can now regularly deep-inspect VMs under their control.
FLARE: Fingerprinting Deep Reinforcement Learning Agents using Universal Adversarial Masks

We propose FLARE, the first fingerprinting mechanism to verify whether a suspected Deep Reinforcement Learning (DRL) policy is an illegitimate copy of another (victim) policy. We first show that it is possible to find non-transferable, universal adversarial masks, i.e., perturbations, to generate adversarial examples that can successfully transfer from a victim policy to its modified versions but not to independently trained policies. FLARE employs these masks as fingerprints to verify the true ownership of stolen DRL policies by measuring an action agreement value over states perturbed by such masks. Our empirical evaluations show that FLARE is effective (100% action agreement on stolen copies) and does not falsely accuse independent policies (no false positives). FLARE is also robust to model modification attacks and cannot be easily evaded by more informed adversaries without negatively impacting agent performance. We also show that not all universal adversarial masks are suitable candidates for fingerprints due to the inherent characteristics of DRL policies. The spatio-temporal dynamics of DRL problems and sequential decision-making process make characterizing the decision boundary of DRL policies more difficult, as well as searching for universal masks that capture the geometry of it.
When Push Comes to Shove: Empirical Analysis of Web Push Implementations in the Wild

Web push notifications are becoming an increasingly prevalent capability of modern web apps, intended to create a direct communication pipeline with users and increase user engagement. The seemingly straightforward functionality of push notifications obscures the complexities of the underlying design and implementation, which deviates from a near-universal practice in the web ecosystem: the ability to access an account (and the associated functionality) from practically any browser or device upon successful completion of the authentication process. Instead, push notifications create a communication endpoint for a specific browser instance. As a result, the challenges of deploying push notifications are further exacerbated due to the integration obstacles that arise from other aspects of web apps and user browsing behaviors (e.g., multi-device environments, account and session management). In this paper, we conduct an empirical analysis of push notification implementations in the wild, and identify common deployment pitfalls. We also demonstrate a series of attacks that target push notification functionality, including a novel subscription-sniffing attack, through a selection of use cases. To better understand current practices in push notifications implementations, we present a large-scale measurement of their deployment and also provide the first, to our knowledge, exploration and analysis of third-party service providers. Finally, we provide guidelines for developers and propose an approach for correctly handling push notifications in multi-browser, post-authentication settings.
From Attachments to SEO: Click Here to Learn More about Clickbait PDFs!

Clickbait PDFs are PDF documents that do not embed malware but trick victims into visiting malicious web pages leading to attacks like password theft or drive-by download. While recent reports indicate a surge of clickbait PDFs, prior works have largely neglected this new threat, considering PDFs only as accessories of email phishing campaigns.
Link Membership Inference Attacks against Unsupervised Graph Representation Learning

Significant advancements have been made in recent years in the field of unsupervised graph representation learning (UGRL) approaches. UGRL involves representing large graphs as low-dimensional vectors, commonly referred to as embeddings. These embeddings can be publicly released or shared with third parties for downstream analytics. However, adversaries can deduce sensitive structural information from the target graph through its embedding using various types of privacy inference attacks. This paper investigates the privacy vulnerabilities of UGRL models through the lens of link membership inference attack (LMIA). Specifically, an LMIA adversary aims to infer whether any two nodes are connected in the target graph from the node embeddings generated by a UGRL model. To achieve this, we propose two LMIA attacks that leverage the properties of node embeddings and various forms of adversary knowledge for inference. By conducting experiments on four state-of-the-art UGRL models using five real-world graph datasets, we demonstrate the effectiveness of the two LMIA attacks against these UGRL models. Furthermore, we conduct a comprehensive analysis to examine how varying degrees of preserved structural information in the embeddings impact the performance of LMIA. To enhance the security of UGRL models against LMIA, we design a family of defense mechanisms that perturb the least significant dimensions of embeddings. Our experimental results show that our defense mechanism achieves a favorable balance between defense effectiveness and embedding quality.
Remote Attestation with Constrained Disclosure

Trusted Platform Modules (TPMs) are used for remote attestation to ensure the authenticity and integrity of software running on a computer system. However, measuring software executed as containers or virtual machines can be challenging as it is measured concurrently, resulting in a jumbled measurement log that is difficult to disentangle. Moreover, disclosing the entire measurement log in traditional binary remote attestation raises privacy and intellectual property concerns. To address these issues, we propose a remote attestation method with constrained disclosure, allowing for selective disclosure of entries in the measurement log using a non-interactive zero-knowledge (NIZK) proof with Schnorr signatures. Our approach is evaluated for security and privacy and proven to be correct, sound, and satisfies the properties of a NIZK proof. Formal verification of our solution with ProVerif also supports our claims. Furthermore, the performance evaluation of our proof-of-concept implementation shows that our contribution is feasible, and the overhead introduced is negligible.
FraudLens: Graph Structural Learning for Bitcoin Illicit Activity Identification

