Miracle-dz/UniFAR-FTUs huggingface dataset:
SMS OTP Security (SOS): Hardening SMS-Based Two Factor Authentication

SMS-based two-factor authentication (2FA) is the most widely deployed 2FA mechanism, despite the fact that SMS messages are known to be vulnerable to rerouting attacks, and despite the availability of alternatives that may be more secure. This is for two reasons. First, it is very effective in practice, as evidenced by reports from Google and Microsoft. Second, users prefer SMS over alternatives, because text messaging is already part of their daily communication. Accepting this practical reality, we developed a new SMS-based protocol that makes rerouting attacks useless to adversaries who aim to take over user accounts. Our protocol delivers one-time passwords (OTP) via text message in a manner that adds minimal overhead (to both the user and the server) over existing SMS-based methods, and is implemented with only small changes to the stock text-message applications that already ship on mobile phones. The security of our protocol rests upon a provably secure authenticated key exchange protocol that, crucially, does not place significant new burdens upon the user. Indeed, we carry out a user study that demonstrates no statistically significant difference between traditional SMS and our protocol, in terms of usability.
Adaptive Malware Control: Decision-Based Attacks in the Problem Space of Dynamic Analysis

Adversarial malware have been widely explored, most often on static analysis based detection and feature space manipulations. With the prevalence of encryption, obfuscation, and packing, dynamic behavior is considered much more revealing of a program's nature. At the same time, defining and performing attacks through the feature representation of malware faces several obstacles, especially in dynamic analysis. However, if program behavior is both malleable and indicative of malicious intent, we concern ourselves with the question of how it can be adaptively controlled in order to evade detection. In this work, we redefine adversarial attacks on malware behavior so that they can be performed directly by the original binary and thus obviate the need to compute gradients through feature representations. We theoretically prove that this can occur even in the fully black-box case where only the final, hard-label decision is disclosed. Furthermore, we empirically evaluate our approach by training state-of-the-art sequence models for detecting malware behavior, constructing several malware manipulation environments, and training a host of reinforcement learning (RL) agents on them that learn evasive policies through interaction. Finally, we utilize the adversarial behavior learned by the RL agents to adversarially train the original detection models and we show that while an indispensable approach, the degree of robustness it imparts can be deceptive; especially when we consider adversaries with broader action sets.
Online Shopping Behavior during Pandemic

Many industries have been impacted, and some have been force to shut down as a result of the pandemic outbreak, . Meanwhile, e-commerce has survived and thrived. Since then, online shopping grown in popularity. Consumers are encouraged to stay at home and order their daily necessities online to reduce the spread of the virus. This paper aims to revisit online shopping behaviour by investigating financial risk, convenience risk, privacy risk, delivery risk, environmental risk, and information trust towards shopping behaviour. We collected a total of 250 questionnaires during the lockdown period in the year 2020. Findings shown that participants agree that information trust, privacy risk, convenient risk, and delivery risk can influence their online shopping behaviour, but the only not is environmental risk.
Enabling Attribute-Based Access Control in Linux Kernel

Linux has built-in security features based on discretionary access control that can be enhanced using the Linux Security Module (LSM) framework. However, so far there has been no reported work on strengthening Linux with Attribute-Based Access Control (ABAC), which is gaining in popularity in recent years due to its flexibility and dynamic nature. In this paper, a method for enabling ABAC for Linux file system objects using LSM is proposed. We report initial experimental results and also share our public repository links for integrating ABAC in any Linux installation.
SoK: Exploring Current and Future Research Directions on XS-Leaks through an Extended Formal Model

A web visit typically consists of the browser rendering a dynamically generated response that is specifically tailored to the user. This generation of responses based on the currently authenticated user, whose authentication credentials are automatically included via cookies in all (including cross-site) requests, have led to a multitude of issues. Through cross-site leaks (XS-Leaks), an adversary can try to circumvent the same-origin policy and extract information about responses, which in turn can reveal potentially sensitive information about the user. As research on this class of vulnerabilities only recently gained traction, and the attacks affect many different components of the web platform, the intrinsic characteristics and underlying causes remain largely unexplored. In this paper we present an abstraction of XS-Leaks attacks and introduce an extended formal model that we use to reason about the cause of different leaks and which strategies the various defense mechanisms employ to defend against them. Furthermore, we provide a classification method for current attacks, and, guided by our model, propose a methodology to comprehensively detect new XS-Leak issues, or indicate their absence. Furthermore, we analyze the current defenses and identify gaps that still require further research to provide extensive solutions for sites that rely on cross-site interactions. Finally, we explore how XS-Leak defenses are currently deployed and which challenges website owners are still facing. As a first step towards facilitating the deployment of XS-Leak defenses, we introduce LeakBuster, a dynamic web interface that provides web developers with suggestions based on the insights provided throughout this paper.
Transformers for End-to-End InfoSec Tasks: A Feasibility Study

