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

作者： Fang Nan Feng Tian Ni Zhang Nian Liu Haonan Miao Guang Dai Mengmeng Wang Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Automation Northwestern Polytechnical University Xi’an China Computer Vision Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi UAE SGIT AI Lab State Grid Corporation of China Zhejiang University of Technology Hang Zhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Previous methods often utilize CLIP semantic classifiers with class names for zero-shot object counting. However, they ignore crucial density and depth knowledge for counting tasks. Thus, we propose a density-aware and depth-aware prompt counting model, which captures density information via learning density-aware prompts based on density-aware contrastive loss and incorporates depth guidance with predefined depth-aware prompts. To facilitate the training process, we design two strategies for standard counting loss and the contrastive loss, where the former prioritizes larger and sparser objects initially, gradually focusing on smaller and denser objects, and the latter adopts coarse-to-fine density learning. Besides, we construct a dataset named LVIS-372 with more real-world scenarios and balanced instance distribution compared to existing ones. Finally, the experimental results demonstrate the effectiveness of our proposed method.

关键词： Training Visualization Semantics Focusing Signal processing Acoustics Power capacitors Speech processing Standards

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： Data fusion

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Cell Manipulation Attack Against Onion Services

Cell Manipulation Attack Against Onion Services

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Jianjun Lin Yinliang Yue Yijing Zhang Xiaoyu Liu XiaoDu Yang Weilin Gai Bo Sun Chinese Academy of Sciences Institute of Information Engineering Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Institute of Software Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team/Coordination Center of China (CNCERT/CC) Beijing China

In this study, we proposed OREA-OS, a cell manipulation attack devised to compromise Onion Services. Through the manipulation of cells, two collaborating nodes within a circuit exchange information to unveil the anonymity of the targeted Onion Service. We conducted an analysis of existing de-anonymization scenarios and effectively addressed the associated challenges, thereby achieving successful de-anonymization of Onion Services. To assess the efficacy of our attacks, we conducted experiments on both private and public Tor networks. The experimental results validate the effectiveness of our approach, as we accomplished 100% accurate identification of the relationship between domain names and Internet Protocol addresses, with no false positives. Within our simulated private network, the recall rate neared 100 %. On the public Tor network, the de-anonymization rate (98.6 %) exceeded the successful download rate (98.2 %). Furthermore, 99.2 % of success attacks took less than 10 seconds to confirm.

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A Lightweight Authentication Protocol for Mobile RFID

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International Journal of network security 2024年第2期26卷 190-199页

作者： Pan, Tao Zuo, Kai-Zhong Wang, Tao-Chun Deng, Chun-Hong College of Internet and Communication Anhui Technical College of Mechanical and Electrical Engineering No.16 Wenjin West Rd. Yijiang Dist. Anhui Province Wuhu City241002 China College of Computer and Information Anhui Normal University China Anhui Key Laboratory of Network and Information Security No.1 Beijing East Rd. Jinghu Dist. Anhui Province Wuhu City241000 China

Recently, Radio Frequency Identification (RFID) has been widely used. A lightweight security authentication protocol for mobile RFID is proposed to aim at the problems of high complexity and system security in existing mobile RFID authentication protocols. The tag identity is encrypted by the matrix’s arrangement and combination of column vectors based on column pseudonyms, which can effectively reduce tag computational complexity. The tag is required to mainly store its identity and shared keys, which can effectively reduce tag storage complexity. During the authentication process, the proposed protocol generates dynamic authentication keys without changing shared keys, which are different in each session. The protocol uses XOR operation to transfer privacy data based on the dynamic authentication keys. This method can effectively reduce the computational complexity of the protocol while protecting privacy data. The protocol requires the receiver to verify the random number in time after receiving information, which can effectively solve the system security problem. Formal proof and analysis results show that the proposed protocol has good security and can resist attacks. The experimental results indicate that the protocol has low complexity, which can effectively reduce the burden of tag operation and storage. It has good value for mobile RFID systems. © (2023), (Femto Technique Co.). All Rights Reserved.

