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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Huang, Xuwen Chen, Yonghong Li, Zhiqiang Zhan, Teng School of Computer Science and Technology Huaqiao University Xiamen Fujian China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen Fujian China

ISBN: (纸本)9798400700705

Network covert timing channels (NCTCs) utilize Inter-Packet Delay (IPD) encoding to hide data. It can be used for spreading malware and data leakage, posing severe threats to network security. With the increasing risk, the research on NCTC detection has become an important and urgent task. However, the detection methods based on IPD statistics are only effective for few types of channels and require large IPD sampling samples. The recent ML-based detection methods have multiple limitations due to their coarse-grained feature extraction method. In this paper, we propose a multi-channel image transformation method for extracting IPD features and select the lightweight network MobileVit for detection. We encode IPD one-dimensional time-series data into Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence plot (RP) images and stack them into dual-channel and three-channel images. After image transformation, we compare mainstream image classification networks and a self-built CNN. Experimental results show that our feature extraction image classification is more effective than the existing IPD extraction image transformation method. The MobileVit network shows better detection performance and accuracy, requiring fewer IPD sampling samples for detection windows. © 2023 ACM.

关键词： Sampling

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LAPAA: A Secure and Lightweight Authentication Protocol for Edge Computing Built on ASCON Algorithm 6

LAPAA: A Secure and Lightweight Authentication Protocol for ...

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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Zhang, Linghui Zhou, Changli Wen, Jingliang HuaQiao University College of Computer Science and Technology Xiamen361021 China HuaQiao University Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen361021 China

ISBN: (纸本)9798350388374

Facing the challenges of next-generation network security, Mobile Edge Computing (MEC) serves as a decentralized model designed to meet the growing demands for data processing and reduce latency, making its security increasingly critical. However, with the rapid development of edge computing technology, traditional authentication protocols have become inadequate due to their complexity, insufficient protection of user anonymity, and neglect of resource constraints. These issues are at the core of current network security challenges. In response, this paper innovatively proposes LAPAA - a secure and lightweight authentication protocol for Edge computing built on ASCON algorithm. LAPAA effectively addresses the problems found in previous solutions and protects user privacy through anonymity by combining the symmetric encryption algorithm ASCON with asymmetric elliptic curve cryptography (ECC). It employs clever parameter and computation strategies to maintain key consistency between edge devices and MEC servers, while avoiding the leakage of users' real identities. Furthermore, due to the lightweight nature of the ASCON algorithm itself, LAPAA provides superior low-latency performance for the protocol. Additionally, through simulation verification and informal security analysis using the Scyther tool, LAPAA demonstrates its strong defense against various malicious threats and comparative evaluations with other protocols highlight LAPAA's significant advantages in various aspects. Collectively, this research offers a secure and streamlined authentication strategy for MEC contexts, underscoring LAPAA's inventive and groundbreaking approach to overcoming prevalent obstacles. © 2024 IEEE.

关键词： Authentication Protocol

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ConTra: A Covert Timing Channel Detection Approach for Little Covert Information in a Network 23

ConTra: A Covert Timing Channel Detection Approach for Littl...

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Li, Zhiqiang Chen, Yonghong Teng, Zhan Huang, Xuwen School of Computer Science and Technology Huaqiao University Xiamen Fujian China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen Fujian China

ISBN: (纸本)9798400700705

A covert timing channel is a technical means that enables information hiding and covert communication. Due to the fact that covert timing channels can elude detection by security defence measures, they offer a substantial security risk to the information of Internet users when they are exploited for illicit reasons. The attacker sends at a low rate to ensure the stealthiness of the covert timing channel communication process, making the number of inter-arrival time encoded as covert information much less than the normal number of inter-arrival time, resulting in the low detection accuracy of existing detection methods. In this paper, we propose a covert timing channel detection method based on one-dimensional convolution and self-attention mechanism. The method begins with local feature extraction of the input inter-arrival time sequence by a one-dimensional convolutional layer, then characterizes the correlation between each inter-arrival time by a self-attention mechanism in the encoder, and finally through the full connection layer to produce the type output. In this study, experimental results on a public dataset and 16 self-built datasets demonstrate that the detection method delivers optimal detection results and efficient detection of covert information in low-volume communications. © 2023 ACM.

