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software Vulnerabilities Detection Based on a Pre-trained Language Model

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Software Vulnerabilities Detection Based on a Pre-trained La...

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Wenlin Xu Tong Li Jinsong Wang Haibo Duan Yahui Tang School of Information Science and Engineering Yunnan University Kunming China School of Big Data Yunnan Agricultural University Kunming China Information Management Center Yunnan University of Finance and Economics Kunming China School of Software Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

software vulnerabilities detection is crucial in cyber security which protects the software systems from malicious attacks. The majority of earlier techniques relied on security professionals to provide software features before training a classification or regression model on the features to find vulnerabilities. However, defining software features and collecting high-quality labeled vulnerabilities for training are both time consuming. To handle these issues, in this paper, we propose an unsupervised and effective method for extracting software features and detecting software vulnerabilities automatically. Firstly, we obtain software features and build a new pre-trained BERT model through constructing C/C++ vocabulary and pre-training on software source code. We then fine-tune the pre-trained BERT model with a deep autoencoder and create low-dimensional embedding from the software features. We finally apply a clustering-based outlier detection method on the embedding to detect vulnerabilities. We evaluate our method on five datasets with programs written in C/C++, experimental results show that our method outperforms state-of-the-art software vulnerability detection methods.

关键词： Training Vocabulary Privacy Computational modeling Source coding Feature extraction software systems

Modality Decoupling is All You Need: A Simple Solution for Unsupervised Hyperspectral Image Fusion

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arXiv 2024年

作者： Du, Songcheng Zou, Yang Wang, Zixu Li, Xingyuan Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University China School of Software Technology Dalian University of Technology China Institute of Mathematics Physics and Computer Science Aberystwyth University United Kingdom The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University China

Hyperspectral Image Fusion (HIF) aims to fuse low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) to reconstruct high spatial and high spectral resolution images. Current methods typically apply direct fusion from the two modalities without valid supervision, failing to fully perceive the deep modality-complementary information and hence, resulting in a superficial understanding of inter-modality connections. To bridge this gap, we propose a simple and effective solution for unsupervised HIF with an assumption that modality decoupling is essential for HIF. We introduce the modality clustering loss that ensures clear guidance of the modality, decoupling towards modality-shared features while steering clear of modality-complementary ones. Also, we propose an end-to-end Modality-Decoupled Spatial-Spectral Fusion (MossFuse) framework that decouples shared and complementary information across modalities and aggregates a concise representation of the LR-HSI and HR-MSI to reduce the modality redundancy. Systematic experiments over multiple datasets demonstrate that our simple and effective approach consistently outperforms the existing HIF methods while requiring considerably fewer parameters with reduced inference time. Copyright © 2024, The Authors. All rights reserved.

关键词： Spectral resolution

BIM: Improving Graph Neural Networks with Balanced Influence Maximization

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BIM: Improving Graph Neural Networks with Balanced Influence...

International Conference on data engineering

作者： Wentao Zhang Xinyi Gao Ling Yang Meng Cao Ping Huang Jiulong Shan Hongzhi Yin Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications The University of Queensland Australia Key Lab of High Confidence Software Technologies Peking University Apple Inc. Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

The imbalanced data classification problem has aroused lots of concerns from both academia and industry since data imbalance is a widespread phenomenon in many real-world scenarios. Although this problem has been well researched from the view of imbalanced class samples, we further argue that graph neural networks (GNNs) expose a unique source of imbalance from the influenced nodes of different classes of labeled nodes, i.e., labeled nodes are imbalanced in terms of the number of nodes they influenced during the influence propagation in GNNs. To tackle this previously unexplored influence-imbalance issue, we connect social influence maximization with the imbalanced node classification problem and propose balanced influence maximization (BIM). Specifically, BIM greedily assigns the pseudo label to the node which can maximize the number of influenced nodes in GNN training while making the influence of each class more balance. Experimental results on five public datasets demonstrate the effectiveness of our method in relieving the influence-imbalance issue. For example, when training a GCN with an imbalance ratio of 0.1, BIM significantly outperforms the most competitive baseline by 0.6% -9.8% in five public datasets in terms of the F1 score.