关键词： Authentication

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Robust Resource Allocation for STAR-RIS Assisted SWIPT Systems

arXiv

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arXiv 2024年

作者： Zhu, Guangyu Mu, Xidong Guo, Li Huang, Ao Xu, Shibiao The Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The Engineering Research Center Ministry of Education for Chain Network Convergence Technology China The National Engineering Research Center for Mobile Internet Security Technology Beijing University of Posts and Telecommunications Beijing100876 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted simultaneous wireless information and power transfer (SWIPT) system is proposed. More particularly, an STAR-RIS is deployed to assist in the information/power transfer from a multi-antenna access point (AP) to multiple single-antenna information users (IUs) and energy users (EUs), where two practical STAR-RIS operating protocols, namely energy splitting (ES) and time switching (TS), are employed. Under the imperfect channel state information (CSI) condition, a multi-objective optimization problem (MOOP) framework, that simultaneously maximizes the minimum data rate and minimum harvested power, is employed to investigate the fundamental rate-energy trade-off between IUs and EUs. To obtain the optimal robust resource allocation strategy, the MOOP is first transformed into a single-objective optimization problem (SOOP) via the ǫ-constraint method, which is then reformulated by approximating semi-infinite inequality constraints with the S-procedure. For ES, an alternating optimization (AO)-based algorithm is proposed to jointly design AP active beamforming and STAR-RIS passive beamforming, where a penalty method is leveraged in STAR-RIS beamforming design. Furthermore, the developed algorithm is extended to optimize the time allocation policy and beamforming vectors in a two-layer iterative manner for TS. Numerical results reveal that: 1) deploying STAR-RISs achieves a significant performance gain over conventional RISs, especially in terms of harvested power for EUs;2) the ES protocol obtains a better user fairness performance when focusing only on IUs or EUs, while the TS protocol yields a better balance between IUs and EUs;3) the imperfect CSI affects IUs more significantly than EUs, whereas TS can confer a more robust design to attenuate these effects. Copyright © 2024, The Authors. All rights reserved.

关键词： Resource allocation

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Backdoor Defense with Machine Unlearning

arXiv

引用

arXiv 2022年

作者： Liu, Yang Fan, Mingyuan Chen, Cen Liu, Ximeng Ma, Zhuo Wang, Li Ma, Jianfeng Xi'An China Shaanxi Key Laboratory of Network and System Security Xidian University Xi'An China College of Computer and Data Science Fuzhou University Fuzhou China School of Data Science and Engineering East China Normal University Shanghai China Ant Group Hangzhou China

Backdoor injection attack is an emerging threat to the security of neural networks, however, there still exist limited effective defense methods against the attack. In this paper, we propose BAERASER, a novel method that can erase the backdoor injected into the victim model through machine unlearning. Specifically, BAERASER mainly implements backdoor defense in two key steps. First, trigger pattern recovery is conducted to extract the trigger patterns infected by the victim model. Here, the trigger pattern recovery problem is equivalent to the one of extracting an unknown noise distribution from the victim model, which can be easily resolved by the entropy maximization based generative model. Subsequently, BAERASER leverages these recovered trigger patterns to reverse the backdoor injection procedure and induce the victim model to erase the polluted memories through a newly designed gradient ascent based machine unlearning method. Compared with the previous machine unlearning solutions, the proposed approach gets rid of the reliance on the full access to training data for retraining and shows higher effectiveness on backdoor erasing than existing fine-tuning or pruning methods. Moreover, experiments show that BAERASER can averagely lower the attack success rates of three kinds of state-of-the-art backdoor attacks by 99% on four benchmark datasets. © 2022, CC BY-NC-ND.

关键词： network security

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Fault Diagnosis for Rolling Bearing Based on RF-LSTM

Fault Diagnosis for Rolling Bearing Based on RF-LSTM

引用

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Qiuting Li Xiuqing Wang Yunpeng Yang Ruiyi Wang Feng Lv College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China

ISBN: (纸本)9781665401166

In the era of big data, it is of great significance to study data-driven fault diagnostic methods for equipment in complex industrial environments. In this paper, a data-driven fault diagnostic method for rolling bearing based on RF-LSTM is proposed, which combines random forest (RF) algorithm with long and short term memory (LSTM) networks. RF is used to select sensor data’s features, which have great influence on rolling bearing fault diagnosis, and LSTM is used for prediction rolling bearing’s working states. The proposed method is used for fault diagnosis of the Case Western Reserve University (CWRU) bearing dataset and the experimental results validated the effectiveness of the proposed RF-LSTM model. Experimental results also show that the performance of the RFLSTM model is better than that of RF, LSTM, RF-RF, PCA-RF, PCA-LSTM models.