关键词： Optimal detection

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Tile selection method based on error minimization for photomosaic image creation

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Frontiers of Computer Science 2021年第3期15卷 165-172页

作者： Hongbo ZHANG Xin GAO Jixiang DU Qing LEI Lijie YANG Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen 361021China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen 361021China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055China

Photomosaic images are composite images composed of many small images called *** its overall visual effect,a photomosaic image is similar to the target image,and photomosaics are also called“montage art”.Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic *** solve these problems and generate a photomosaic image in this study,we propose a tile selection method based on error minimization.A photomosaic image can be generated by partitioning the target image in a rectangular pattern,selecting appropriate tile images,and then adding them with a weight *** on the principles of montage art,the quality of the generated photomosaic image can be evaluated by both global and local *** the proposed framework,via an error function analysis,the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error ***,the weight coefficient of the image superposition can be used to adjust the ratio of the global and local ***,to verify the proposed method,we built a new photomosaic creation dataset during this *** experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.

关键词： photomosaic image tile image target image error minimization mean absolute error

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Association Pattern-aware Fusion for Biological Entity Relationship Prediction 38

Association Pattern-aware Fusion for Biological Entity Relat...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jia, Lingxiang Ying, Yuchen Feng, Zunlei Zhong, Zipeng Yao, Shaolun Hu, Jiacong Duan, Mingjiang Wang, Xingen Song, Jie Song, Mingli State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Bangsheng Technology Co Ltd. China

Deep learning-based methods significantly advance the exploration of associations among triple-wise biological entities (e.g., drug-target protein-adverse reaction), thereby facilitating drug discovery and safeguarding human health. However, existing researches only focus on entity-centric information mapping and aggregation, neglecting the crucial role of potential association patterns among different entities. To address the above limitation, we propose a novel association pattern-aware fusion method for biological entity relationship prediction, which effectively integrates the related association pattern information into entity representation learning. Additionally, to enhance the missing information of the low-order message passing, we devise a bind-relation module that considers the strong bind of low-order entity associations. Extensive experiments conducted on three biological datasets quantitatively demonstrate that the proposed method achieves about 4%-23% hit@1 improvements compared with state-of-the-art baselines. Furthermore, the interpretability of association patterns is elucidated in detail, thus revealing the intrinsic biological mechanisms and promoting it to be deployed in real-world scenarios. Our data and code are available at https://***/hry98kki/PatternBERP. © 2024 Neural information processing systems foundation. All rights reserved.

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GROOT: Generating Robust Watermark for Diffusion-Model-Based Audio Synthesis 24

GROOT: Generating Robust Watermark for Diffusion-Model-Based...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Weizhi Li, Yue Lin, Dongdong Tian, Hui Li, Haizhou Huaqiao University Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen China

ISBN: (纸本)9798400706868

Amid the burgeoning development of generative models like diffusion models, the task of differentiating synthesized audio from its natural counterpart grows more daunting. Deepfake detection offers a viable solution to combat this challenge. Yet, this defensive measure unintentionally fuels the continued refinement of generative models. Watermarking emerges as a proactive and sustainable tactic, preemptively regulating the creation and dissemination of synthesized content. Thus, this paper, as a pioneer, proposes the generative robust audiowatermarking method (Groot), presenting a paradigm for proactively supervising the synthesized audio and its source diffusion models. In this paradigm, the processes of watermark generation and audio synthesis occur simultaneously, facilitated by parameter-fixed diffusion models equipped with a dedicated encoder. The watermark embedded within the audio can subsequently be retrieved by a lightweight decoder. The experimental results highlight Groot's outstanding performance, particularly in terms of robustness, surpassing that of the leading state-of-the-art methods. Beyond its impressive resilience against individual post-processing attacks, Groot exhibits exceptional robustness when facing compound attacks, maintaining an average watermark extraction accuracy of around 95%. Our audio samples are available at https://***/. © 2024 ACM.

关键词： Audio watermarking

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A2PO: Towards Effective Offline Reinforcement Learning from an Advantage-aware Perspective 38

A2PO: Towards Effective Offline Reinforcement Learning from ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qing, Yunpeng Liu, Shunyu Cong, Jingyuan Chen, Kaixuan Zhou, Yihe Song, Mingli College of Computer Science and Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Offline reinforcement learning endeavors to leverage offline datasets to craft effective agent policy without online interaction, which imposes proper conservative constraints with the support of behavior policies to tackle the out-of-distribution problem. However, existing works often suffer from the constraint conflict issue when offline datasets are collected from multiple behavior policies, i.e., different behavior policies may exhibit inconsistent actions with distinct returns across the state space. To remedy this issue, recent advantage-weighted methods prioritize samples with high advantage values for agent training while inevitably ignoring the diversity of behavior policy. In this paper, we introduce a novel Advantage-Aware Policy Optimization (A2PO) method to explicitly construct advantage-aware policy constraints for offline learning under mixed-quality datasets. Specifically, A2PO employs a conditional variational auto-encoder to disentangle the action distributions of intertwined behavior policies by modeling the advantage values of all training data as conditional variables. Then the agent can follow such disentangled action distribution constraints to optimize the advantage-aware policy towards high advantage values. Extensive experiments conducted on both the single-quality and mixed-quality datasets of the D4RL benchmark demonstrate that A2PO yields results superior to the counterparts. Our code is available at https://***/Plankson/A2PO. © 2024 Neural information processing systems foundation. All rights reserved.

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Design and Implementation of a Vision- and Grating-Sensor-Based Intelligent Unmanned Settlement System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 254-264页

作者： Zhang, Hong-Bo Zhou, Yi-Zhong Dong, Li-Jia Lei, Qing Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361000 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition The Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

In this article, a new vision- and grating-sensor-based intelligent unmanned settlement (IUS) system is proposed for convenience stores to automatically recognize the shopping behavior of customers, record their identities, and generate invoices. First, we design a new IUS architecture, which includes a shelf module and exit module. To achieve automatic settlement for each customer, a shopping event detection method is proposed. In this method, a vision-based human pose estimation algorithm is used to detect a human form standing in front of a shelf. The hand actions of each customer are detected by a grating sensor, and an image recognition method based on a convolutional neural network (CNN) is applied to recognize the items in the hands of customers. To reduce the image annotation workload, we propose a semisupervised training method for the recognition network. Based on hand action detection and item recognition, a shopping event recognition method is designed for the system, and a facial image of the customer corresponding to each shopping behavior is captured. Finally, each detected shopping event is added to the invoice of the corresponding customer via a facial recognition method. To verify the effectiveness of the proposed IUS system, we have built a handheld item image dataset and a shopping event dataset for an unmanned convenience store. The experimental results show that the proposed system can accurately recognize shopping behaviors and generate invoices. © 2020 IEEE.

关键词： Sales

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Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting 38

Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shang, Zongjiang Chen, Ling Wu, Binqing Cui, Dongliang State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University China

Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e.g., rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4.56%, 10.38%, and 4.97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https://***/shangzongjiang/Ada-MSHyper. © 2024 Neural information processing systems foundation. All rights reserved.

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Public auditing for real‑time medical sensor data in cloud‑assisted HealthIIoT system

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Frontiers of Optoelectronics 2022年第3期15卷 1-14页

作者： Weiping Ye Jia Wang Hui Tian Hanyu Quan College of Computer Science and Technology Huaqiao UniversityXiamen 361021China Wuhan National Laboratory for Optoelectronics Wuhan 430074China Xiamen Key Laboratory of Data Security and Blockchain Technology Xiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen 361021China

With the advancement of industrial internet of things(IIoT),wireless medical sensor networks(WMSNs)have been widely introduced in modern healthcare systems to collect real-time medical data from patients,which is known as *** the limited computing and storage capabilities of lightweight HealthIIoT devices,it is necessary to upload these data to remote cloud servers for storage and ***,there are still some serious security issues within outsourcing medical sensor data to the *** of the most signifcant challenges is how to ensure the integrity of these data,which is a prerequisite for providing precise medical diagnosis and *** meet this challenge,we propose a novel and efcient public auditing scheme,which is suitable for cloud-assisted HealthIIoT ***,to address the contradiction between the high real-time requirement of medical sensor data and the limited computing power of HealthIIoT devices,a new online/ofine tag generation algorithm is designed to improve preprocessing efciency;to protect medical data privacy,a secure hash function is employed to blind the data *** formally prove the security of the presented scheme,and evaluate the performance through detailed experimental comparisons with the state-of-the-art *** results show that the presented scheme can greatly improve the efciency of tag generation,while achieving better auditing performance than previous schemes.

关键词： Healthcare industrial internet of things(HealthIIoT) Medical sensor data Online/ofine signature Public auditing

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