1,264 research outputs found
Scalable Inference of Symbolic Adversarial Examples
We present a novel method for generating symbolic adversarial examples: input
regions guaranteed to only contain adversarial examples for the given neural
network. These regions can generate real-world adversarial examples as they
summarize trillions of adversarial examples.
We theoretically show that computing optimal symbolic adversarial examples is
computationally expensive. We present a method for approximating optimal
examples in a scalable manner. Our method first selectively uses adversarial
attacks to generate a candidate region and then prunes this region with
hyperplanes that fit points obtained via specialized sampling. It iterates
until arriving at a symbolic adversarial example for which it can prove, via
state-of-the-art convex relaxation techniques, that the region only contains
adversarial examples. Our experimental results demonstrate that our method is
practically effective: it only needs a few thousand attacks to infer symbolic
summaries guaranteed to contain adversarial examples
Test generation from P systems using model checking
This paper presents some testing approaches based on model checking and using different testing criteria. First, test sets are built from different Kripke structure representations. Second, various rule coverage criteria for transitional, non-deterministic, cell-like P systems, are considered in order to generate adequate test sets. Rule based coverage criteria (simple rule coverage, context-dependent rule coverage and variants) are defined and, for each criterion, a set of LTL (Linear Temporal Logic) formulas is provided. A codification of a P system as a Kripke structure and the sets of LTL properties are used in test generation: for each criterion, test cases are obtained from the counterexamples of the associated LTL formulas, which are automatically generated from the Kripke structure codification of the P system. The method is illustrated with an implementation using a specific model checker, NuSMV. (C) 2010 Elsevier Inc. All rights reserved
Simplifying Contract-Violating Traces
Contract conformance is hard to determine statically, prior to the deployment
of large pieces of software. A scalable alternative is to monitor for contract
violations post-deployment: once a violation is detected, the trace
characterising the offending execution is analysed to pinpoint the source of
the offence. A major drawback with this technique is that, often, contract
violations take time to surface, resulting in long traces that are hard to
analyse. This paper proposes a methodology together with an accompanying tool
for simplifying traces and assisting contract-violation debugging.Comment: In Proceedings FLACOS 2012, arXiv:1209.169
Linear Encodings of Bounded LTL Model Checking
We consider the problem of bounded model checking (BMC) for linear temporal
logic (LTL). We present several efficient encodings that have size linear in
the bound. Furthermore, we show how the encodings can be extended to LTL with
past operators (PLTL). The generalised encoding is still of linear size, but
cannot detect minimal length counterexamples. By using the virtual unrolling
technique minimal length counterexamples can be captured, however, the size of
the encoding is quadratic in the specification. We also extend virtual
unrolling to Buchi automata, enabling them to accept minimal length
counterexamples.
Our BMC encodings can be made incremental in order to benefit from
incremental SAT technology. With fairly small modifications the incremental
encoding can be further enhanced with a termination check, allowing us to prove
properties with BMC. Experiments clearly show that our new encodings improve
performance of BMC considerably, particularly in the case of the incremental
encoding, and that they are very competitive for finding bugs. An analysis of
the liveness-to-safety transformation reveals many similarities to the BMC
encodings in this paper. Using the liveness-to-safety translation with
BDD-based invariant checking results in an efficient method to find shortest
counterexamples that complements the BMC-based approach.Comment: Final version for Logical Methods in Computer Science CAV 2005
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Symbolic Model Learning: New Algorithms and Applications
In this thesis, we study algorithms which can be used to extract, or learn, formal mathematical models from software systems and then using these models to test whether the given software systems satisfy certain security properties such as robustness against code injection attacks. Specifically, we focus on studying learning algorithms for automata and transducers and the symbolic extensions of these models, namely symbolic finite automata (SFAs). In a high level, this thesis contributes the following results:
1. In the first part of the thesis, we present a unified treatment of many common variations of the seminal L* algorithm for learning deterministic finite automata (DFAs) as a congruence learning algorithm for the underlying Nerode congruence which forms the basis of automata theory. Under this formulation the basic data structures used by different variations are unified as different ways to implement the Nerode congruence using queries.
2. Next, building on the new formulation of L*-style algorithms we proceed to develop new algorithms for learning transducer models. Firstly, we present the first algorithm for learning deterministic partial transducers. Furthermore, we extend my algorithm into non-deterministic models by introducing a novel, generalized congruence relation over string transformations which is able to capture a subclass of string transformations with regular lookahead. We demonstrate that this class is able to capture many practical string transformation from the domain of string sanitizers in Web applications.