关键词： Training Industries data engineering Graph neural networks

GM-DF: Generalized Multi-Scenario Deepfake Detection

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arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

Towards overfitting avoidance: Tuning-free tensor-aided multi-user channel estimation for 3D massive MIMO communications

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arXiv 2021年

作者： Cheng, Lei Shi, Qingjiang Shenzhen Research Institute of Big Data Shenzhen Guangdong China School of Software Engineering Tongji University Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

Channel estimation has long been deemed as one of the most critical problems in three-dimensional (3D) massive multiple-input multiple-output (MIMO), which is recognized as the leading technology that enables 3D spatial signal processing in the fifth-generation (5G) wireless communications and beyond. Recently, by exploring the angular channel model and tensor decompositions, the accuracy of single-user channel estimation for 3D massive MIMO communications has been significantly improved given a limited number of pilot signals. However, these existing approaches cannot be straightforwardly extended to the multi-user channel estimation task, where the base station (BS) aims at acquiring the channels of multiple users at the same time. The difficulty is that the coupling among multiple users’ channels makes the channel estimation deviate from widely-used tensor decompositions. It gives a non-standard tensor decomposition format that has not been well tackled. To overcome this challenge, besides directly fitting the new tensor model for channel estimation to the wireless data via block coordinate descent (BCD) method, which is prone to the overfitting of noises or requires regularization parameter tuning, we further propose a novel tuning-free channel estimation algorithm that can automatically control the channel model complexity and thus effectively avoid the overfitting. Numerical results are presented to demonstrate the excellent performance of the proposed algorithm in terms of both estimation accuracy and overfitting avoidance. © 2021, CC BY.

关键词： MIMO systems

MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARNING

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arXiv 2023年

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention. Copyright © 2023, The Authors. All rights reserved.

关键词：

Graph Attention Multi-Layer Perceptron

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arXiv 2022年

作者： Zhang, Wentao Yin, Ziqi Sheng, Zeang Li, Yang Ouyang, Wen Li, Xiaosen Tao, Yangyu Yang, Zhi Cui, Bin School of CS & Key Laboratory of High Confidence Software Technologies Peking University China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China Tencent Inc China Beijing Institute of Technology China

Graph neural networks (GNNs) have achieved great success in many graph-based applications. However, the enormous size and high sparsity level of graphs hinder their applications under industrial scenarios. Although some scalable GNNs are proposed for large-scale graphs, they adopt a fixed k-hop neighborhood for each node, thus facing the over-smoothing issue when adopting large propagation depths for nodes within sparse regions. To tackle the above issue, we propose a new GNN architecture — Graph Attention Multi-Layer Perceptron (GAMLP), which can capture the underlying correlations between different scales of graph knowledge. We have deployed GAMLP in Tencent with the Angel platform 1, and we further evaluate GAMLP on both real-world datasets and large-scale industrial datasets. Extensive experiments on these 14 graph datasets demonstrate that GAMLP achieves state-of-the-art performance while enjoying high scalability and efficiency. Specifically, it outperforms GAT by 1.3% regarding predictive accuracy on our large-scale Tencent Video dataset while achieving up to 50× training speedup. Besides, it ranks top-1 on both the leader-boards of the largest homogeneous and heterogeneous graph (i.e., ogbn-papers100M and ogbn-mag) of Open Graph Benchmark 2 Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

VMeta: A QoS dataset for Metaverse Services 6th

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VMeta: A QoS Dataset for Metaverse Services

6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Ge, Zhiyuan Jin, Huiying Dong, Hai Ji, Shunhui Li, Jiajia Yang, Shuhan Wang, Qi Zhang, Pengcheng Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing210000 China College of Computer Science and Software Engineering Hohai University Nanjing210000 China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210000 China School of Computing Technologies RMIT University Melbourne3000 Australia

ISBN: (纸本)9789819614134

The metaverse is undergoing repaid evolution with metaverse services emerging as a significant focal point of research across virtual reality, augmented reality, and metaverse social networking. However, a key challenge lies in the scarcity of datasets that accurately represent metaverse services, impeding the advancement of metaverse service technologies and applications. To address this challenge, we have categorized metaverse services into virtual scene services and meta-services. Subsequently, we introduced a metaverse service dataset called "VMeta". VMeta encompasses comprehensive information about virtual scene services and meta-services, including essential QoS (Quality of Service) data. This dataset serves as a valuable resource for researchers exploring various aspects of metaverse services, with the ultimate goal of enhancing user satisfaction in the metaverse. We have conducted evaluations and tests on VMeta to optimize metaverse QoS. The results demonstrate its substantial value for metaverse service research. Our datasets are available at https://***/Gelieziman/VMeta. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime