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27th IEEE International Conference on Parallel and Distributed systems, ICPADS 2021

作者： Jiang, Yu Xu, Xiaolong Lin, Kunda Duan, Weihua Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665408783

In order to select a composition scheme that meets user's needs and high performance from large-scale web services in the edge cloud, this paper proposes a trusted service composition optimization scheme called TSC-ECFA for edge cloud, which applies the predation strategy to the firefly algorithm (FA) and divides the services with the same input and output into one category, reducing the number of combinations to search for feasible solutions. The cotangent chaos theory is used to generate the initial firefly population and disturb the location of the individual to improve the overall search efficiency of FA. This paper also improves the step factor and attractiveness formula of FA to give full play to the detection ability of the step factor in the early stage of the algorithm, and balance the local search and the global search. Considering that the QoS attribute values of the service are vulnerable to be tampered with, the blockchain is used to store the QoS attribute values to ensure the tamper-proof and reliability of the QoS attribute value. Finally, simulation experiments compare the number of iterations and optimization of the three algorithms. The experimental results show that the comprehensive performance of TSC-ECFA is better than other algorithms. © 2021 IEEE.

关键词： Quality of service

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JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

JSRevealer: A Robust Malicious JavaScript Detector against O...

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International Conference on Dependable systems and Networks (DSN)

作者： Kunlun Ren Weizhong Qiang Yueming Wu Yi Zhou Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to the convenience and popularity of Web applications, they have become a prime target for attackers. As the main programming language for Web applications, many methods have been proposed for detecting malicious JavaScript, among which static analysis-based methods play an important role because of their high effectiveness and efficiency. However, obfuscation techniques are commonly used in JavaScript, which makes the features extracted by static analysis contain many useless and disguised features, leading to many false positives and false negatives in detection results. In this paper, we propose a novel method to find out the essential features related to the semantics of JavaScript code. Specifically, we develop JS-Revealer, a robust, effective, scalable, and interpretable detector for malicious JavaScript. To test the capabilities of JSRevealer, we conduct comparative experiments with four other state-of-the-art malicious JavaScript detection tools. The experimental results show that JSRevealer has an average F1 of 84.8% on the data obfuscated by different obfuscators, which is 21.6%, 22.3%, 18.7%, and 22.9% higher than the tools CUJO, ZOZZLE, JAST, and JSTAP, respectively. Moreover, the detection results of JSRevealer can be interpreted, which can provide meaningful insights for further security research.

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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Multiple Targets Directed Greybox Fuzzing

arXiv

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arXiv 2022年

作者： Liang, Hongliang Cheng, Xianglin Liu, Jie Li, Jin Trusted Software and Intelligent System Lab Beijing University of Posts and Telecommunications Beijing China Nation Key Laboratory of Science and Technology on Information System Security Beijing China

Directed greybox fuzzing (DGF) can quickly discover or reproduce bugs in programs by seeking to reach a program location or explore some locations in order. However, due to their static stage division and coarse-grained energy scheduling, prior DGF tools perform poorly when facing multiple target locations (targets for short). In this paper, we present multiple targets directed greybox fuzzing which aims to reach multiple programs locations in a fuzzing campaign. Specifically, we propose a novel strategy to adaptively coordinate exploration and exploitation stages, and a novel energy scheduling strategy by considering more relations between seeds and target locations. We implement our approaches in a tool called LeoFuzz and evaluate it on crash reproduction, true positives verification, and vulnerability exposure in real-world programs. Experimental results show that LeoFuzz outperforms six state-of-the-art fuzzers, i.e., QYSM, AFLGo, Lolly, Berry, Beacon and WindRanger in terms of effectiveness and efficiency. Moreover, LeoFuzz has detected 23 new vulnerabilities in real-world programs, and 11 of them have been assigned CVE IDs. Copyright © 2022, The Authors. All rights reserved.

关键词： Location

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Model-based Offline Policy Optimization with Adversarial Network

arXiv

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arXiv 2023年

作者： Yang, Junming Chen, Xingguo Wang, Shengyuan Zhang, Bolei Jiangsu Key Laboratory of Big Data Security and Intelligent Processing China School of Computer Science Nanjing University of Posts and Telecommunications China School of Modern Posts Nanjing University of Posts and Telecommunications China

Model-based offline reinforcement learning (RL), which builds a supervised transition model with logging dataset to avoid costly interactions with the online environment, has been a promising approach for offline policy optimization. As the discrepancy between the logging data and online environment may result in a distributional shift problem, many prior works have studied how to build robust transition models conservatively and estimate the model uncertainty accurately. However, the over-conservatism can limit the exploration of the agent, and the uncertainty estimates may be unreliable. In this work, we propose a novel Model-based Offline policy optimization framework with Adversarial Network (MOAN). The key idea is to use adversarial learning to build a transition model with better generalization, where an adversary is introduced to distinguish between in-distribution and out-of-distribution samples. Moreover, the adversary can naturally provide a quantification of the model’s uncertainty with theoretical guarantees. Extensive experiments showed that our approach outperforms existing state-of-the-art baselines on widely studied offline RL benchmarks. It can also generate diverse in-distribution samples, and quantify the uncertainty more accurately. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Continuous Verification Mechanism for Clients in Federated Unlearning to Defend the Right to be Forgotten

