arXiv subjects

Fu Song

Publications and source records attributed to Fu Song.

44 records · Page 3Linked to original sources

arXiv2020

Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain

Numerous methods for crafting adversarial examples were proposed recently with high success rate. Since most existing machine learning based classifiers normalize images into some continuous, real vector, domain firstly, attacks often craft adversarial examples in such domain. However, "adversarial" examples may become benign after denormalizing them back into the discrete integer domain, known as the discretization problem. This problem was mentioned in some work, but has received relatively little attention. In this work, we first conduct a comprehensive study of existing methods and tools for crafting. We theoretically analyze 34 representative methods and empirically study 20 representative open source tools for crafting adversarial images. Our study reveals that the discretization problem is far more serious than originally thought. This suggests that the discretization problem should be taken into account seriously when crafting adversarial examples and measuring attack success rate. As a first step towards addressing this problem in black-box scenario, we propose a black-box method which reduces the adversarial example searching problem to a derivative-free optimization problem. Our method is able to craft adversarial images by derivative-free search in the discrete integer domain. Experimental results show that our method is comparable to recent white-box methods (e.g., FGSM, BIM and C\&W) and achieves significantly higher success rate in terms of adversarial examples in the discrete integer domain than recent black-box methods (e.g., ZOO, NES-PGD and Bandits). Moreover, our method is able to handle models that is non-differentiable and successfully break the winner of NIPS 2017 competition on defense with 95\% success rate. Our results suggest that discrete optimization algorithms open up a promising area of research into effective black-box attacks.

cs.LG↗

arXiv2020

Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems

Speaker recognition (SR) is widely used in our daily life as a biometric authentication or identification mechanism. The popularity of SR brings in serious security concerns, as demonstrated by recent adversarial attacks. However, the impacts of such threats in the practical black-box setting are still open, since current attacks consider the white-box setting only. In this paper, we conduct the first comprehensive and systematic study of the adversarial attacks on SR systems (SRSs) to understand their security weakness in the practical blackbox setting. For this purpose, we propose an adversarial attack, named FAKEBOB, to craft adversarial samples. Specifically, we formulate the adversarial sample generation as an optimization problem, incorporated with the confidence of adversarial samples and maximal distortion to balance between the strength and imperceptibility of adversarial voices. One key contribution is to propose a novel algorithm to estimate the score threshold, a feature in SRSs, and use it in the optimization problem to solve the optimization problem. We demonstrate that FAKEBOB achieves 99% targeted attack success rate on both open-source and commercial systems. We further demonstrate that FAKEBOB is also effective on both open-source and commercial systems when playing over the air in the physical world. Moreover, we have conducted a human study which reveals that it is hard for human to differentiate the speakers of the original and adversarial voices. Last but not least, we show that four promising defense methods for adversarial attack from the speech recognition domain become ineffective on SRSs against FAKEBOB, which calls for more effective defense methods. We highlight that our study peeks into the security implications of adversarial attacks on SRSs, and realistically fosters to improve the security robustness of SRSs.

eess.AS↗

arXiv2020

Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers

Machine learning (ML) based approaches have been the mainstream solution for anti-phishing detection. When they are deployed on the client-side, ML-based classifiers are vulnerable to evasion attacks. However, such potential threats have received relatively little attention because existing attacks destruct the functionalities or appearance of webpages and are conducted in the white-box scenario, making it less practical. Consequently, it becomes imperative to understand whether it is possible to launch evasion attacks with limited knowledge of the classifier, while preserving the functionalities and appearance. In this work, we show that even in the grey-, and black-box scenarios, evasion attacks are not only effective on practical ML-based classifiers, but can also be efficiently launched without destructing the functionalities and appearance. For this purpose, we propose three mutation-based attacks, differing in the knowledge of the target classifier, addressing a key technical challenge: automatically crafting an adversarial sample from a known phishing website in a way that can mislead classifiers. To launch attacks in the white- and grey-box scenarios, we also propose a sample-based collision attack to gain the knowledge of the target classifier. We demonstrate the effectiveness and efficiency of our evasion attacks on the state-of-the-art, Google's phishing page filter, achieved 100% attack success rate in less than one second per website. Moreover, the transferability attack on BitDefender's industrial phishing page classifier, TrafficLight, achieved up to 81.25% attack success rate. We further propose a similarity-based method to mitigate such evasion attacks, Pelican. We demonstrate that Pelican can effectively detect evasion attacks. Our findings contribute to design more robust phishing website classifiers in practice.

