Reviewerly/exHarmony huggingface dataset:
Deep learning assisted quality ranking for list decoding of videos subject to transmission errors

In this paper, we propose a new deep learning-based quality ranking framework to assist video list decoding methods in the context of unreliable video transmissions. The objective is to identify an intact image (corrected video frame) among a list of candidate images generated by a list decoding method, where all candidates, except for the intact image are corrupted. The framework comprises a deep learning-based no-reference image quality assessment (NR-IQA) for non-uniform video distortions (NUD) system to rank the candidate images according to their quality, which allows identifying the best one. To show the validity of our proposed framework, we develop an NR-IQA system relying on a proven patch-based convolutional neural network (CNN) architecture, which we adapt to better account for the non-uniform distortions observed in the candidate images, e.g., H.265 transmission errors during wireless communications. Specifically, we modify the patch size on which our CNN for non-uniform distortions (CNN-NUD) operates to capture a larger and more meaningful spatial context. Moreover, we develop a new training database using images resulting from various bit modifications in the received video packets, to simulate the list decoding process, and train the system using a full reference IQA (FR-IQA) method. Experiments on intra frames of videos encoded using H.265 show the ability of this system to identify an intact image among a set of five candidate images with an average accuracy of 96.6%, whereas traditional NR-IQA metrics or the initially trained CNN system offer poor accuracy ranging between 15.7% and 33.6%, respectively.
Exploring the Potential of Residual Networks for Efficient Sub-Nyquist Spectrum Sensing

We propose ReSense, a residual network for spectrum sensing high-frequency signals with low-frequency samplers. ReSense first transforms the aliased signal from low-frequency samplers into image-like inputs and uses multiple convolution layers and skip connections to predict the signal’s frequency components to enable spectrum sensing. We evaluate ReSense on the signal dataset with single and double frequencies and achieve 95% and 40% accuracy in detecting modulation type on respective datasets, indicating accurate spectrum sensing.
Multi-Feature Physical Layer Authentication for URLLC based on Linear Supervised Learning

Physical Layer Authentication (PLA) can be a lightweight alternative to conventional security schemes such as certificates or Message Authentication Codes (MACs) for secure message transmission within Ultra Reliable Low Latency Communication (URLLC) scenarios. Single features such as Received Signal Strength Indicator (RSSI) are however not providing sufficient authentication accuracy. Therefore, multi-feature techniques for PLA are introduced within this work and evaluated using a Universal Software Radio Peripheral (USRP) based testbed in a mobile URLLC campus network scenario. Linear supervised classification is proposed for PLA and evaluated under different attacker scenarios. The results show promising authentication performances in most of the evaluated senarions and can be increased by the application of multi-feature authentication.
Deep Bribe: Predicting the Rise of Bribery in Blockchain Mining with Deep RL

Blockchain security relies on incentives to ensure participants, called miners, cooperate and behave as the protocol dictates. Such protocols have a security threshold – a miner whose relative computational power is larger than the threshold can deviate to improve her revenue. Moreover, blockchain participants can behave in a petty compliant manner: usually follow the protocol, but deviate to increase revenue when deviation cannot be distinguished externally from the prescribed behavior. The effect of petty compliant miners on the security threshold of blockchains is not well understood. Due to the complexity of the analysis, it remained an open question since Carlsten et al. identified it in 2016. In this work, we use deep Reinforcement Learning (RL) to analyze how a rational miner performs selfish mining by deviating from the protocol to maximize revenue when petty compliant miners are present. We find that a selfish miner can exploit petty compliant miners to increase her revenue by bribing them. Our method reveals that the security threshold is lower when petty compliant miners are present. In particular, with parameters estimated from the Bitcoin blockchain, we find the threshold drops from the known value of 25% to only 21% (or 19%) when 50% (or 75%) of the other miners are petty compliant. Hence, our deep RL analysis puts the open question to rest; the presence of petty compliant miners exacerbates a blockchain's vulnerability to selfish mining and is a major security threat.
Benchmarking the Effect of Poisoning Defenses on the Security and Bias of Deep Learning Models