A Continuous Verification Mechanism for Clients in Federated...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Shanshan Chen Jun Liu Yuying Li Yamin Li Yang Yang Gaoyang Liu Chen Wang Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In Federated Learning (FL), the regulatory need for the "right to be forgotten" requires efficient Federated Unlearning (FU) methods, which enable FL models to unlearn appointed training data. Associating with the emergence FU, verifying the performance of FU plays a critical role in evaluating the consistency FU methods, in case of the unexpected degradation of the FL model. Though well developed, none of the existing verification methods in FU stands for the clients who opt out of the FL process, which is a universal demand in FL. More specifically, after the clients quit the FL cooperation, they can no longer verify whether the FL model unlearns their data after the FL keeps training for several rounds. To this end, we introduce a continuous verification mechanism for FL clients, called Backdoor Attack-based Forgetting Verification (BAFV). Inspired by backdoor attack, BAFV embedded a persistent mark for the client that proposes to leave, with the intention that the client still has the right to verify of FU after leaving the FL cooperation for a relatively long period. Extensive experiments across diverse FU environments and datasets demonstrated that our method maintains the accuracy of model and provides clients with a continuous verification mechanism to defend their rights. Our code of BAFV is publicly available at: https://***/paper-liu/***.

关键词： Training Privacy Distributed processing Accuracy Federated learning Training data Feature extraction data models Servers Protection

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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RKSeg+: make full use of Runge–Kutta methods in medical image segmentation

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Multimedia systems 2024年第2期30卷 65-65页

作者： Zhu, Mai Fu, Chong Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. Skip connections and multiple scaling are often used in common models but lack mathematical explanations. RKSeg interprets and uses skip connections based on the RK methods. Therefore, RKSeg greatly improves segmentation efficiency. However, it does not explain and use multiple scales from a dynamical system perspective but only inherits the multi-scale scheme of existing models. We compensate for this shortcoming by interpreting and using multiple scales based on the RK methods. In addition, the network structure also limits the excellent image classification networks as the backbones of RKSegs. Therefore, we modify the network structure to support more image classification networks as backbones. As a result, we propose a novel network structure RKSeg+. Our proposed RKSeg+ achieves better segmentation results with fewer parameters than RKSeg. Furthermore, RKSeg+, well configured with few parameters, outperforms state-of-the-art models on six of the ten organ datasets in the Medical Segmentation Decathlon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

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Few-Shot Camouflaged Object Segmentation

Few-Shot Camouflaged Object Segmentation

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International Joint Conference on Neural Networks (IJCNN)

作者： Ziqiu Wang Yuying Li Yang Yang Yamin Li Gaoyang Liu Key Laboratory of Intelligent Sensing System and Security (Ministry of Education) School of Artificial Intelligence Hubei University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the domain of computer vision, Camouflaged Object Segmentation (COS) is a crucial task aimed at identifying objects that blend into their surroundings, with applications spanning diverse sectors such as military, medical, and beyond. Traditional COS techniques, which primarily depend on supervised learning, necessitate large-scale labeled datasets. However, the acquisition of sufficient camouflage images for such purposes is often constrained due to their scarcity and the high cost of manual annotation. Additionally, these conventional methods frequently struggle to generalize to novel, unseen classes. In response to these challenges, this paper proposes the Camouflage Few-Shot (CAMFS) framework, an innovative approach integrating few-shot learning into COS. The CAMFS framework comprises two main components: the Camouflaged-Meta module, which converts the semantic information of camouflaged objects into compact feature vectors to facilitate knowledge transfer from support to query images; and the Camouflaged-Base module, focused on refining edge detection and enhancing the contrast between foreground and background elements. To overcome the limitations of existing COS datasets, which are primarily designed for supervised learning, we have developed the COS-FSS dataset, the first public few-shot COS dataset. It is based on the COD10K dataset and supplemented with approximately 3200 additional camouflage images. We conducted extensive evaluations of our CAMFS framework on the COS-FSS dataset. Compared to existing COS models, CAMFS demonstrates an average improvement of 5.7% in S α and 14.02% in $F_\beta ^w$, while against few-shot segmentation models, it achieves a 5.97% increase in m-IoU. The dataset and additional resources are available at https://***/CAM-FSS/FSS-COD.

关键词： Image segmentation Military computing Supervised learning Semantics Refining Neural networks Object segmentation

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A Triangular Stable Node Network based on Self-supervised Learning for personalized prediction

A Triangular Stable Node Network based on Self-supervised Le...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Qing Gao, Qian Fan, Jun Zhang, Zhiqiang 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 Jinan250014 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd. No.1999 Shunhua road Shandong Jinan250101 China

ISBN: (纸本)9798350368741

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN-a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model's effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets. © 2025 IEEE.

关键词： graph contrastive learning graph neural networks recommender systems self-supervised learning

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