Reviewerly/exHarmony huggingface dataset:
Bicoptor: Two-round Secure Three-party Non-linear Computation without Preprocessing for Privacy-preserving Machine Learning

The overhead of non-linear functions dominates the performance of the secure multiparty computation (MPC) based privacy-preserving machine learning (PPML). This work introduces a family of novel secure three-party computation (3PC) protocols, Bicoptor, which improve the efficiency of evaluating non-linear functions. The basis of Bicoptor is a new sign determination protocol, which relies on a clever use of the truncation protocol proposed in SecureML (S&P 2017). Our 3PC sign determination protocol only requires two communication rounds, and does not involve any preprocessing. Such sign determination protocol is well-suited for computing non-linear functions in PPML, e.g. the activation function ReLU, Maxpool, and their variants. We develop suitable protocols for these non-linear functions, which form a family of GPU-friendly protocols, Bicoptor. All Bicoptor protocols only require two communication rounds without preprocessing. We evaluate Bicoptor under a 3-party LAN network over a public cloud, and achieve more than 370,000 DReLU/ReLU or 41,000 Maxpool (find the maximum value of nine inputs) operations per second. Under the same settings and environment, our ReLU protocol has a one or even two orders of magnitude improvement to the state-of-the-art works, Falcon (PETS 2021) or Edabits (CRYPTO 2020), respectively without batch processing.
Breaking Security-Critical Voice Authentication

Voice authentication (VA) has recently become an integral part in numerous security-critical operations, such as bank transactions and call center conversations. The vulnerability of automatic speaker verification systems (ASVs) to spoofing attacks instigated the development of countermeasures (CMs), whose task is to differentiate between bonafide and spoofed speech. Together, ASVs and CMs form today’s VA systems and are being advertised as an impregnable access control mechanism. We develop the first practical attack on spoofing countermeasures, and demonstrate how a malicious actor may efficiently craft audio samples against these defenses. Previous adversarial attacks against VA have been mainly designed for the whitebox scenario, which assumes knowledge of the system’s internals, or requires large query and time budgets to launch target-specific attacks. When attacking a security-critical system, these assumptions do not hold. Our attack, on the other hand, targets common points of failure that all spoofing countermeasures share, making it real-time, model-agnostic, and completely blackbox without the need to interact with the target to craft the attack samples. The key message from our work is that CMs mistakenly learn to distinguish between spoofed and bonafide audio based on cues that are easily identifiable and forgeable. The effects of our attack are subtle enough to guarantee that these adversarial samples can still bypass the ASV as well and preserve their original textual contents. These properties combined make for a powerful attack that can bypass security-critical VA in its strictest form, yielding success rates of up to 99% with only 6 attempts. Finally, we perform the first targeted, over-telephony-network attack on CMs, bypassing several known challenges and enabling a variety of potential threats, given the increased use of voice biometrics in call centers. Our results call into question the security of modern VA systems and urge users to rethink their trust in them, in light of the real threat of attackers bypassing these measures to gain access to their most valuable resources.
D-ARM: Disassembling ARM Binaries by Lightweight Superset Instruction Interpretation and Graph Modeling

ARM binary analysis has a wide range of applications in ARM system security. A fundamental challenge is ARM disassembly. ARM, particularly AArch32, has a number of unique features making disassembly distinct from x86 disassembly, such as the mixing of ARM and Thumb instruction modes, implicit mode switching within an application, and more prevalent use of inlined data. Existing techniques cannot achieve high accuracy when binaries become complex and have undergone obfuscation. We propose a novel ARM binary disassembly technique that is particularly designed to address challenges in legacy code for 32-bit ARM binaries. It features a lightweight superset instruction interpretation method to derive rich semantic information and a graph-theory based method that aggregates such information to produce final results. Our comparative evaluation with a number of state-of-the-art disassemblers, including Ghidra, IDA, P-Disasm, XDA, D-Disasm, and Spedi, on thousands of binaries generated from SPEC2000 and SPEC2006 with various settings, and real-world applications collected online show that our technique D-ARM substantially outperforms the baselines.
Robust Multi-tab Website Fingerprinting Attacks in the Wild

