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TechRxiv 2022年

作者： Zhang, Chunyong Liu, Bin Fan, Qi Xin, Yang Zhu, Hongliang School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing100876 China College of Information and Network Engineering Anhui Science and Technology University Bengbu233030 China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

Static code vulnerability detection is a critical topic in software security. Existing software analysis methods have a high rate of false positives and false negatives. Researchers are interested in employing deep learning to discover vulnerabilities automatically, thanks to the recent success of deep learning algorithms in other application *** paper aims at the problem of insufficient and effective extraction of syntax and semantics, the issue of data imbalance, and the problem of overlapping feature distributions between vulnerable and non-vulnerable samples. We illustrate how to create models in a more principled way. We build GSM, a systematic vulnerability detection model based on Graph Attention network, Sampling methods, and Metric Learning, one phase for one problem solution. When compared to the state-of-the-art approaches, our method achieves 11.5%, 12.3%, 12.57%, and 7.90% improvement in Precision, Recall, F1-Score, and AUC, respectively. Finally, based on the methods proposed in each stage of this paper, we put forward directions and suggestions for more efficient vulnerability detection tasks in the following research. © 2022, CC BY.

关键词： Learning algorithms

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Gaf-Net: Graph Attention Fusion network for Multi-View Semi-Supervised Classification

SSRN

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SSRN 2023年

作者： Song, Na Du, Shide Wu, Zhihao Zhong, Luying Wang, Shiping School of Computer Science and Technology Hainan University Hainan570228 China School of Mechanical Electrical and Information Engineering Putian University Putian351100 China College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China

Multi-view semi-supervised classification is a typical task to classify data using a small amount of supervised information, which has attracted a lot of attention from researchers in past years. In practice, existing methods focus more on extracting spatial or spectral features using graph convolutional neural networks without considering the diversity and variability of graph structures and the contributions of different views. To address this challenge, a network framework termed graph attention fusion network is proposed, which is comprised of two phases: view-specific feature embedding and graph embedding fusion. In the former feature extraction stage, view-specific feature embedding module can flexibly focus on the neighborhood calculation operation to learn a weight for each neighboring node. In the latter feature fusion stage, the graph embedding fusion module is performed by complementarity and consistency to fuse these embeddings for semi-supervised classification tasks. We conduct extensive experiments in terms of semi-supervised classification on real-world datasets to validate the superiority of the proposed method over various existing state-ofthe-art methods. © 2023, The Authors. All rights reserved.

关键词： Graph neural networks

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Heterogeneous and plaintext checkable signcryption for integrating IoT in healthcare system

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Journal of Systems Architecture 2024年 154卷

作者： Hadabi, Abdalla Qu, Zheng Yeh, Kuo-Hui Chen, Chien-Ming Kumari, Saru Xiong, Hu Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Information Management National Dong Hwa University Hualien974 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

Preserving the confidentiality and integrity of data transmission is paramount in the Internet of Things (IoT)-based healthcare systems. Current encryption techniques that allow plaintext checks primarily serve a specific cryptosystem, lacking the adaptability to work with a diverse system incorporating various cryptographic methods. To address this, we present a unique online/offline heterogeneous signcryption scheme with plaintext checkable encryption (HOOSC-PCE). This approach enables the transition of signcrypted messages from an identity-based cryptosystem (IBC) to a public key infrastructure (PKI) system, improving information interoperability. A key aspect of our scheme is its capacity to allow cloud servers to perform plaintext queries, facilitating efficient data searches using plaintext keywords over encrypted data. Furthermore, the signcryption process is divided into online and offline phases. The online phase handles tasks that require fewer resources, while the offline phase carries out more resource-intensive preparatory tasks. We rigorously tested the HOOSC-PCE scheme's security by proving it secure under the Random Oracle Model (ROM). Meanwhile, compared to similar work, it effectively reduces computation costs by 46.39%, 19.45%, 18.73%, and 13.25% across offline encryption, online encryption, decryption, and search algorithms. The results indicate that the HOOSC-PCE is secure and efficient, confirming its feasibility for IoT-based healthcare systems. © 2024 Elsevier B.V.