Training a machine learning (ML) model from raw information security (InfoSec) data involves utilizing distinct data types and input formats that require unique considerations compared to more conventional applications of ML like natural language processing (NLP) and computer vision (CV). In this paper, we assess the viability of transformer models in end-to-end InfoSec settings, in which no intermediate feature representations or processing steps occur outside the model. We implement transformer models for two distinct InfoSec data formats - specifically URLs and PE files - in a novel end-to-end approach, and explore a variety of architectural designs, training regimes, and experimental settings to determine the ingredients necessary for performant detection models. We show that in contrast to conventional transformers trained on more standard NLP-related tasks, our URL transformer model requires a different training approach to reach high performance levels. Specifically, we show that 1) pre-training on a massive corpus of unlabeled URL data for an auto-regressive task does not readily transfer to binary classification of malicious or benign URLs, but 2) that using an auxiliary auto-regressive loss improves performance when training from scratch. We introduce a method for mixed objective optimization, which dynamically balances contributions from both loss terms so that neither one of them dominates. We show that this method yields quantitative evaluation metrics comparable to that of several top-performing benchmark classifiers. Unlike URLs, binary executables contain longer and more distributed sequences of information-rich bytes. To accommodate such lengthy byte sequences, we introduce additional context length into the transformer by providing its self-attention layers with an adaptive span similar to Sukhbaatar et al. We demonstrate that this approach performs comparably to well-established malware detection models on benchmark PE file datasets, but also point out the need for further exploration into model improvements in scalability and compute efficiency.
Multivariate Time Series Imputation Based on Residual GRU and AANN

The existence of missing values brings inconvenience to data mining. In this paper, we propose a residual gated recurrent unit (GRU) and auto-associative neural network (AANN) based imputation method to impute missing values of multivariate time series. Instead of only utilizing GRU to learn the dynamic law and estimate the missing values of time series, AANN is employed to improve the estimation accuracy. By using AANN, observed values of current time step can be applied to the estimation of missing values, which makes available information can be fully utilized. Moreover, outputs of adjacent recurrent units are connected to form temporal residual network to promote the learning ability of the entire model. The experiments on several datasets validate the effectiveness of proposed method for incomplete multivariate time series imputation.
WDC'22: 1st Workshop on the Security Implications of Deepfakes and Cheapfakes

The development of techniques for creating completely synthetic photographic images and videos, as well as the increasing prevalence of disinformation associated with such synthetic media, have sparked interest in the computer community. One such issue is the proliferation of deepfakes. However, much of the research is devoted to outwitting the SOTA in order to generate and detect fabricated images and video. Considering them from the perspective of computer security and human ethics is largely ignored. As such, this workshop aims to provide a forum for researchers to exchange ideas and methodologies, as well as to provide academia and industry with insights into the development and identification of fake media from a computer security perspective.
DyGraph: a dynamic graph generator and benchmark suite

Dynamic graph processing, execution on vertex-edge graphs that change over time, is quickly becoming a key computing need of the twenty-first century. Dynamic graph algorithms unlock real-time optimization solutions and a wide range of data-mining applications in logistics, finance, marketing, healthcare, and social media, among many others. However, graph algorithms are extremely memory-bound (i.e., their performance is limited by the bandwidth of memory accesses on the underlying hardware platform, rather than the compute capacity). Moreover, dynamic graph algorithms are being applied to increasingly-large datasets, further straining the memory systems and reducing performance. As a result, additional research is needed to leverage new memory technologies for faster, more efficient, dynamic graph-based processing. Such research is difficult without access to hitherto unavailable industrial-scale dynamic graph datasets to evaluate solutions. In this work, we present DyGraph, a dynamic graph synthetic dataset generator paired with a collection of real-world graphs in the domains of social media, recommendation systems, and fintech. We demonstrate the breadth of graph features represented in this repository and evaluate the DyGraph Generator's ability to generate synthetic graphs that mimic these real datasets. In our case study, we find that the degree distribution of DyGraph Generator datasets correlate 3 to 5.5 times more closely to real-world datasets than Power Law models, paving the way for much-needed research for high-performance dynamic graph processing.
A Human Factors Approach to Validating Driver Models for Interaction-aware Automated Vehicles