关键词： Fault diagnosis Radio frequency Fault detection Time series analysis Rolling bearings Forestry Predictive models

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Fault-Tolerant Differential Privacy Routing of Human-Cyber-Physical Fusion Systems for Large Language Models security

引用

IEEE Internet of Things Journal 2025年

作者： Lin, Limei Huang, Yanze Wang, Xiaoding Garg, Sahil Moussa, Sherif Alrashoud, Mubarak Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China École de technologie supérieure Electrical Engineering Department MontréalQCH3C 1K3 Canada Canadian University School of Engineering Applied Science and Technology Dubai United Arab Emirates Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India Canadian University School of Engineering Applied Science and Technology Dubai United Arab Emirates Riyadh11543 Saudi Arabia

The rapid proliferation of Internet of Things (IoT) systems has introduced complex networks of interconnected devices, computational resources, and web-based communication infrastructure. Privacy protection in IoT data routing is critical to enabling secure deployment of large language models (LLMs) for processing distributed sensor data, user queries, and device-generated content. However, IoT environments inherently involve heterogeneous devices, dynamic network topologies, and resource-constrained nodes, complicating the design of privacy-preserving routing mechanisms that simultaneously ensure reliability across diverse communication layers. To address these challenges, we propose an innovative FtPR (Fault-tolerant Privacy Routing) model based on secure multiparty computing mechanism, which enables secure and efficient data fusion and transmission in IoT networks. FtPR establishes a novel connection between IoT device clusters and data center network architecture AQDNn routers, leveraging the hierarchical architecture of AQDNn to construct completely independent spanning trees (CIST). By exploiting the non-overlapping paths between nodes in distinct CISTs, FtPR achieves fault-tolerant routing while maintaining privacy guarantees. Building on this framework, we introduce a secure multiparty computing mechanism to perturb link weights in the AQDNn. This ensures that link weights across different CISTs adhere to constrained ranges, preventing adversarial inference of routing paths. Each node operates with localized knowledge of its connected link weights, eliminating the need for global network visibility. Consequently, even if malicious actors compromise one or multiple nodes, they cannot reconstruct end-to-end communication paths, thereby preserving route anonymity. Experimental results demonstrate that FtPR improves IoT network performance and security, reducing misclassification rates and marginal release score compared to state-of-the-art methods. © 2014 IEEE.

关键词： Anonymity

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High-Dimensional Quantum Secure Multiparty Summation with Random Decoy Particles

SSRN

引用

SSRN 2022年

作者： Song, Xiuli Yang, Shuai Deng, Hongyao Liao, Jinwei Wu, Tao School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing400065 China Henan Key Laboratory of Network Cryptography Technology Henan450001 China School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer Engineering Yangtze Normal University Chongqing408100 China

Quantum secure multiparty summation (QSMS) calculates the sum of the privacy inputs of multiple participants without exposing these inputs. The current QSMS protocols mostly employ the fixed bases of decoy particles to enhance the security of information transmission. Unfortunately, this measure increases the risk of eavesdropper guessing the fixed bases. In this paper, a high-dimensional quantum secure multiparty summation with random decoy particles (HQSMS-RDP) is proposed to remedy the above-mentioned security deficiencies. In HQSMS-RDP, each participant performs the general Pauli operator on the received message particle to embed his own secret integer and random number into it, and then sends the transformed message particle and random decoy particles to the next participant. Moreover, each participant needs to neither prepare the decoy particles nor measure them during every transmission route. These decoy particles are chosen from a set of random single particles, so that an eavesdropper cannot deterministically guess the specific measurement bases for measuring them, resulting in the exposure probability of the eavesdropper eavesdropping on them approaching 1. The performance analysis shows that the resource consumption of HQSMS-RDP is lower than that of other similar QSMS protocols. The results simulated on the IBM cloud platform prove the correctness of HQSMS-RDP. © 2022, The Authors. All rights reserved.

关键词： security of data

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Shellshock Bash Vulnerability Modeling Analysis Based on Petri Net

Shellshock Bash Vulnerability Modeling Analysis Based on Pet...

引用

International Conference on networking and network Applications (NaNA)

作者： Liumei Zhang Xi Deng Yichuan Wang School of Computer Science Xi’an Shiyou University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technolog Xi’an China

The enhancement of Internet connectivity and the increase of information transmission speed yield the increasing frequency of network attacks. The Shellshock attackers often exploit bash vulnerability to read the content behind the function definition when importing environment variable functions. Then, malicious scripts can be executed in systems and servers, which compromises everything. Therefore, this paper proposes a formal modeling analysis method for the Shellshock Bash basis and automates the analysis of the patched position of the model. The relationship between the established model and the actual attack process is also discussed, which is a feasible reference for exploring unknown vulnerabilities and the location of corresponding patches.

关键词： Analytical models Firing Petri nets Information processing Tools Software Internet

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