3. Classical learning algorithms for automata and transducers operate over finite alphabets and have a query complexity that scales linearly with the size of the alphabet. However, in practice, this dependence on the alphabet size hinders the performance of the algorithms. To address this issue, we develop the MAT* algorithm for learning symbolic finite state automata (SFAs) which operate over infinite alphabets. In practice, the MAT* learning algorithm allow us to plug custom transition learning algorithms which will efficiently infer the predicates in the transitions of the SFA without querying the whole alphabet set.
4. Finally, we use our learning algorithm toolbox as the basis for the development of a set of black-box testing algorithms. More specifically, we present Grammar Oriented Filter Auditing (GOFA), a novel technique which allows one to utilize my learning algorithms to evaluate the robustness of a string sanitizer or filter against a set of attack strings given as a context-free grammar. Furthermore, because such grammars are many times unavailable, we developed sfadiff a differential testing technique based on symbolic automata learning which can be used in order to perform differential testing of two different parser implementations using SFA learning algorithms and we demonstrate how our algorithm can be used to develop program fingerprints. We evaluate our algorithms against state-of-the-art Web Application Firewalls and discover over 15 previously unknown vulnerabilities which result in evading the firewalls and performing code injection attacks in the backend Web application. Finally, we show how our learning algorithms can uncover vulnerabilities which are missed by other black-box methods such as fuzzing and grammar-based testing
Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustness
In recent years, the notion of local robustness (or robustness for short) has
emerged as a desirable property of deep neural networks. Intuitively,
robustness means that small perturbations to an input do not cause the network
to perform misclassifications. In this paper, we present a novel algorithm for
verifying robustness properties of neural networks. Our method synergistically
combines gradient-based optimization methods for counterexample search with
abstraction-based proof search to obtain a sound and ({\delta}-)complete
decision procedure. Our method also employs a data-driven approach to learn a
verification policy that guides abstract interpretation during proof search. We
have implemented the proposed approach in a tool called Charon and
experimentally evaluated it on hundreds of benchmarks. Our experiments show
that the proposed approach significantly outperforms three state-of-the-art
tools, namely AI^2 , Reluplex, and Reluval
A Theory of Formal Synthesis via Inductive Learning
Formal synthesis is the process of generating a program satisfying a
high-level formal specification. In recent times, effective formal synthesis
methods have been proposed based on the use of inductive learning. We refer to
this class of methods that learn programs from examples as formal inductive
synthesis. In this paper, we present a theoretical framework for formal
inductive synthesis. We discuss how formal inductive synthesis differs from
traditional machine learning. We then describe oracle-guided inductive
synthesis (OGIS), a framework that captures a family of synthesizers that
operate by iteratively querying an oracle. An instance of OGIS that has had
much practical impact is counterexample-guided inductive synthesis (CEGIS). We
present a theoretical characterization of CEGIS for learning any program that
computes a recursive language. In particular, we analyze the relative power of
CEGIS variants where the types of counterexamples generated by the oracle
varies. We also consider the impact of bounded versus unbounded memory
available to the learning algorithm. In the special case where the universe of
candidate programs is finite, we relate the speed of convergence to the notion
of teaching dimension studied in machine learning theory. Altogether, the
results of the paper take a first step towards a theoretical foundation for the
emerging field of formal inductive synthesis
Generating Diagnoses for Probabilistic Model Checking Using Causality
One of the most major advantages of Model checking over other formal methods of verification, its ability to generate an error trace in case of a specification falsified in the model. We call this trace a counterexample. However, understanding the counterexample is not that easy task, because model checker generates usually multiple counterexamples of long length, what makes the analysis of counterexample time-consuming as well as costly task. Therefore, counterexamples should be small and as indicative as possible to be understood. In probabilistic model checking (PMC) counterexample generation has a quantitative aspect.Ā The counterexample in PMC is a set of paths in which a path formula holds, and their accumulative probability mass violates the probability bound. In this paper, we address the complementary task of counterexample generation which is the counterexample diagnosis in PMC. We propose an aided-diagnostic method for probabilistic counterexamples based on the notion of causality and responsibility. Given a counterexample for a Probabilistic CTL (PCTL) formula that doesnāt hold over Discreet-Time-Markov-Chain (DTMC) model, this method guides the user to the most responsible causes in the counterexample.</p
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