A major challenge for autonomous vehicles is interacting with other traffic participants safely and smoothly. A promising approach to handle such traffic interactions is equipping autonomous vehicles with interaction-aware controllers (IACs). These controllers predict how surrounding human drivers will respond to the autonomous vehicle’s actions, based on a driver model. However, the predictive validity of driver models used in IACs is rarely validated, which can limit the interactive capabilities of IACs outside the simple simulated environments in which they are demonstrated. In this article, we argue that besides evaluating the interactive capabilities of IACs, their underlying driver models should be validated on natural human driving behavior. We propose a workflow for this validation that includes scenario-based data extraction and a two-stage (tactical/operational) evaluation procedure based on human factors literature. We demonstrate this workflow in a case study on an inverse-reinforcement-learning-based driver model replicated from an existing IAC. This model only showed the correct tactical behavior in 40% of the predictions. The model’s operational behavior was inconsistent with observed human behavior. The case study illustrates that a principled evaluation workflow is useful and needed. We believe that our workflow will support the development of appropriate driver models for future automated vehicles.
Efficient approximate search for sets of lineage vectors

One can approximate the lineage of a Database (DB) tuple using a small set of low dimensional vectors. To identify actual lineage tuples using these vector sets, given a set of vectors (of the target tuple), one needs to locate "close" sets of vectors associated with the lineage tuples. We first consider a similarity measure between two sets A and B of vectors, that balances the average and maximum cosine distance between pairs of vectors, one from set A and one from set B. The proposed similarity measure is intuitive and permutation invariant. To practically realize this measure, we need an approximate search algorithm that given a set of vectors A and sets of vectors B1, . , Bn, the algorithm quickly locates the k-closest sets Bi1 . , Bik that maximize the similarity measure. For the case where all sets are singleton sets, essentially each is a single vector, there are known efficient approximate search algorithms, e.g., approximated versions of tree search algorithms, locality-sensitive hashing (LSH), vector quantization (VQ) and proximity graph algorithms. We utilize the mathematical properties of the cosine distance measure to transform the set-set search problem into a vector-vector search problem. However, this abovementioned transformation cannot handle the Euclidean-based version of the similarity measure. For this version, we devise a more elaborate transformation. For this latter transformation, we present algorithms for the general case, with sets of differing cardinalities. The underlying idea in both of these transformations is encoding a set of vectors A via |A| "long" independent representative vectors. Then, we are able to transform the set-set search problem into the well-studied approximate (ordinary) vector search problem. For both cosine-based and Euclidean-based similarity measures, the proposed approximate search achieves significant performance gains over an optimized, exact search on vector sets.
Understanding the Security of Free Content Websites by Analyzing their SSL Certificates: A Comparative Study

Online services that provide books, music, movies, etc., for free have existed on the Internet for decades. While there are some common beliefs and warnings that such online services may contain hidden security risks, many ordinary users still visit such websites, making them a convenient vehicle for subsequent exploitation. In this paper, we investigate and quantify through measurements the potential vulnerability of such free content websites (FCWs). For this purpose, we curated 834 FCWs offering books, games, movies, music, and software. For a comparison purpose, we also sampled a comparable number of premium content websites, where users need to pay for using the service for the same type of content. For our modality of analysis, we use SSL certificates. Namely, we explore SSL certificates' structural and fundamental differences between free and premium content websites. Through our analysis, we unveil that 36% of the free websites' certificates have major issues, with 17% invalid certificates, 7% expired, and 12% with mismatched domain names. Moreover, although surprisingly, we uncover the usage of ECDSA predominantly among the free websites. Among other observations, we notice that 38% of the FCWs use ECDSA-256, compared to only 20% of their premium counterparts, which provides better security guarantees (and performance) than the common algorithm option and key size (RSA-2048) in premium websites. Our observations raise concerns regarding the safety of using such free services from a transport standpoint and call for in-depth analysis of their risks.
Introduction to the special issue on big data analytics with internet of things‐oriented infrastructures for future smart cities

A smart city refers to a city equipped with the basic infrastructure to provide a good quality of life and a clean and sustainable environment to its citizens using smart technology-based solutions. It is a smart way to provide the best services to its residents and develop the infrastructure. The smart city focuses on controlling available resources safely, sustainably, and efficiently to improve the economy and societal outcomes. People, systems, and things in the cities generate data. However, their heterogeneity makes it difficult to publish, organize, discover, interpret, combine, analyse, and consume them. The next generation of these technologies, including the 6G and intelligent internet of things (6G/IIoT), have been recently proposed aiming to provide endless networking capabilities to the city users. General estimates revealed that the number of smart IoT devices would approach over 50 billion by 2022. With the proliferation of smart IoT devices, smart applications are expected to lead to further innovation in 6G/IIoT-oriented cities. This special issue aims to stimulate discussion on the IoT and Big Data analytics for future smart cities with smart applications including smart health, smart governance, smart homes, and smart buildings, smart mobility and transportation, smart factories, and smart data-driven decision-making.
Single molecule multi-omics reveals context-dependent regulation of enhancers by DNA methylation