关键词： Internet of things

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A Survey on Service Route and Time Prediction in Instant Delivery: Taxonomy, Progress, and Prospects

arXiv

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arXiv 2023年

作者： Wen, Haomin Lin, Youfang Wu, Lixia Mao, Xiaowei Cai, Tianyue Hou, Yunfeng Guo, Shengnan Liang, Yuxuan Jin, Guangyin Zhao, Yiji Zimmermann, Roger Ye, Jieping Wan, Huaiyu The Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing101318 China The School of Computing National University of Singapore Singapore College of Systems Engineering National University of Defense Technology Changsha China The School of Information Science and Engineering Yunnan University Kunming650091 China The Cainiao Network Hangzhou China The Alibaba Group Hangzhou China INTR Thrust and DSA Thrust The Hong Kong University of Science and Technology Guangzhou China

Instant delivery services, such as food delivery and package delivery, have achieved explosive growth in recent years by providing customers with daily-life convenience. An emerging research area within these services is service Route&Time Prediction (RTP), which aims to estimate the future service route as well as the arrival time of a given worker. As one of the most crucial tasks in those service platforms, RTP stands central to enhancing user satisfaction and trimming operational expenditures on these platforms. Despite a plethora of algorithms developed to date, there is no systematic, comprehensive survey to guide researchers in this domain. To fill this gap, our work presents the first comprehensive survey that methodically categorizes recent advances in service route and time prediction. We start by defining the RTP challenge and then delve into the metrics that are often employed. Following that, we scrutinize the existing RTP methodologies, presenting a novel taxonomy of them. We categorize these methods based on three criteria: (i) type of task, subdivided into only-route prediction, only-time prediction, and joint route&time prediction;(ii) model architecture, which encompasses sequence-based and graph-based models;and (iii) learning paradigm, including Supervised Learning (SL) and Deep Reinforcement Learning (DRL). Conclusively, we highlight the limitations of current research and suggest prospective avenues. We believe that the taxonomy, progress, and prospects introduced in this paper can significantly promote the development of this field. © 2023, CC BY.

关键词： Taxonomies

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Beyond 10logm Array Gain: A Beamforming Method Under Non-Gaussian Noise and Multi-Sources

SSRN

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SSRN 2023年

作者： Su, Xiruo Tao, Liurong Ren, Haoran Wu, Bin Ye, Lingyun College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou310027 China Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province School of Earth Sciences Zhejiang University Hangzhou310058 China Zhejiang Provincial Key Laboratory for Network Multimedia Technology Hangzhou310027 China Hainan Institute Zhejiang University Sanya572025 China Explosion and Seismic Sensing Research Center Advanced Technology Institute Zhejiang University Hangzhou310027 China

Researchers have applied deep learning techniques in the direction of arrival domain in various real-world applications, such as traffic positioning and hydro-acoustic detection. However, the effectiveness of these algorithms is dependent on the similarity of training data with actual data, which is challenging to obtain, particularly in hydro-acoustic environments. This is due to the limited array gain (AG) and constantly changing ambient noise. Moreover, the presence of non-Gaussian noise in the background further complicates the identification process, especially lower the basic 10logM AG. Additionally, the majority of existing algorithms are only capable of detecting up to four sources. To tackle this issue, we propose a novel method that employs a diagonal beam spectrum (DBS) feature to suppress non-Gaussian noise and enhance array gain. By utilizing DBS, our method surpasses the theoretical 10logM array gain and achieves an impressive 25logM array gain, while effectively detecting signals as low as -38dB (M denotes the number of sensors).We also use SA-U-NET, an image edge detection network based on DBS, to detect unlimited sound sources and optimize angular positioning. Our proposed method achieves up to 95.67% correctness (4 times better than the best baseline) in experiments , demonstrating the potential of DBS and SA-U-NET. © 2023, The Authors. All rights reserved.

关键词： Beamforming

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An Efficient Image Captioning Method Based on Generative Adversarial networks 4

An Efficient Image Captioning Method Based on Generative Adv...

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4th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2021

作者： Lin, Zhenxian Feng, Feirong Yang, Xiaobao Ding, Chen School of Science Xi'an University of Posts and Telecommunications China School of Telecommunication and Information Engineering Xi'an University of Posts and Telecommunications China School of Computer Science and Technology Northwestern Polytechnical University China School of Computer Science and Technology Xi'an University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9781450384087

Image captioning is usually based on the mainstream encoder-decoder framework, the accuracy of the encoders and the generation capability of the decoders directly affect the quality of image captions. But no matter how the encoders and the decoders are combined, the generated captions will only imitate the ground-truth captions, and its diversity is suppressed. To generate more accurate and diverse captions while exploring the impact of different encoder-decoder frameworks for the quality of the generated captions, we conducted an exploration of an efficient image captioning method based on GAN. Specifically, we use ResNet152, EfficientNet-B0, and EfficientNet-B2 as the encoders to extract image features, GRU or Transformer as the decoders to generate captions, respectively. Based on MS COCO2014 dataset, a large number of experiment results show that the EfficientNet-B2+GRU framework has best performance, training time is reduced, BLUE-4 score increases by 0.6, parameters reduce by 0.66 million, which can be used as an efficient encoder-decoder framework for generating diverse captions. © 2021 ACM.

关键词： Generative adversarial networks

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network Security Situational Awareness Model Based on Threat Intelligence 14th

Network Security Situational Awareness Model Based on Threat...

引用

14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Gao, Hongbin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang05000 China

ISBN: (纸本)9783030898137

In order to deal with the problems that the increasing scale of the network in the real environment leads to the continuous high incidence of network attacks, the threat intelligence was applied to situational awareness, and the situational awareness model based on random game was constructed. Threat perception of the target system was performed by comparing the similarity between the exogenous threat intelligence and the internal security events of the system. At the same time, internal threat intelligence was generated based on the threat information inside the system. In this process, game theory was used to quantify the current network security situation of the system, evaluate the security status of the network. Finally, the prediction of the network security situation was realized. The experimental results show that the network security situation awareness method based on threat intelligence can reflect the changes in the network security situation and predict attack behaviors accurately. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Game theory

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Deep Learning Strategies for Addressing Anomalous Exposure in Image Processing: The FARDBUNet Approach 5

Deep Learning Strategies for Addressing Anomalous Exposure i...

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5th International Conference on High Performance Big data and Intelligent Systems, HDIS 2023

作者： Zhou, Qi Yang, Kai Ke, Zunwang Wang, Gang Zhang, Yugui Jia, Yizhen Cao, Fengcai Ma, Junxiao Liu, Changlin Zhang, Kaijie Wu, Min School of Semiconductor Science and Technology South China Normal University GuangZhou510631 China CGNPC Uranium Industry Development Co. Ltd. Beijing100020 China School of Software Xinjiang University Urumqi830046 China School of Computing and Data Engineering NingboTech University Ningbo315100 China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Co. Ltd Beijing100176 China School of Information Beijing Forestry University Beijing100083 China

