Petri net-based verification of security protocol implementation in software evolution

Ahmadon, Mohd Anuaruddin Bin; Yamaguchi, Shingo; Gupta, Brij B.

doi:10.1504/ijes.2018.095754

Cited by 7 publications

(2 citation statements)

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“…The correlation matrix is calculated mathematically after classifying and analyzing the types of attacks. The concept of model-driven security validation was explored by literature [21] through software development and behavioral analysis, and the results confirmed the feasibility of the scheme. Literature [22] designed an integrated approach that seamlessly combines security slicing with hybrid constraint solving to detect vulnerabilities developed in open source components, and numerical detection through simulation confirms that the approach can effectively and accurately detect vulnerabilities.…”

Section: Introductionmentioning

confidence: 77%

Effective Application of Artificial Intelligence Techniques in Security Risk Assessment and Dependency Analysis of Open Source Components

Hu,

Zhao,

Hang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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With the wide application of open source software, the security of open source components has become a non-negligible problem in software development. In this paper, based on the research on deep learning algorithms, the deep reinforcement learning algorithm DQN is proposed, and the DQN-LightGBM model is constructed by combining LightGBM classifiers to achieve better mining performance. This model introduces the attention mechanism and BiLSTM network, and the TextACBL vulnerability identification algorithm is proposed. In the end, the performance of the open source component vulnerability mining model is evaluated to investigate its impact on vulnerability identification, risk assessment, and dependency analysis of open source components. The results show that the performance of DQN-LightGBM model is above 0.9 in accuracy, checking accuracy, checking completeness, AUC value, and F1, and the mining completion time is only 54s, which is the best. The accuracy of TextACBL model in identifying the types of vulnerabilities is above 94%, and the vulnerability danger levels on the seven items measured are high, low, high, high, and high risk, respectively, medium risk, high risk, and medium risk. The differences in the global graph features of the identified normal and vulnerability samples are obvious, and there are frequent dependencies in the probability graphs of the opcodes of the vulnerability samples. This study allows for the assessment of security risks of open source components and the comprehensive, accurate, and efficient conduct of dependency analysis.

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Section: Introductionmentioning

confidence: 77%

Effective Application of Artificial Intelligence Techniques in Security Risk Assessment and Dependency Analysis of Open Source Components

Hu,

Zhao,

Hang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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“…And a usable tool, CPN Tools [25], can be used for CPN modelling, simulation, and model checking analysis. Petri net and its extension are widely used in software design and management [26], workflow analysis [27,28], modelling analysis [29,30], fault diagnosis, and protocol verification [31,32]. Additionally, the CPN can provide technical support for error detection by model checking.…”

Section: Related Workmentioning

confidence: 99%

Concurrent software fine‐coarse‐grained automatic modelling by Coloured Petri Nets for model checking

2022

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The state space explosion restricts the error detection of concurrent software. The abstraction can provide a solution to avoid state space explosion, but it is easy to ignore important details, resulting in inaccurate detection results. This paper proposes a methodology of fine-coarse-grained automatic modelling for Java source programs. By the principle that the execution details of property-unchecked, non-interactive, and unrelated statements do not affect the model checking results, we model coarse-grained model fragments for such statements, while fine-grained model fragments for property-checked, interactive, and related statements. Our method reduces the model and state space and ensures the error detection of the source program based on model checking. Moreover, we prove the equivalence of the fine-grained model, the coarse-grained model, and the program. Finally, this paper gives an experiment to verify the effectiveness of the proposed method.

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Software fault classification using extreme learning machine: a cognitive approach

Pandey

Gupta

2018

Evol. Intel.

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Petri net-based verification of security protocol implementation in software evolution

Cited by 7 publications

References 0 publications

Effective Application of Artificial Intelligence Techniques in Security Risk Assessment and Dependency Analysis of Open Source Components

Effective Application of Artificial Intelligence Techniques in Security Risk Assessment and Dependency Analysis of Open Source Components

Concurrent software fine‐coarse‐grained automatic modelling by Coloured Petri Nets for model checking

Software fault classification using extreme learning machine: a cognitive approach

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