Enhancers are cis-regulatory elements that control the establishment of cell identities during development. In mammals, enhancer activation is tightly coupled with local DNA demethylation. Yet, whether this epigenetic remodelling is necessary for enhancer activation is unknown. Here, we developed a single molecule multi-omics approach to measure chromatin accessibility and transcription factor binding as a function of the presence of methylation on the same DNA molecules. We leveraged natural epigenetic heterogeneity at active enhancers to test the impact of DNA methylation on their activity in multiple cell lineages. While reduction of DNA methylation appears dispensable for the activity of most enhancers, we identify a class of cell-type-specific enhancers where presence of DNA methylation antagonises the binding of transcription factors. Genetic perturbations reveal that chromatin accessibility and transcription factor binding are a direct function of active demethylation at these loci. Thus, in addition to safeguarding the genome from spurious activation, DNA methylation directly controls transcription factor occupancy at cell-type-specific active enhancers.
ZDHHC5 targets Protocadherin 7 to the cell surface by a palmitoylation-dependent mechanism to promote successful cytokinesis

Cell division requires dramatic reorganization of the cell cortex that is primarily driven by the actomyosin network. We previously reported that Protocadherin 7 (PCDH7) enriches at the cell surface during mitosis which is required for building up the full mitotic rounding pressure. Here we showed that PCDH7 gets palmitoylated and interacts with the palmitoyltransferase, ZDHHC5. Both PCDH7 and ZDHHC5 co-localize at the mitotic cell surface, and they translocate to the cleavage furrow during cytokinesis. PCDH7’s localization depends on palmitoylation activity of ZDHHC5. Loss of expression of PCDH7 impairs active RhoA and phospho-myosin levels at the cleavage furrow and increases the rate of multinucleated cells. This work uncovers a palmitoylation-dependent translocation mechanism for PCDH7 and attributes a regulatory role to contributing actomyosin activity during cytokinesis.
Self-generation of goal-directed choices in a distributed dopaminergic and prefrontal circuit

Goal-directed choices that are not triggered by external cues arise from internal representations of the outcomes. The use of a stimulus to specify when to act, which option to take, or whether to explore, has led to consider the reward circuit as a feedforward set of modules carrying independent computations. Here, we develop an uncued task in which mice self-determine the initiation, direction, vigor and pace of their actions based on their knowledge of the outcomes. Using electrophysiological recordings, pharmacology and optogenetics, we identify a sequence of oscillations and firing in the ventral tegmental area (VTA), orbitofrontal (OFC) and prefrontal cortices (PFC) that co-encodes and co-determines self-initiation and choices. This sequence appeared with learning as an unguided realignment of spontaneous dynamics. The interactions between the structures depended on the reward context, in particular regarding the uncertainty associated with the different options. We suggest that self-generated choices arise from a distributed circuit based on an OFC-VTA core setting whether to wait or to initiate actions, while the PFC is specifically engaged by reward uncertainty to participate in both the selection and pace of actions. Highlights Self-paced actions arise from contextual reorganization of mesocortical dynamics. VTA, PFC and OFC complementarily encode predictions and errors about outcomes. Distributed firing-then-oscillations dynamics set the goal, initiation and pace of actions. VTA and PFC antagonistically promote and inhibit motivation by reward uncertainty.
Nucleoli and the nucleoli–centromere association are dynamic during normal development and in cancer

Centromeres are known to cluster around nucleoli in drosophila and mammalian cells. However, the functional significance of nucleoli-centromere interaction remains underexplored. We hypothesize that if this conserved interaction is functionally important, it should be dynamic under different physiological and pathological conditions. We examined the nucleolar structure and centromeres at various differentiation stages using cell culture models. The results show dynamic changes of nucleolar number, area, and nucleoli-centromere interactions at differentiation stages and in cancer cells. Embryonic stem cells usually have a single large nucleolus, which associates with a high percentage of centromeres. As cells differentiate into intermediate states, the nucleolar number increases and the association with centromeres decreases. In terminally differentiated cells, including myotubes, neurons and keratinocytes, the number of nucleoli and their association with centromeres are at the lowest. Cancer cells demonstrate the pattern of nucleoli number and nucleoli-centromere association that is akin to proliferative less differentiated cell types, suggesting that nucleolar reorganization and changes in nucleoli-centromere interactions may help facilitate malignant transformation. This idea is supported in a case of pediatric rhabdomyosarcoma, in which induced differentiation inhibits cell proliferation and reduces nucleolar number and centromere association. These findings suggest active roles of nucleolar structure in centromere function and genome organization critical for cellular function in both normal development and cancer.