Machine learning models are susceptible to a class of attacks known as adversarial poisoning where an adversary can maliciously manipulate training data to hinder model performance or, more concerningly, insert backdoors to exploit at inference time. Many methods have been proposed to defend against adversarial poisoning by either identifying the poisoned samples to facilitate removal or developing poison agnostic training algorithms. Although effective, these proposed approaches can have unintended consequences on the model, such as worsening performance on certain data sub-populations, thus inducing a classification bias. In this work, we evaluate several adversarial poisoning defenses. In addition to traditional security metrics, i.e., robustness to poisoned samples, we also adapt a fairness metric to measure the potential undesirable discrimination of sub-populations resulting from using these defenses. Our investigation highlights that many of the evaluated defenses trade decision fairness to achieve higher adversarial poisoning robustness. Given these results, we recommend our proposed metric to be part of standard evaluations of machine learning defenses.
ASanity: On Bug Shadowing by Early ASan Exits

Bugs in memory-unsafe languages are a major source of critical vulnerabilities. Large-scale fuzzing campaigns, such as Google's OSS-Fuzz, can help find and fix these bugs. To find bugs faster during fuzzing, as well as to cluster and triage the bugs more easily in an automated setup, the targets are compiled with a set of sanitizers enabled, checking certain conditions at runtime. The most common sanitizer, ASan, reports common bug patterns found during a fuzzing campaign, such as out-of-bounds reads and writes or use-after-free bugs, and aborts the program early. The information also contains the type of bug the sanitizer found. During triage, out-of-bounds reads are often considered less critical than other bugs, namely out-of-bounds writes and use-after-free bugs. However, in this paper we show that these more severe vulnerabilities can remain undetected in ASan, shadowed by an earlier faulty read access. To prove this claim empirically, we conduct a large-scale study on 814 out-of-bounds read bugs reported by OSS-Fuzz. By rerunning the same testcases, but disabling ASan's early exits, we show that almost five percent of test cases lead to more critical violations later in the execution. Further, we pick the real-world target wasm3, and show how the reported out-of-bounds read covered up an exploitable out-of-bounds write, that got silently patched.
ROPfuscator: Robust Obfuscation with ROP

Software obfuscation is crucial in protecting intellectual property in software from reverse engineering attempts. While some obfuscation techniques originate from the obfuscation-reverse engineering arms race, others stem from different research areas, such as binary software exploitation. Return-oriented programming (ROP) became one of the most effective exploitation techniques for memory error vulnerabilities. ROP interferes with our natural perception of a process control flow, inspiring us to repurpose ROP as a robust and effective form of software obfuscation. Although previous work already explores ROP's effectiveness as an obfuscation technique, evolving reverse engineering research raises the need for principled reasoning to understand the strengths and limitations of ROP-based mechanisms against man-at-the-end (MATE) attacks. To this end, we present ROPFuscator, a compiler-driven obfuscation pass based on ROP for any programming language supported by LLVM. We incorporate opaque predicates and constants and a novel instruction hiding technique to withstand sophisticated MATE attacks. More importantly, we introduce a realistic and unified threat model to thoroughly evaluate ROPFuscator and provide principled reasoning on ROP-based obfuscation techniques that answer to code coverage, incurred overhead, correctness, robustness, and practicality challenges. The project's source code is published online to aid further research.
On the Brittleness of Robust Features: An Exploratory Analysis of Model Robustness and Illusionary Robust Features

Neural networks have been shown to be vulnerable to visual data perturbations imperceptible to the human eye. Nowadays, the leading hypothesis about the reason for the existence of these adversarial examples is the presence of non-robust features, which are highly predictive but brittle. Also, it has been shown that there exist two types of non-robust features depending on whether or not they are entangled with robust features; perturbing non-robust features entangled with robust features can form adversarial examples. This paper extends earlier work by showing that models trained exclusively on robust features are still vulnerable to one type of adversarial example. Standard-trained networks can classify more accurately than robustly trained networks in this situation. Our experiments show that this phenomenon is due to the high correlation between most of the robust features and both correct and incorrect labels. In this work, we define features highly correlated with correct and incorrect labels as illusionary robust features. We discuss how perturbing an image attacking robust models affects the feature space. Based on our observations on the feature space, we explain why standard models are more successful in correctly classifying these perturbed images than robustly trained models. Our observations also show that, similar to changing non-robust features, changing some of the robust features is still imperceptible to the human eye.
Your Email Address Holds the Key: Understanding the Connection Between Email and Password Security with Deep Learning