Illicit activity in cryptocurrency has increased dramatically over the years. Bitcoin mechanics allow for users to mask their identity through obfuscation techniques. Much research has been published in the domain of identifying illicit activity in cryptocurrency, and in particular the emergence of Graph Neural Networks (GNNs) has shown great promise in this area. In this paper, we propose two graph preprocessing methods to improve performance and robustness of our node classification GNN models in identifying illicit transactions in the Bitcoin network. Our methods focus on graph restructuring through measuring the connectivity of nodes in a graph, and the similarity of the underlying features each node possesses. We demonstrate the graph restructuring methodologies on five GNN architectures and empirically show an improvement of evaluation metrics when compared against the unprocessed graph dataset. We compare our proposed methods against other imbalanced node classification techniques on a common graph dataset. This methodology has great opportunity in the transaction monitoring landscape for exchanges and financial institutions attempting to capture potential illicit activity taking place on their networks including money laundering.
DOPE: DOmain Protection Enforcement with PKS

The number of Linux kernel vulnerabilities discovered has increased drastically over the past years. In the kernel, even simple memory safety vulnerabilities can have devastating consequences, e.g., compromising the entire system. Efforts to mitigate these vulnerabilities have so far focused mainly on control-flow hijacking attacks in the kernel. Yet, data-oriented attacks remain largely unmitigated in practice as existing mitigations are limited in providing robust security guarantees at reasonable performance overhead for multiple sensitive data objects.
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks

Training deep neural networks (DNNs) requires large datasets and powerful computing resources, which has led some owners to restrict redistribution without permission. Watermarking techniques that embed confidential data into DNNs have been used to protect ownership, but these can degrade model performance and are vulnerable to watermark removal attacks. Recently, DeepJudge was introduced as an alternative approach to measuring the similarity between a suspect and a victim model. While DeepJudge shows promise in addressing the shortcomings of watermarking, it primarily addresses situations where the suspect model copies the victim’s architecture. In this study, we introduce DeepTaster, a novel DNN fingerprinting technique, to address scenarios where a victim’s data is unlawfully used to build a suspect model. DeepTaster can effectively identify such DNN model theft attacks, even when the suspect model’s architecture deviates from the victim’s. To accomplish this, DeepTaster generates adversarial images with perturbations, transforms them into the Fourier frequency domain, and uses these transformed images to identify the dataset used in a suspect model. The underlying premise is that adversarial images can capture the unique characteristics of DNNs built with a specific dataset. To demonstrate the effectiveness of DeepTaster, we evaluated the effectiveness of DeepTaster by assessing its detection accuracy on three datasets (CIFAR10, MNIST, and Tiny-ImageNet) across three model architectures (ResNet18, VGG16, and DenseNet161). We conducted experiments under various attack scenarios, including transfer learning, pruning, fine-tuning, and data augmentation. Specifically, in the Multi-Architecture Attack scenario, DeepTaster was able to identify all the stolen cases across all datasets, while DeepJudge failed to detect any of the cases.
Domain and Website Attribution beyond WHOIS

Currently, WHOIS is the main method for identifying which company or individual owns a domain or website. But, WHOIS usefulness is limited due to privacy protection services and data redaction. We present a novel automated approach for domain and website attribution. When WHOIS data does not reveal the owner, our approach leverages information from multiple other sources such as passive DNS, TLS certificates, and the analysis of website content. We propose a novel ranking technique to select the domain owner among multiple identified entities. Our approach identifies the domain owner with an F1 score of 0.94 compared to 0.54 for WHOIS. When applied on 3,001 tracker domains from the popular Disconnect list, it identifies needed updates to the list. It also attributes 84% of previously unattributed tracker domains.
An Empirical Analysis of Enterprise-Wide Mandatory Password Updates

Enterprise-scale mandatory password changes are disruptive, complex endeavors that require the entire workforce to prioritize a goal that is often secondary to most users. While ample literature exists around user perceptions and struggles, there are few “best practices” from the perspective of the enterprise—either to achieve the end goal or to minimize IT costs. In this paper, we provide an empirical analysis of an enterprise-scale mandatory password change, covering almost 10,000 faculty and staff at an academic institution. Using a combination of user notifications logs, password update records, and help desk ticket information, we construct an empirical model of user response over time. In particular, we characterize the elements of the campaign that relate to ideal and non-ideal outcomes, including unnecessary user actions and IT help desk overhead. We aim to provide insight into successes and challenges that can generalize to other organizations implementing similar initiatives.
OAuth 2.0 Redirect URI Validation Falls Short, Literally

