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Article
Computer Science, Theory & Methods
Triet H. M. Le et al.
Summary: This article highlights the importance of SV assessment and prioritization and provides a taxonomy of past research efforts and best practices. The article also discusses current limitations and proposes potential solutions.
ACM COMPUTING SURVEYS
(2023)
Proceedings Paper
Computer Science, Software Engineering
Van-Anh Nguyen et al.
Summary: Identifying vulnerabilities in source code is crucial for protecting software systems from cyber security attacks. This paper proposes ReGVD, a graph neural network-based model, which treats vulnerability detection as a text classification problem and can run on various source codes and libraries. ReGVD outperforms existing state-of-the-art models and achieves the highest accuracy on a real-world benchmark dataset.
2022 ACM/IEEE 44TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2022)
(2022)
Article
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Chen Zhang et al.
Summary: Federated learning is a setup where multiple clients collaborate to solve machine learning problems under the coordination of a central aggregator. It reduces systematic privacy risks and costs through local computing and model transmission. This method ensures data privacy for each device and improves learning efficiency and security.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Software Engineering
Xiao Cheng et al.
Summary: Static bug detection is effective in detecting well-defined memory errors, but modern software systems have diverse and complex vulnerabilities. DeepWukong utilizes deep learning to embed code fragments and detect software vulnerabilities in C/C++ programs, showing promising results in addressing general static code analysis challenges.
ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
(2021)
Article
Computer Science, Information Systems
Sawsan AbdulRahman et al.
Summary: Driven by privacy concerns and deep learning visions, a paradigm shift has occurred in the applicability mechanism of machine learning (ML) over the past four years. A new model called federated learning (FL) has emerged as a privacy-preserving decentralized approach that involves local ML training and eliminates data communication overhead. This article explores and compares various ML-based deployment architectures, with a focus on in-depth investigation of FL, providing a new classification of FL topics and research fields based on analysis of technical challenges and current work in the field.
IEEE INTERNET OF THINGS JOURNAL
(2021)
Article
Computer Science, Information Systems
Sicong Cao et al.
Summary: The proposed BGNN4VD method improves vulnerability detection performance by constructing a Bidirectional Graph Neural Network. By extracting syntax and semantic information of source code and introducing bidirectional edges, BGNN4VD achieves 4.9%, 11.0%, and 8.4% improvement in F1-measure, accuracy, and precision compared to four state-of-the-art vulnerability detection methods.
INFORMATION AND SOFTWARE TECHNOLOGY
(2021)
Article
Computer Science, Theory & Methods
Huanting Wang et al.
Summary: This paper presents FUNDED, a novel learning framework that leverages graph neural networks to develop a graph-based learning method. Unlike prior work, Funded operates on a graph representation of the program source code, capturing the program syntax, semantics, and flows.
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
(2021)
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PROCEEDINGS OF THE IEEE
(2020)
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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
(2019)
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JOURNAL OF SYSTEMS AND SOFTWARE
(2019)
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Guanjun Lin et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2018)
Article
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Seyed Mohammad Ghaffarian et al.
ACM COMPUTING SURVEYS
(2017)
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Computer Science, Artificial Intelligence
Sukarna Barua et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
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Article
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Julian Jang-Jaccard et al.
JOURNAL OF COMPUTER AND SYSTEM SCIENCES
(2014)