Vulnerability detection based on federated learning

Article Computer Science, Theory & Methods

A Survey on Data-driven Software Vulnerability Assessment and Prioritization

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)

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Proceedings Paper Computer Science, Software Engineering

ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection

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)

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Article Computer Science, Artificial Intelligence

A survey on federated learning

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)

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Article Computer Science, Software Engineering

DeepWukong: Statically Detecting Software Vulnerabilities Using Deep Graph Neural Network

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)

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Article Computer Science, Information Systems

A Survey on Federated Learning: The Journey From Centralized to Distributed On-Site Learning and Beyond

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)

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Article Computer Science, Information Systems

BGNN4VD: Constructing Bidirectional Graph Neural-Network for Vulnerability Detection

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)

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Article Computer Science, Theory & Methods

Combining Graph-Based Learning With Automated Data Collection for Code Vulnerability Detection

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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Article Engineering, Electrical & Electronic