In this work, we investigate the effectiveness of deep-learning-based password guessing models for targeted attacks on human-chosen passwords. In recent years, service providers have increased the level of security of users' passwords. This is done by requiring more complex password generation patterns and by using computationally expensive hash functions. For the attackers this means a reduced number of available guessing attempts, which introduces the necessity to target their guess by exploiting a victim's publicly available information. In this work, we introduce a context-aware password guessing model that better capture attackers' behavior. We demonstrate that knowing a victim's email address is already critical in compromising the associated password and provide an in-depth analysis of the relationship between them. We also show the potential of such models to identify clusters of users based on their password generation behaviour, which can spot fake profiles and populations more vulnerable to context-aware guesses. The code is publicly available at https://github.com/spring-epfl/DCM_sp.
Distributed (α, β)-Core Decomposition over Bipartite Graphs

(α, β)-core is an important cohesive subgraph model for bipartite graphs. Given a bipartite graph G, the problem of (α, β)-core decomposition is to compute non-empty (α, β)-cores for all possible values of α and β. The state-of-the-art (α, β)-core decomposition algorithm is a peeling-based algorithm, which iteratively deletes the vertex from high degree to low degree. However, as the peeling-based algorithm is designed for centralized environments, it cannot be applied to distributed environments, where graphs are partitioned and stored in different machines. Motivated by this, in this paper, we study the distributed (α, β)-core decomposition problem, aiming to develop new algorithms to support (α, β)-core decomposition in distributed environments. To this end, first, we analyze the local properties of (α, β)-core, and devise n-order Bi-indexes for the vertex, which are iteratively defined using the vertex neighbors’ (n − 1)-order Bi-indexes. Next, we propose an algorithm for (α, β)-core decomposition through iteratively calculating n-order Bi-indexes for every vertex. To further improve the efficiency of the algorithm, we propose two optimizations. Then, we extend our proposed algorithms to different distributed graph processing frameworks to make them run in distributed environments. Finally, extensive experimental results on both real and synthetic bipartite graphs demonstrate the efficiency of our proposed algorithms.
A Survey of Parser Differential Anti-Patterns

Parser differentials emerge when two (or more) parsers interpret the same input in different ways. Differences in parsing behavior are difficult to detect due to (1) challenges in abstracting out the parser from complex code-bases and (2) proving the equivalence of parsers. Parser differentials remain understudied as they are a novel unexpected bug resulting from the interaction of software components—sometimes even independent modules—which may individually appear bug-free. We present a survey of many known parser differentials and conduct a root-cause analysis of them. We do so with an aim to uncover insights on how we can best conceptualize the underlying causes of their emergence. In studying these differentials, we have isolated certain design anti-patterns that give rise to parser differentials in software systems. We show how these differentials do not fit nicely into the state-of-the-art model of parser differentials and thus propose improvements to it.
Unsupervised clustering of file dialects according to monotonic decompositions of mixtures

This paper proposes an unsupervised classification method that partitions a set of files into non-overlapping dialects based upon their behaviors, determined by messages produced by a collection of programs that consume them. The pattern of messages can be used as the signature of a particular kind of behavior, with the understanding that some messages are likely to co-occur, while others are not. We propose a novel definition for a file format dialect, based upon these behavioral signatures. A dialect defines a subset of the possible messages, called the required messages. Once files are conditioned upon a dialect and its required messages, the remaining messages are statistically independent. With this definition in hand, we present a greedy algorithm that deduces candidate dialects from a dataset consisting of a matrix of file-message data, demonstrate its performance on several file formats, and prove conditions under which it is optimal. We show that an analyst needs to consider fewer dialects than distinct message patterns, which reduces their cognitive load when studying a complex format.
Toward Privacy-Preserving Localization and Mapping in eXtended Reality: A Privacy Threat Model

This study focuses on modelling privacy threats in the eXtended Reality (XR) domain with an emphasis on generic localization and mapping processes that run on or are offloaded by widely used equipment such as XR glasses. With the help of a privacy threat assessment framework, we model privacy threats that can affect the different XR processes and equipment. In addition to the threat analysis results, we provide a brief overview of threat mitigation techniques for each threat category. This strengthens privacy in XR processes and equipment and allows for privacy-preserving localization and mapping in XR scenario.
Generalizability of NLP-based Models for Modern Software Development Cross-Domain Environments

Natural Language Processing (NLP) has shown to be effective for solving complex problems in the Software Engineering (SE) domain, such as building chatbots and its ability to translate multi-languages. Despite the advances allowed by NLP, there are technical loopholes that hinder its fullest potential within the SE domain. The open problem remains in their generalizability for modern software development tasks that typically operate in a dynamic environment, such as AWS and SaaS platforms. The problem with these setups is that they may not contain labeled data. This poses a challenge when applying most prominent data-centric NLP models such as BERT transformer models. This position paper highlights some of the most pressing challenges drawn between the intersection of NLP and SE domains. Our vision revolves around improving the NLP model generalizability for dynamic cross-domain environments that contain little or no labeled target-domain data. We discuss these challenges and propose a research roadmap to tackle this problem as a research community emanating from SE lenses.
Fuzzing the Latest NTFS in Linux with Papora: An Empirical Study

