Model Inference and Testing

Irfan, Muhammad Naeem; Oriat, Catherine; Groz, Roland

doi:10.1016/b978-0-12-408094-2.00003-5

Cited by 24 publications

(13 citation statements)

References 23 publications

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“…In the absence of literature available on the topic, we explored the closest relevant field that is identifying vulnerable android and web applications using machine learning algorithms [45,46]. Most of the techniques extract a set of features from web, android or other types of applications and then use the features to train machine learning algorithms to identify vulnerable applications [1,4,7,47,48].…”

Section: Related Workmentioning

confidence: 99%

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Predicting sensitive information leakage in IoT applications using flows-aware machine learning approach

Naeem¹,

Alalfi²

2022

Preprint

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This paper presents an approach for identification of vulnerable IoT applications. The approach focuses on a category of vulnerabilities that leads to sensitive information leakage which can be identified by using taint flow analysis. Tainted flows vulnerability is very much impacted by the structure of the program and the order of the statements in the code, designing an approach to detect such vulnerability needs to take into consideration such information in order to provide precise results. In this paper, we propose and develop an approach, FlowsMiner, that mines features from the code related to program structure such as control statements and methods, in addition to program's statement order. FlowsMiner, generates features in the form of tainted flows. We developed, Flows2Vec, a tool that transform the features recovered by FlowsMiner into vectors, which are then used to aid the process of machine learning by providing a flow's aware model building process. The resulting model is capable of accurately classify applications as vulnerable if the vulnerability is exhibited by changes in the order of statements in source code. When compared to a base Bag of Words (BoW) approach, the experiments show that the proposed approach has improved the AUC of the prediction models for all algorithms and the best case for Corpus1 dataset is improved from 0.91 to 0.94 and for Corpus2 from 0.56 to 0.96.

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Section: Related Workmentioning

confidence: 99%

“…Previous work use machine learning or deep learning algorithms for identification of similar category of vulnerabilities in platforms other than IoT applications, such as android and web applications. [1,2,11,4,7,39,40,41,42,43,44].…”

Section: Introductionmentioning

confidence: 99%

Predicting sensitive information leakage in IoT applications using flows-aware machine learning approach

Naeem¹,

Alalfi²

2022

Preprint

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“…A counterexample is an input sequence in which the result of 𝐻 is different from the result of the SUL [2,11,41]. Then, the counterexample is used to update the observation table by adding prefixes or suffixes (for which several heuristics have been proposed) [23].…”

Section: Observation Tablementioning

confidence: 99%

Adaptive behavioral model learning for software product lines

Tavassoli

Damasceno

Khosravi

et al. 2022

Proceedings of the 26th ACM International Systems and Software Product Line Conference - Volume A

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Behavioral models enable the analysis of the functionality of software product lines (SPL), e.g., model checking and model-based testing. Model learning aims to construct behavioral models. Due to the commonalities among the products of an SPL, it is possible to reuse the previously-learned models during the model learning process. In this paper, an adaptive approach, called PL * , for learning the product models of an SPL is presented based on the well-known 𝐿 * algorithm. In this method, after learning each product, the sequences in the final observation table are stored in a repository which is used to initialize the observation table of the remaining products. The proposed algorithm is evaluated on two open-source SPLs and the learning cost is measured in terms of the number of rounds, resets, and input symbols. The results show that for complex SPLs, the total learning cost of PL * is significantly lower than that of the non-adaptive method in terms of all three metrics. Furthermore, it is observed that the order of learning products affects the efficiency of PL * . We introduce a heuristic to determine an ordering which reduces the total cost of adaptive learning. CCS CONCEPTS• Networks → Formal specifications; • Theory of computation → Query learning; • Hardware → Finite state machines; • Software and its engineering → Software product lines.

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“…Model inference [ 56 ] enables the prediction of LEC behaviors at run time. In contrast to software testing, where program inputs are generated to produce an intended system behavior, model inference deduces resulting system behavior from a given input.…”

Section: Introductionmentioning

confidence: 99%

MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty

et al. 2023

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Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., learning-enabled components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain and therefore cannot be assured as safe. Automated methods are needed for self-assessment and adaptation to decide when learned behavior can be trusted. This work introduces a model-driven approach to manage self-adaptation of a learning-enabled system (LES) to account for run-time contexts for which the learned behavior of LECs cannot be trusted. The resulting framework enables an LES to monitor and evaluate goal models at run time to determine whether or not LECs can be expected to meet functional objectives and enables system adaptation accordingly. Using this framework enables stakeholders to have more confidence that LECs are used only in contexts comparable to those validated at design time.

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Model Inference and Testing

Cited by 24 publications

References 23 publications

Predicting sensitive information leakage in IoT applications using flows-aware machine learning approach

Predicting sensitive information leakage in IoT applications using flows-aware machine learning approach

Adaptive behavioral model learning for software product lines

MoDALAS: addressing assurance for learning-enabled autonomous systems in the face of uncertainty

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