ISBN: (纸本)9798350394160

In real-world scenarios, capturing scenes with excessive dynamic range often leads to the partial loss of highlight or dark area information due to irradiance variations and limitations in the capture capabilities of imaging devices caused by their size and material constraints. This phenomenon gives rise to anomalous exposure problems and triggers the loss of crucial initial details, thereby becoming a significant obstacle to achieving high image quality. Despite numerous researchers’ integration of exposure compensation techniques into the imaging process, the anomalous exposure problem remains unsolved, hindering progress in image processing. Consequently, considerable research is valuable in implementing exposure calibration techniques on visual perception and multimedia devices. This dissertation proposes FARDBUNet, a novel end-to-end exposure calibration model designed to address the anomalous exposure problem. A key innovation of FARDBUNet is the incorporation of a fused attention mechanism, enabling the model to prioritize regions needing enhanced correction while accounting for the global exposure characteristics of the image. Additionally, to tackle the challenge of inadequate information availability in the localized areas due to exposure issues, we introduce an atrous residual dense block that efficiently captures local information by expanding the receptive field. Comprehensive tests validate the effectiveness of our suggested model, which performs on par with leading techniques on expansive exposure datasets. ©2023 IEEE.

关键词： Image enhancement

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Focus on the Stability of Large Systems: Toward Automatic Prediction and Analysis of Vulnerability Threat Intelligence

Focus on the Stability of Large Systems: Toward Automatic Pr...

引用

IEEE International Conference on data Science in Cyberspace (DSC)

作者： Shiwen Song Qiong Wu Xin Zheng Peng Wang Yuchen Dou Zhongwen Li Lidong Zhai Institute of Information Engineering Chinese Academy of Sciences BeiJing China Bag Data Academy Zhongke Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665418164

With the increase in the number of users and business volume, the business systems of Internet companies are becoming more and more complex, resulting in a surge in the number of alarms. A large number of dirty alarms add a huge workload to security operations, which indirectly pose a large number of threats to business systems. At present, most systems use the method of accessing third-party Threat Intelligence to assist operators to realize automatic handling of alarms. However, this method has lagging and accuracy problems, making this work always difficult to meet the requirements of fast and accurate. This article proposes a new method for gathering vulnerability Threat Intelligence, which can obtain vulnerability information in advance of security announcements issued by security vendors. By analyzing the vulnerability disclosure process, this method obtains vulnerability information from the original source submitted by open source mail group, of developers. We used NLP technology and XGBoost model to automatically analyze the vulnerability information, and finally generate FINTEL. The experimental result shows that this method has an accuracy of 93%, and can obtain vulnerability information 10h to 7 days before security vendors release. The scope of application covers all open source code repositories and some closed source repositories.

关键词： Machine learning algorithms Scalability Prediction methods Stability analysis Security Surges Research and development

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Larger receptive Field and Context Information for Pose estimation:Larger Gaussian kernel 5

Larger receptive Field and Context Information for Pose esti...

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5th International Conference on High Performance Big data and Intelligent Systems, HDIS 2023

作者： Ma, Junxiao Yang, Kai Ke, Zunwang Wang, Gang Zhang, Yugui Cao, Fengcai Zhou, Qi Fei, Yang School of Information Beijing Forestry University Beijing100083 China CGNPC Uranium Industry Development Co. Ltd. Beijing100020 China School of Software Xinjiang University Urumqi830046 China School of Computing and Data Engineering NingboTech University Ningbo315100 China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Co. Ltd Beijing100176 China School of Semiconductor Science and Technology South China Normal University GuangZhou510631 China

ISBN: (纸本)9798350394160

The field of pose estimation has a wide range of application prospects in various industries in the current era. With the continuous development of deep learning techniques, the effects in the field of human pose estimation are constantly being optimized. However, due to the influence of factors such as occlusion and human posture variability, it is easy to lead to inaccurate identification of keypoints and even keypoints’ missing and matching errors, there is still a lot of optimization space for the effect of pose estimation. In this paper, we propose an improvement to generate heatmaps in the training process: increase the radius of the Gaussian kernel and adjust the smoothness of Gaussian distribution to get a larger receptive field, encourage the model to use more context information in prediction, and control other parameters unchanged on the premise of using heatmap regression. Improve keypoint detection performance by optimizing this item. In the experiment, for the COCO-Keypoint data set, we selected different methods published in recent years, and after our improvement, we obtained better accuracy and performance than the original method, which proves the validity of our method. ©2023 IEEE.

关键词： Gaussian distribution

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