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Smart Contract Vulnerability Detection Based on Dual Attention Graph Convolutional network 17th

Smart Contract Vulnerability Detection Based on Dual Attent...

17th EAI International Conference on Collaborative Computing: networking, Applications, and Worksharing, CollaborateCom 2021

作者： Fan, Yuqi Shang, Siyuan Ding, Xu School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity Anhui Wuhu241002 China Institute of Industry and Equipment Technology Hefei University of Technology Anhui Hefei230009 China

ISBN: (纸本)9783030926373

Smart contracts on blockchains have received increasing attention due to the decentralized, transparent, and immutable characteristics of blockchain. However, smart contracts are prone to security problems caused by critical vulnerabilities, which can lead to huge economic losses. Therefore, it is urgent to provide strong and robust security assurance for smart contracts. Most existing studies on smart contract vulnerability detection methods take heavy reliance on experts-defined rules, which are extremely time-consuming and labor-demanding. Moreover, the manually-set rules are limited to specific tasks and subject to errors. Although some studies explore the use of deep learning methods, they fail to represent both semantics and structural information. In this paper, we propose a novel model, Dual Attention Graph Convolutional network (DA-GCN), to detect vulnerabilities in smart contracts on blockchains. Both control flow graph and opcode sequence extracted from smart contract bytecodes are fed into the feature extractor based on graph convolutional network and self-attention mechanism. Model DA-GCN then uses control flow level attention to focus on the more important nodes in the control flow graph and suppress useless information. Finally, a multi layer perceptron is used to identify whether the smart contract is vulnerable. Experimental results on the real-world smart contract data set containing two vulnerabilities of reentrancy and timestamp dependency demonstrate that our proposed model DA-GCN can effectively improve the performance of smart contract vulnerability detection. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Smart contract

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DICOM-Fuzzer: Research on DICOM Vulnerability Mining Based on Fuzzing Technology 14th

DICOM-Fuzzer: Research on DICOM Vulnerability Mining Based o...

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14th EAI International Conference on Communications and networking in China, ChinaCom 2019

作者： Wang, Zhiqiang Li, Quanqi Liu, Qian Liu, Biao Zhang, Jianyi Yang, Tao Liu, Qixu Beijing Electronic Science and Technology Institute Beijing China State Information Center Beijing China Key Lab of Information Network Security Ministry of Public Security Shanghai China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783030411138

In recent years, the medical equipment and related information systems show the characteristics of mobility, networking, intelligence. At the same time, security incidents caused by medical equipment emerge in an endless stream, which brings a huge threat to the information security of users and causes serious harm. Most medical devices use open source protocol library, which brings great security risks to the digitalization and informatization of medical devices. Therefore, in the face of growing security threats and challenges, it is urgent to study the security of medical equipment. In this paper, the vulnerability mining of DICOM was studied, the most commonly used communication standard for high-performance medical devices, and a vulnerability mining model based on Fuzzing technology was proposed. This model constructed a vulnerability mining environment by simulating PACS system, and implemented a prototype system DICOM-Fuzzer. The system includes initialization, test case generation and other modules, which can complete large-scale automatic testing and exception monitoring. Then, three different versions of the open source library were selected to test the 1000 test cases generated respectively. It was found that when the received file data was greater than 7080 lines, the overflow would occur, resulting in the denial of service of the system. Finally, the security suggestions and repair measures were put forward, and the future research was described. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Medical applications

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CGNet: Classification-Guided Multi-Task Interactive network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohua Lv Yanlong Xu Yongbiao Gao Guixin Zhao Guotao Wang Xiangcheng Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing Image fusion information exchange Hyperspectral imaging

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Towards Better Distortion Feature Learning for Object Detection in Top-View Fisheye Cameras

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IEEE Transactions on Multimedia 2024年

作者： Guo, Pengbo Liu, Chengxu Hou, Xingsong Qian, Xueming Xi'an Jiaotong University School of Software Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xi'an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Information and Communication Engineering SMILES LAB Xi'an710049 China Shaanxi Yulan Jiuzhou Intelligent Optoelectronic Technology Co. Ltd Xi'an710000 China