OAuth 2.0 requires a complex redirection trail between websites and Identity Providers (IdPs). In particular, the "redirect URI" parameter included in the popular Authorization Grant Code flow governs the callback endpoint that users are routed to, together with their security tokens. The protocol specification, therefore, includes guidelines on protecting the integrity of the redirect URI.
DefWeb: Defending User Privacy against Cache-based Website Fingerprinting Attacks with Intelligent Noise Injection

Cache-based website fingerprinting (WF) attacks violate user privacy where the attacker leverages the shared last-level cache in CPUs and analyzes the fingerprints through machine learning and deep learning models. WF attacks are even applicable in Incognito and anonymized browser platforms, leading to a serious threat to the public. Several defense techniques inject random noise during website rendering to degrade the attack success rate, while these techniques either create large performance overhead or cannot obfuscate the WF dataset entirely when the attacker retrains a new learning model with noisy fingerprints.
Mostree: Malicious Secure Private Decision Tree Evaluation with Sublinear Communication

A private decision tree evaluation (PDTE) protocol allows a feature vector owner (FO) to classify its data using a tree model from a model owner (MO) and only reveals an inference result to the FO. This paper proposes Mostree, a PDTE protocol secure in the presence of malicious parties with sublinear communication. We design Mostree in the three-party honest-majority setting, where an (untrusted) computing party (CP) assists the FO and MO in the secure computation. We propose two low-communication oblivious selection (OS) protocols by exploiting nice properties of three-party replicated secret sharing (RSS) and distributed point function. Mostree combines OS protocols with a tree encoding method and three-party secure computation to achieve sublinear communication. We observe that most of the protocol components already maintain privacy even in the presence of a malicious adversary, and what remains to achieve is correctness. To ensure correctness, we propose a set of lightweight consistency checks and seamlessly integrate them into Mostree. As a result, Mostree achieves sublinear communication and malicious security simultaneously. We implement Mostree and compare it with the state-of-the-art. Experimental results demonstrate that Mostree is efficient and comparable to semi-honest PDTE schemes with sublinear communication. For instance, when evaluated on the MNIST dataset in a LAN setting, Mostree achieves an evaluation using approximately 768 ms with communication of around 168 KB.
Hades: Practical Decentralized Identity with Full Accountability and Fine-grained Sybil-resistance

Decentralized identity (DID), the idea of giving users complete control over their identity-related data, is being used to solve the privacy tension in the identity management of decentralized applications (Dapps). While existing approaches do an excellent job of solving the privacy tension, they have not adequately addressed the accountability and Sybil-resistance issues. Moreover, these approaches have a considerable gas overhead, making them impractical for Dapps.
Differentially Private Resource Allocation

Recent studies have shown that systems with limited resources like Metadata-private Messenger (MPM) suffer from side-channel attacks under resource allocation (RA). In the case of MPM, which is designed to keep the identities and activities of both callers and callees private from network adversaries, an attacker can compromise a victim’s friends and keep calling the victim to infer whether the victim is busy, which breaks the privacy guarantee of MPM.
Attack of the Knights:Non Uniform Cache Side Channel Attack

For a distributed last-level cache (LLC) in a large multicore chip, the access time to one LLC bank can significantly differ from that to another due to the difference in physical distance. In this paper, we successfully demonstrate a new distance-based side-channel attack by timing the AES decryption operation and extracting part of an AES secret key on an Intel Knights Landing CPU. We introduce several techniques to overcome the challenges of the attack, including the use of multiple attack threads to ensure LLC hits, to detect vulnerable memory locations, and to obtain fine-grained timing of the victim operations. While operating as a covert channel, this attack can reach a bandwidth of 205 KBPS with an error rate of only 0.02%. We also observed that the side-channel attack can extract 4 bytes of an AES key with 100% accuracy with only 4000 trial rounds of encryption.
On the Detection of Image-Scaling Attacks in Machine Learning

Image scaling is an integral part of machine learning and computer vision systems. Unfortunately, this preprocessing step is vulnerable to so-called image-scaling attacks where an attacker makes unnoticeable changes to an image so that it becomes a new image after scaling. This opens up new ways for attackers to control the prediction or to improve poisoning and backdoor attacks. While effective techniques exist to prevent scaling attacks, their detection has not been rigorously studied yet. Consequently, it is currently not possible to reliably spot these attacks in practice.
14P Classification of molecular subtypes of breast cancer in whole-slide histopathological images using a novel deep learning algorithm