Recently, the first feature-rich NTFS implementation, NTFS3, has been upstreamed to Linux. Although ensuring the security of NTFS3 is essential for the future of Linux, it remains unclear, however, whether the most recent version of NTFS for Linux contains 0-day vulnerabilities. To this end, we implemented Papora, the first effective fuzzer for NTFS3. We have identified and reported 3 CVE-assigned 0-day vulnerabilities and 9 severe bugs in NTFS3. Furthermore, we have investigated the underlying causes as well as types of these vulnerabilities and bugs. We have conducted an empirical study on the identified bugs while the results of our study have offered practical insights regarding the security of NTFS3 in Linux.
Predictive Resource Allocation for URLLC using Empirical Mode Decomposition

Effective resource allocation is a crucial requirement to achieve the stringent performance targets of ultra-reliable low-latency communication (URLLC) services. Predicting future interference and utilizing it to design efficient interference management algorithms is one way to allocate resources for URLLC services effectively. This paper proposes an empirical mode decomposition (EMD) based hybrid prediction method to predict the interference and allocate resources for downlink based on the prediction results. EMD is used to decompose the past interference values faced by the user equipment. Long short-term memory and auto-regressive integrated moving average methods are used to predict the decomposed components. The final predicted interference value is reconstructed using individual predicted values of decomposed components. It is found that such a decomposition-based prediction method reduces the root mean squared error of the prediction by 20 – 25%. The proposed resource allocation algorithm utilizing the EMD-based interference prediction was found to meet near-optimal allocation of resources and correspondingly results in 2 – 3 orders of magnitude lower outage compared to state-of-the-art baseline prediction algorithm-based resource allocation.
The ghost is the machine: Weird machines in transient execution

Microarchitectural attacks typically exploit some form of transient execution to steal sensitive data. More recently, though, a new class of attacks has used transient execution to (covertly) compute: Wampler et al. use Spectre primitives to obfuscate control flow, and Evtyushkin et al. construct “weird” logic gates that use Intel's TSX to compute entirely using microarchitectural side effects (i.e., in a cache side channel). This paper generalizes weird gate constructions beyond TSX and shows how to build such gates using any transient execution primitive. We build logic gates using exceptions, the branch predictor, and the branch target buffer, and we design a NOT gate that appears to perform roughly one order of magnitude <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The data in the original paper reports XOR execution speed and XOR executions per second that do not agree with one another. Taking the execution speed at face value, our construction is two orders of magnitude faster; instead, we calculate a faster execution speed for their reported executions per second, and our approach only yields an order of magnitude improvement. better than the prior state of the art. These constructions work on AMD, Intel, and ARM machines with ≈95-99% accuracy; a million AND gate executions take from half a second (when built with TSX) to four and a half seconds (when built with the branch target buffer). Our results indicate that weird gates are more generally applicable than previously known and may become more widely used, e.g., for malware obfuscation.
FLBooster: A Unified and Efficient Platform for Federated Learning Acceleration

Federated learning (FL) has emerged as a paradigm to train a global machine learning model in a distributed manner while taking privacy concerns and data protection regulations into consideration. Although a variety of FL algorithms have been proposed, the training efficiency of FL remains challenging due to massive mathematical computations and expensive client-server communication costs. However, existing FL-acceleration studies are limited as they can only solve the computation and communication overheads separately, which is suboptimal and constrains their acceleration ability. Moreover, previous studies are typically designed for specific FL scenarios and can support only one or two FL models, thus exhibiting poor generality.To fill these critical voids, we propose FLBooster, which provides unified and efficient acceleration capacity for a broad range of FL models. This is the first proposal to solve the computation and communication overheads simultaneously. Specifically, we utilize GPUs to boost the computation-intensive homomorphic encryption (HE) operations in a parallel manner, which significantly reduces the computation costs. On the other hand, a simple but efficient compression method is designed to lighten the exchange of data volumes between client and server. Extensive experiments using four standard FL models on three datasets show that FLBooster acquires superior speed-up gains (i.e., 14.3× – 138×) over state-of-the-art acceleration systems. Finally, we integrate FLBooster into the open-source FL benchmark FATE and offer user-friendly APIs for development.