With the development of deep learning in recent years, the performance of object detection under conventional cameras has been significantly improved. Nevertheless, due to the distortion caused by the fisheye cameras, detecting objects in this scenario remains a significant challenge. The dominant approaches focus on modifying the shape of the bounding box to better align the boundaries of the distorted object. However, these methods neglect the learning of spatial distortion information, which prevents them from satisfactory results. In this paper, we propose a novel fisheye camera detection network to learn distortion features better, dubbed SDANet. SDANet is composed of a series of SDABlocks, which are designed to learn spatial distortion features. Each SDABlock consists of multiple convolution kernels of different sizes, and it can generate the most suitable kernel based on the current input's distortion characteristics. Moreover, to address the limitations of the scarcity and uneven spatial distribution of fisheye image datasets on performance improvement, we propose a dedicated data augmentation strategy called Prominent Fisheye Distortion Augmentation (PFDAug). PFDAug can further introduce distortions to fisheye images, effectively alleviating these problems. Experimental results on the CEPDOF, MW-R, HABBOF, LOAF, and FishEye8k fisheye image datasets demonstrate that our method achieves state-ofthe-art performance. © 1999-2012 IEEE.

关键词： Cameras

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A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Science China(information Sciences) 2019年第3期62卷 196-198页

作者： Yusong DU Baodian WEI Huang ZHANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Chongqing Key Lab of Computer Network and Communication Technology

Dear editor,Discrete Gaussian sampling, that is, sampling from a discrete Gaussian distribution DΛ,σ,cwith parameter σ> 0 and center c∈R~n over an ndimensional lattice Λ, has been considered by the cryptography research community as one of the fundamental building blocks of lattice-based cryptography [1, 2]. The simplest lattice is the one-

关键词： A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node Classification

arXiv

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arXiv 2023年

作者： Fu, Xingcheng Yuan, Haonan Wei, Yuecen Wu, Jia Li, Jianxin Sun, Qingyun Peng, Hao School of Computer Science and Engineering BDBC Beihang University Beijing China Guangxi Key Lab of Multi-source Information Mining Security Guangxi Normal University Guilin China School of Computing Macquarie University Sydney Australia

Learning unbiased node representations for imbalanced samples in the graph has become a more remarkable and important topic. For the graph, a significant challenge is that the topological properties of the nodes (e.g., locations, roles) are unbalanced (topology-imbalance), other than the number of training labeled nodes (quantity-imbalance). Existing studies on topology-imbalance focus on the location or the local neighborhood structure of nodes, ignoring the global underlying hierarchical properties of the graph, i.e., hierarchy. In the real-world scenario, the hierarchical structure of graph data reveals important topological properties of graphs and is relevant to a wide range of applications. We find that training labeled nodes with different hierarchical properties have a significant impact on the node classification tasks and confirm it in our experiments. It is well known that hyperbolic geometry has a unique advantage in representing the hierarchical structure of graphs. Therefore, we attempt to explore the hierarchy-imbalance issue for node classification of graph neural networks with a novelty perspective of hyperbolic geometry, including its characteristics and causes. Then, we propose a novel hyperbolic geometric hierarchy-imbalance learning framework, named HyperIMBA, to alleviate the hierarchy-imbalance issue caused by uneven hierarchy-levels and cross-hierarchy connectivity patterns of labeled nodes. Extensive experimental results demonstrate the superior effectiveness of HyperIMBA for hierarchy-imbalance node classification tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Robust and fast image hashing with two-dimensional PCA

Robust and fast image hashing with two-dimensional PCA

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作者： Liang, Xiaoping Tang, Zhenjun Xie, Xiaolan Wu, Jingli Zhang, Xianquan Guangxi Key Lab of Multi-Source Information Mining and Security and Department of Computer Science Guangxi Normal University No.15 Yucai Road Guilin541004 China College of Information Science and Engineering Guilin University of Technology Guilin541006 China

