Combining requirement mining, software model checking and simulation-based verification for industrial automotive systems

Yamaguchi, Tomoya; Kaga, Tomoyuki; Donzé, Alexandre; Seshia, Sanjit A.

doi:10.1109/fmcad.2016.7886680

Cited by 24 publications

(17 citation statements)

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“…We have applied the above approach to the problem of compositional falsification of cyber-physical systems (CPS) with machine learning components [10]. For this class of CPS, including those with highly non-linear dynamics and even blackbox components, simulation-based falsification of temporal logic properties is an approach that has proven effective in industrial practice (e.g., [23,50]). We present here a sample of results on the AEBS example from [10], referring the reader to more detailed descriptions in the other papers on the topic [10,11].…”

Section: Sample Resultsmentioning

confidence: 99%

Semantic Adversarial Deep Learning

Dreossi

Jha

Seshia

2018

Computer Aided Verification

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Abstract.Fueled by massive amounts of data, models produced by machine-learning (ML) algorithms, especially deep neural networks, are being used in diverse domains where trustworthiness is a concern, including automotive systems, finance, health care, natural language processing, and malware detection. Of particular concern is the use of ML algorithms in cyber-physical systems (CPS), such as self-driving cars and aviation, where an adversary can cause serious consequences.However, existing approaches to generating adversarial examples and devising robust ML algorithms mostly ignore the semantics and context of the overall system containing the ML component. For example, in an autonomous vehicle using deep learning for perception, not every adversarial example for the neural network might lead to a harmful consequence. Moreover, one may want to prioritize the search for adversarial examples towards those that significantly modify the desired semantics of the overall system. Along the same lines, existing algorithms for constructing robust ML algorithms ignore the specification of the overall system. In this paper, we argue that the semantics and specification of the overall system has a crucial role to play in this line of research. We present preliminary research results that support this claim.

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Section: Sample Resultsmentioning

confidence: 99%

Semantic Adversarial Deep Learning

Dreossi

Jha

Seshia

2018

Computer Aided Verification

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“…Keynote in industrial practice [21], [22]. We present here a sample of results on the AEBS example from [14], referring the reader to more detailed descriptions in the other papers on the topic [14], [15].…”

Section: Ieee Designandtestmentioning

confidence: 99%

Semantic Adversarial Deep Learning

2020

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Adversarial examples have emerged as a key threat for machine-learningbased systems, especially the ones that employ deep neural networks. Unlike a large body of research in this area, this Keynote article accounts for the semantic, context, and specifications of the complete system with machine learning components in resource-constrained environments.-Muhammad Shafique, Technische Universität Wien  Machine learning (Ml) algorithms, fueled by massive amounts of data, are increasingly being utilized in several domains, including healthcare, finance, and transportation. Models produced by ML algorithms, especially deep neural networks (DNNs), are being deployed in domains where trustworthiness is a big concern, such as automotive systems [1], finance [2], healthcare [3], and cyber security [4]. Of particular concern is the use of ML (including deep learning) in cyber-physical systems (CPSs) [5], such as autonomous vehicles, where the presence of an adversary can cause serious consequences. However, in designing and deploying these algorithms in critical CPSs, the presence of an active adversary is often ignored.

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“…We focus on specifications in Signal Temporal Logic (STL), and for this reason use a temporal logic falsifier, Breach [8], as our CPS Analyzer; however, other specification formalisms and tools may also be used and our framework is largely agnostic to these choices. Even though temporal logic falsification is a mature technology with initial industrial adoption (e.g., [44]), several technical challenges remain. First, we need to construct the validity domain of an STL specification -the input sub-space where the property is satisfied -for a CPSML model with abstracted (correct/incorrect) ML components, and identify the region of uncertainty (ROU).…”

Section: Introductionmentioning

confidence: 99%

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

Donzé²,

2019

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Cyber-physical systems (CPS), such as automotive systems, are starting to include sophisticated machine learning (ML) components. Their correctness, therefore, depends on properties of the inner ML modules. While learning algorithms aim to generalize from examples, they are only as good as the examples provided, and recent efforts have shown that they can produce inconsistent output under small adversarial perturbations. This raises the question: can the output from learning components lead to a failure of the entire CPS? In this work, we address this question by formulating it as a problem of falsifying signal temporal logic specifications for CPS with ML components. We propose a compositional falsification framework where a temporal logic falsifier and a machine learning analyzer cooperate with the aim of finding falsifying executions of the considered model. The efficacy of the proposed technique is shown on an automatic emergency braking system model with a perception component based on deep neural networks.

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Combining requirement mining, software model checking and simulation-based verification for industrial automotive systems

Cited by 24 publications

References 1 publication

Semantic Adversarial Deep Learning

Semantic Adversarial Deep Learning

Semantic Adversarial Deep Learning

Compositional Falsification of Cyber-Physical Systems with Machine Learning Components

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