cs.CR↗

arXiv2019

Model-based Automated Testing of JavaScript Web Applications via Longer Test Sequences

JavaScript has become one of the most widely used languages for Web development. However, it is challenging to ensure the correctness and reliability of Web applications written in JavaScript, due to their dynamic and event-driven features. A variety of dynamic analysis techniques for JavaScript Web applications have been proposed, but they are limited in either coverage or scalability. In this paper, we propose a model-based automated approach to achieve high code coverage in a reasonable amount of time via testing with longer event sequences. We implement our approach as the tool LJS, and perform extensive experiments on 21 publicly available benchmarks (18,559 lines of code in total). On average, LJS achieves 86.4\% line coverage in 10 minutes, which is 5.4\% higher than that of JSDep, a breadth-first search based automated testing tool enriched with partial order reduction. In particular, on large applications, the coverage of LJS is 11-18\% higher than that of JSDep. Our empirical finding supports that longer test sequences can achieve higher code coverage in JavsScript testing.

cs.SE↗

arXiv2019

Quantitative Verification of Masked Arithmetic Programs against Side-Channel Attacks

Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel information. However, designing masking algorithms is an error-prone process. In this paper, we propose a hybrid approach combing type inference and model-counting to verify masked arithmetic programs against side-channel attacks. The type inference allows an efficient, lightweight procedure to determine most observable variables whereas model-counting accounts for completeness. In case that the program is not perfectly masked, we also provide a method to quantify the security level of the program. We implement our methods in a tool QMVerif and evaluate it on cryptographic benchmarks. The experimental results show the effectiveness and efficiency of our approach.

cs.CR↗

arXiv2018

Making Agents' Abilities Explicit

Alternating-time temporal logics (ATL/ATL*) represent a family of modal logics for reasoning about agents' strategic abilities in multiagent systems (MAS). The interpretations of ATL/ATL* over the semantic model Concurrent Game Structures (CGS) usually vary depending on the agents' abilities, for instance, perfect vs. imperfect information, perfect vs. imperfect recall, resulting in a variety of variants which have been studied extensively in literature. However, they are defined at the semantic level, which may limit modeling flexibilities and may give counter-intuitive interpretations. To mitigate these issues, in this work, we propose to extend CGS with agents' abilities and study the new semantics of ATL/ATL* under this model. We give PSACE/2EXPTIME model-checking algorithms for ATL/ATL* and implement them as a prototype tool. Experiment results show the practical feasibility of the approach.

cs.AI↗

arXiv2018

KRust: A Formal Executable Semantics of Rust

Rust is a new and promising high-level system programming language. It provides both memory safety and thread safety through its novel mechanisms such as ownership, moves and borrows. Ownership system ensures that at any point there is only one owner of any given resource. The ownership of a resource can be moved or borrowed according to the lifetimes. The ownership system establishes a clear lifetime for each value and hence does not necessarily need garbage collection. These novel features bring Rust high performance, fine low-level control of C and C++, and unnecessity in garbage collection, which differ Rust from other existing prevalent languages. For formal analysis of Rust programs and helping programmers learn its new mechanisms and features, a formal semantics of Rust is desired and useful as a fundament for developing related tools. In this paper, we present a formal executable operational semantics of a realistic subset of Rust, called KRust. The semantics is defined in K, a rewriting-based executable semantic framework for programming languages. The executable semantics yields automatically a formal interpreter and verification tools for Rust programs. KRust has been thoroughly validated by testing with hundreds of tests, including the official Rust test suite.

cs.PL↗

arXiv2016

LTL Model-Checking for Dynamic Pushdown Networks Communicating via Locks

A Dynamic Pushdown Network (DPN) is a set of pushdown systems (PDSs) where each process can dynamically create new instances of PDSs. DPNs are a natural model of multi-threaded programs with (possibly recursive) procedure calls and thread creation. Extending DPNs with locks allows processes to synchronize with each other. Thus, DPNs with locks are a well adapted formalism to model multi-threaded programs that synchronize via locks. Therefore, it is important to have model-checking algorithms for DPNs with locks. We consider in this work model-checking for DPNs with locks against single-indexed LTL properties of the form V fi s.t. fi is a LTL formula interpreted over the PDS i. We consider the model-checking problems w.r.t. simple valuations (i.e, whether a configuration satisfies an atomic proposition depends only on its control location and held locks) and w.r.t. regular valuations (i.e., the set of the configurations satisfying an atomic proposition is a regular set of configurations). We show that these model-checking problems are decidable.

cs.LO↗