Image hashing is a useful technology of many multimedia systems, such as image retrieval, image copy detection, multimedia forensics and image authentication. Most of the existing hashing algorithms do not reach a good classification between robustness and discrimination and some hashing algorithms based on dimensionality reduction have high computational cost. To solve these problems, we propose a robust and fast image hashing based on two-dimensional (2D) principal component analysis (PCA) and saliency map. The saliency map determined by a visual attention model called LC (luminance contrast) method can ensure good robustness of our hashing. Since 2D PCA is a fast and efficient technique of dimensionality reduction, the use of 2D PCA helps to learn a compact and discriminative code and provide a fast speed of our hashing. Extensive experiments are carried out to validate the performances of our hashing. Classification comparison shows that our hashing is better than some state-of-the-art algorithms. Computational time comparison illustrates that our hashing outperforms some compared algorithms based on dimensionality reduction. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Principal component analysis

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GrayINT – Detection and Localization of Gray Failures via Hybrid In-band network Telemetry

GrayINT – Detection and Localization of Gray Failures via H...

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Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Kuichao Zhang Wei Su Huiling Shi Kai Zhang Wei Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China National Engineering Laboratory for Next Generation Internet Interconnection Devices (NGIID) School of Electronics and Information Engineering Beijing Jiaotong University Beijing China

As the underlying infrastructure, data center networks (DCNs) provide an important platform for cloud computing and next-generation networks. However, due to the influence of many factors, such as network, hardware, and software, various failures may occur during the operation of the DCN, which brings great challenges to its reliability. Particularly, a more insidious type of network failure, known as gray failure, presents a significant threat to system stability. In response to this challenge, in this work, we propose GrayINT, a gray failure detection and localization system that leverages In-band network Telemetry (INT) technology to monitor all paths within the data center network in real-time. To verify the feasibility of our design, we build a fat-tree data center network on a virtual network testbed. A large number of experimental results demonstrate that our system can monitor device and link status in real-time, achieving gray failure detection and localization.

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Active Power Control for Off-Shore Wind Turbines Against FDI Attack

Active Power Control for Off-Shore Wind Turbines Against FDI...

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IEEE International Conference on Communications in China Workshops (ICCC)

作者： Xuguo Jiao Hao Luo Liuyang Jiang Lin Wang Xin Wang Zuqiang Huang State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China School of Information and Control Engineering Qingdao University of Technology Qingdao China Zhejiang Windey Co. Ltd. Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Civil Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

Recently, offshore wind power has rapidly developed due to its low environmental interference and abundant wind resources. However, due to its remote geographical location and high demand for communication infrastructure, off-shore wind turbines (OWTs) are vulnerable to potential threats from false data injection (FDI) attack. FDI attack can interfere with active power control strategies by tampering with the rotor speed data measured by sensors and pose a threat to the stability of the power grid. To ensure that OWTs can accurately track the active power instructions set by wind farm's control center under FDI attack, this paper proposes a rotor speed control (RSC) strategy based on Kalman filtering technology (RSC- KF). This strategy can estimate the rotor speed of the wind turbine in real-time, and then a residual detection and compensation mechanism is designed to effectively detect and correct attacked rotor speed information caused by FDI attack. Further, based on the corrected rotor speed, the RSC method is developed, which can flexibly switch control strategies according to different rotor speed values, ensuring the efficient operation of OWTs under different wind speed conditions. The proposed approach's effectiveness is proved via simulations conducted on the OpenFAST platform.

关键词： Filtering Velocity control Power control Rotors Power system stability Stability analysis Real-time systems Wind turbines Kalman filters Cyberattack

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HCF-Net: Hierarchical Context Fusion network for Infrared Small Object Detection

arXiv

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arXiv 2024年

作者： Xu, Shibiao Zheng, ShuChen Xu, Wenhao Xu, Rongtao Wang, Changwei Zhang, Jiguang Teng, Xiaoqiang Li, Ao Guo, Li Artificial Intelligence Beijing University of Posts and Telecommunications China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China School of Computer Science and Technology Harbin University of Science and Technology China

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://***/zhengshuchen/HCFNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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