Website fingerprinting enables an eavesdropper to determine which websites a user is visiting over an encrypted connection. State-of-the-art website fingerprinting (WF) attacks have demonstrated effectiveness even against Tor-protected network traffic. However, existing WF attacks have critical limitations on accurately identifying websites in multi-tab browsing sessions, where the holistic pattern of individual websites is no longer preserved, and the number of tabs opened by a client is unknown a priori. In this paper, we propose ARES, a novel WF framework natively designed for multi-tab WF attacks. ARES formulates the multi-tab attack as a multi-label classification problem and solves it using a multi-classifier framework. Each classifier, designed based on a novel transformer model, identifies a specific website using its local patterns extracted from multiple traffic segments. We implement a prototype of ARES and extensively evaluate its effectiveness using our large-scale dataset collected over multiple months (by far the largest multi-tab WF dataset studied in academic papers.) The experimental results illustrate that ARES effectively achieves the multi-tab WF attack with the best F1-score of 0.907. Further, ARES remains robust even against various WF defenses.
"We are a startup to the core": A qualitative interview study on the security and privacy development practices in Turkish software startups

Security and privacy are often neglected in software development, and rarely a priority for developers. This insight is commonly based on research conducted by researchers and on developer populations living and working in the United States, Europe, and the United Kingdom. However, the production of software is global, and crucial populations in important technology hubs are not adequately studied. The software startup scene in Turkey is impactful, and comprehension, knowledge, and mitigations related to software security and privacy remain understudied. To close this research gap, we conducted a semi-structured interview study with 16 developers working in Turkish software startups. The goal of the interview study was to analyze if and how developers ensure that their software is secure and preserves user privacy. Our main finding is that developers rarely prioritize security and privacy, due to a lack of awareness, skills, and resources. We find that regulations can make a positive impact on security and privacy. Based on the study, we issue recommendations for industry, individual developers, research, educators, and regulators. Our recommendations can inform a more globalized approach to security and privacy in software development.
SecureCells: A Secure Compartmentalized Architecture

Modern programs are monolithic, combining code of varied provenance without isolation, all the while running on network-connected devices. A vulnerability in any component may compromise code and data of all other components. Compartmentalization separates programs into fault domains with limited policy-defined permissions, following the Principle of Least Privilege, preventing arbitrary interactions between components. Unfortunately, existing compartmentalization mechanisms target weak attacker models, incur high overheads, or overfit to specific use cases, precluding their general adoption. The need of the hour is a secure, performant, and flexible mechanism on which developers can reliably implement an arsenal of compartmentalized software.We present SecureCells, a novel architecture for intra-address space compartmentalization. SecureCells enforces per-Virtual Memory Area (VMA) permissions for secure and scalable access control, and introduces new userspace instructions for secure and fast compartment switching with hardware-enforced call gates and zero-copy permission transfers. SecureCells enables novel software mechanisms for call stack maintenance and register context isolation. In microbenchmarks, SecureCells switches compartments in only 8 cycles on a 5-stage in-order processor, reducing cost by an order of magnitude compared to state-of-the-art. Consequently, SecureCells helps secure high-performance software such as an in-memory key-value store with negligible overhead of less than 3%.
Improving Developers’ Understanding of Regex Denial of Service Tools through Anti-Patterns and Fix Strategies

Regular expressions are used for diverse purposes, including input validation and firewalls. Unfortunately, they can also lead to a security vulnerability called ReDoS (Regular Expression Denial of Service), caused by a super-linear worst-case execution time during regex matching. Due to the severity and prevalence of ReDoS, past work proposed automatic tools to detect and fix regexes. Although these tools were evaluated in automatic experiments, their usability has not yet been studied; usability has not been a focus of prior work. Our insight is that the usability of existing tools to detect and fix regexes will improve if we complement them with anti-patterns and fix strategies of vulnerable regexes.We developed novel anti-patterns for vulnerable regexes, and a collection of fix strategies to fix them. We derived our anti-patterns and fix strategies from a novel theory of regex infinite ambiguity — a necessary condition for regexes vulnerable to ReDoS. We proved the soundness and completeness of our theory. We evaluated the effectiveness of our anti-patterns, both in an automatic experiment and when applied manually. Then, we evaluated how much our anti-patterns and fix strategies improve developers’ understanding of the outcome of detection and fixing tools. Our evaluation found that our anti-patterns were effective over a large dataset of regexes (N=209,188): 100% precision and 99% recall, improving the state of the art 50% precision and 87% recall. Our anti-patterns were also more effective than the state of the art when applied manually (N=20): 100% developers applied them effectively vs. 50% for the state of the art. Finally, our anti-patterns and fix strategies increased developers’ understanding using automatic tools (N=9): from median "Very weakly" to median "Strongly" when detecting vulnerabilities, and from median "Very weakly" to median "Very strongly" when fixing them.
On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks

While the literature on security attacks and defenses of Machine Learning (ML) systems mostly focuses on unrealistic adversarial examples, recent research has raised concern about the under-explored field of realistic adversarial attacks and their implications on the robustness of real-world systems. Our paper paves the way for a better understanding of adversarial robustness against realistic attacks and makes two major contributions. First, we conduct a study on three real-world use cases (text classification, botnet detection, malware detection) and seven datasets in order to evaluate whether unrealistic adversarial examples can be used to protect models against realistic examples. Our results reveal discrepancies across the use cases, where unrealistic examples can either be as effective as the realistic ones or may offer only limited improvement. Second, to explain these results, we analyze the latent representation of the adversarial examples generated with realistic and unrealistic attacks. We shed light on the patterns that discriminate which unrealistic examples can be used for effective hardening. We release our code, datasets and models to support future research in exploring how to reduce the gap between unrealistic and realistic adversarial attacks.
Everybody’s Got ML, Tell Me What Else You Have: Practitioners’ Perception of ML-Based Security Tools and Explanations

Significant efforts have been investigated to develop machine learning (ML) based tools to support security operations. However, they still face key challenges in practice. A generally perceived weakness of machine learning is the lack of explanation, which motivates researchers to develop machine learning explanation techniques. However, it is not yet well understood how security practitioners perceive the benefits and pain points of machine learning and corresponding explanation methods in the context of security operations. To fill this gap and understand "what is needed", we conducted semi-structured interviews with 18 security practitioners with diverse roles, duties, and expertise. We find practitioners generally believe that ML tools should be used in conjunction with (instead of replacing) traditional rule-based methods. While ML’s output is perceived as difficult to reason, surprisingly, rule-based methods are not strictly easier to interpret. We also find that only few practitioners considered security (robustness to adversarial attacks) as a key factor for the choice of tools. Regarding ML explanations, while recognizing their values in model verification and understanding security events, practitioners also identify gaps between existing explanation methods and the needs of their downstream tasks. We collect and synthesize the suggestions from practitioners regarding explanation scheme designs, and discuss how future work can help to address these needs.
Effective ReDoS Detection by Principled Vulnerability Modeling and Exploit Generation

Regular expression Denial-of-Service (ReDoS) is one kind of algorithmic complexity attack. For a vulnerable regex, attackers can craft certain strings to trigger the super-linear worst-case matching time, which causes denial-of-service to regex engines. Various ReDoS detection approaches have been proposed recently. Among them, hybrid approaches which absorb the advantages of both static and dynamic approaches have shown their performance superiority. However, two key challenges still hinder the effectiveness of the detection: 1) Existing modelings summarize localized vulnerability patterns based on partial features of the vulnerable regex; 2) Existing attack string generation strategies are ineffective since they neglected the fact that non-vulnerable parts of the regex may unexpectedly invalidate the attack string (we name this kind of invalidation as disturbance.)Rengar is our hybrid ReDoS detector with new vulnerability modeling and disturbance free attack string generator. It has the following key features: 1) Benefited by summarizing patterns from full features of the vulnerable regex, its modeling is a more precise interpretation of the root cause of ReDoS vulnerability. The modeling is more descriptive and precise than the union of existing modelings while keeping conciseness; 2) For each vulnerable regex, its generator automatically checks all potential disturbances and composes generation constraints to avoid possible disturbances.Compared with nine state-of-the-art tools, Rengar detects not only all vulnerable regexes they found but also 3 – 197 times more vulnerable regexes. Besides, it saves 57.41% – 99.83% average detection time compared with tools containing a dynamic validation process. Using Rengar, we have identified 69 zero-day vulnerabilities (21 CVEs) affecting popular projects which have more than dozens of millions weekly download count.
A Product based Recommendation System for E-Commerce Sites

Recommendation system help the customer to find best product for them. Basically recommendation system is the tools that are re-shaping the word of E-Commerce sites. It will help to people that they can find the efficient product without any constraint. In this paper we will discuss about how to recommendation system will work and also we will analyze the algorithm. The products are recommended based on the user viewing time, product rating and many more information which we will get from the client. It is basically recommendation system which is show the better product to the customer. In this paper we will review various techniques which is help us to recommend the product in very efficient way.
Side Eye: Characterizing the Limits of POV Acoustic Eavesdropping from Smartphone Cameras with Rolling Shutters and Movable Lenses