Classification of molecular subtypes of breast cancer is widely used in clinical decision-making, leading to different treatment responses and clinical outcomes. We classified molecular subtypes using a novel deep learning algorithm in whole-slide histopathological images (WSIs) with invasive ductal carcinoma of the breast. We obtained 1,094 breast cancer cases with available hematoxylin and eosin-stained WSIs from the TCGA database. We applied a new deep learning algorithm for artificial neural networks (ANNs) that is completely different from the back-propagation method developed in previous studies. Our model based on the ANN algorithm had an accuracy of 67.8% for all datasets (training and testing), and the area under the receiver operating characteristic curve was 0.819 when classifying molecular subtypes of breast cancer. In approximately 30% of cases, the molecular subtype did not reflect the unique histological subtype, which lowered the accuracy. The set revealed relatively high sensitivity (70.5%) and specificity (84.4%). Our approach involving this ANN model has favorable diagnostic performance for molecular classification of breast cancer based on WSIs and could provide reliable results for planning treatment strategies.
Prominent expression method for line features in multiview reconstruction

In multi-view reconstruction, mesh models are one of the most important data carriers. However, existing reconstruction methods have not adequately considered the existence of real-world edge features, resulting in insufficient utilization of geometric feature information from images. This leads to issues such as surface distortion and a lack of prominent edge features in the reconstructed mesh models. To address these challenges, this paper proposes a method of line features prominent expression during the multi-view reconstruction process. This algorithm integrates image line features and the mesh model to enhance geometric features. Specifically, in this paper, the line features on the image are first extracted, and the 2D line features are sampled and fitted to synthesize 3D line features by depth map. Then, it clusters the 3D line features and establishes minimum bounding boxes. Finally, the mesh line features are determined based on graph cut methods and are prominently expressed in the mesh model. Experimental validation demonstrates that in terms of the accuracy of line feature expression, the average reprojection pass rate for line features in five datasets reaches 93.7 %. This proves that this method significantly mitigates the issue of unclear edge features on the model surface, greatly improving the visual quality of mesh models while maintaining a high accuracy.
Big Data for Social Media Analysis During the COVID-19 Pandemic: An Emotion Analysis Based on Influences from Social Networks

Since the beginning of the COVID-19COVID-19 outbreak, the situation has soon become the most popular topic across social networksSocial networks worldwide. For almost three years, news and social media were covered with information, updates, and daily/regular reports on the COVID-19COVID-19 pandemic. When face-to-face activities were restricted, people started to use social networksSocial networks more than before. The situation led to the empowermentEmpowerment of digital media, such as social media and networks. These platforms became the leading online social hubs for people to express their feelings and record their daily lives during the pandemic. In this study, we analyzed this critical societal change as a major topic, mainly related to the pandemic on WeiboWeibo, which is the most popular social media platform in ChinaChina. The study is focused on one particular context allowing in-depth data analysis related to China’sChina societal impactSocietal impact due to the COVID-19COVID-19 pandemic. The study also investigates the relationship between COVID-19COVID-19 trends or topics and public sentimentsSentiment on social networksSocial networks. Machine learningMachine learning (ML) is used to verify the correlation between emotion on social media and the COVID-19COVID-19 pandemic trends. The study concludes with a prediction model using big data for public sentimentsSentiment on social media.
Optimized Clustering Model for Healthcare Sentiments on Twitter: A Big Data Analysis Approach

Social media, such as TwitterTwitter, typically stores a large amount of user-generated content regarding different aspects of society. These contents include social eventsSocial event, e-commerceE-Commerce products, healthcare, etc. This chapter proposes a best-fitted clusteringClustering method to classify sentimentSentiment samples related to healthcare topics. Thus, we examine other clusteringClustering models with keyword extraction methods on the real healthcare datasets collected from TwitterTwitter. The experiment results indicate that self-organized mapSelf-organized map (SOM) model with the TF-IDF extraction method can achieve the best clusteringClustering accuracyAccuracy. Moreover, the optimized model can have great potential to handle large-scale data in real practice.
Shadow Costar: Exploring Collaborative Performances in Virtual Reality

Artistic performances increasingly incorporate agents into interactive media art, facilitating collaborative endeavors between humans and these agents. Drawing inspiration from the ongoing "ShadowPlayVR" project, which melds virtual reality (VR) with classical Chinese shadow puppetry, this paper introduces the "Shadow Costar" agent prototype. Designed as both a co-performing companion and guide, "Shadow Costar" aims to enhance VR immersion and performance capabilities. The paper delves into design considerations, emphasizing the intricate balance between honoring traditional puppetry and leveraging modern technological immersion.