Our research discovers how the rolling shutter and movable lens structures widely found in smartphone cameras modulate structure-borne sounds onto camera images, creating a point-of-view (POV) optical-acoustic side channel for acoustic eavesdropping. The movement of smartphone camera hardware leaks acoustic information because images unwittingly modulate ambient sound as imperceptible distortions. Our experiments find that the side channel is further amplified by intrinsic behaviors of Complementary Metal-oxide–Semiconductor (CMOS) rolling shutters and movable lenses such as in Optical Image Stabilization (OIS) and Auto Focus (AF). Our paper characterizes the limits of acoustic information leakage caused by structure-borne sound that perturbs the POV of smartphone cameras. In contrast with traditional optical-acoustic eavesdropping on vibrating objects, this side channel requires no line of sight and no object within the camera’s field of view (images of a ceiling suffice). Our experiments test the limits of this side channel with a novel signal processing pipeline that extracts and recognizes the leaked acoustic information. Our evaluation with 10 smartphones on a spoken digit dataset reports 80.66%, 91.28%, and 99.67% accuracies on recognizing 10 spoken digits, 20 speakers, and 2 genders respectively. We further systematically discuss the possible defense strategies and implementations. By modeling, measuring, and demonstrating the limits of acoustic eavesdropping from smartphone camera image streams, our contributions explain the physics-based causality and possible ways to reduce the threat on current and future devices.
StyleFool: Fooling Video Classification Systems via Style Transfer

Video classification systems are vulnerable to adversarial attacks, which can create severe security problems in video verification. Current black-box attacks need a large number of queries to succeed, resulting in high computational overhead in the process of attack. On the other hand, attacks with restricted perturbations are ineffective against defenses such as denoising or adversarial training. In this paper, we focus on unrestricted perturbations and propose StyleFool, a black-box video adversarial attack via style transfer to fool the video classification system. StyleFool first utilizes color theme proximity to select the best style image, which helps avoid unnatural details in the stylized videos. Meanwhile, the target class confidence is additionally considered in targeted attacks to influence the output distribution of the classifier by moving the stylized video closer to or even across the decision boundary. A gradient-free method is then employed to further optimize the adversarial perturbations. We carry out extensive experiments to evaluate StyleFool on two standard datasets, UCF-101 and HMDB-51. The experimental results demonstrate that StyleFool outperforms the state-of-the-art adversarial attacks in terms of both the number of queries and the robustness against existing defenses. Moreover, 50% of the stylized videos in untargeted attacks do not need any query since they can already fool the video classification model. Furthermore, we evaluate the indistinguishability through a user study to show that the adversarial samples of StyleFool look imperceptible to human eyes, despite unrestricted perturbations.
Inducing Wireless Chargers to Voice Out for Inaudible Command Attacks

Recent works demonstrated that speech recognition systems or voice assistants can be manipulated by malicious voice commands, which are injected through various inaudible media, such as ultrasound, laser, and electromagnetic interference (EMI). In this work, we explore a new kind of inaudible voice attack through the magnetic interference induced by a wireless charger. Essentially, we show that the microphone components of smart devices suffer from severe magnetic interference when they are enjoying wireless charging, due to the absence of effective protection against the EMI at low frequencies (100 kHz or below). By taking advantage of this vulnerability, we design two inaudible voice attacks, HeartwormAttack and ParasiteAttack, both of which aim to inject malicious voice commands into smart devices being wirelessly charged. They make use of a compromised wireless charger or accessory equipment (called parasite) to inject the voice, respectively. We conduct extensive experiments with 17 victim devices (iPhone, Huawei, Samsung, etc.) and 6 types of voice assistants (Siri, Google STT, Bixby, etc.). Evaluation results demonstrate the feasibility of two proposed attacks with commercial charging settings.
Optically Detected Finger Recognition-Based Virtual Keyboard

AI Virtual Keyboard is a model that imitates the standard keyboard. This project of virtualizing the hardware keyboard with the help of Computer Vision and the Hand Detecting module allows users to type with hand gestures and modify different layouts of the keyboard. AI virtual keyboard is a computer-based keyboard that works by typing on the optically detectable area which can be used to remove the use of a physical keyboard because a physical keyboard requires many resources and it's restricted by its physical features.
Exploratory Testing of Software Product Linesusing Distance Metrics

Quality is very important in any software product, so that the customer trusts the efficiency of the product. The quality or validity of a product is measured by its testing. The test measures the quality of a product as well as shows the validity of the product. For example, testing and measuring the validity of a product in a single software system is not easy. Similarly, the software product line, which produces many different products of the same family at once, makes it even more difficult to check their validity and quality. Several techniques and models have been proposed to test the validity of the software line products and to ascertain the quality. In the same direction, we also propose a method for testing the products of software product line. The proposed method isbased on the distance between the features of the product (Desired and Original). We have used the proposed technique on product line with less features and found that the method presented gives fast and better results than traditional methods.