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Industrial Cyber-Physical Systems (ICPS)

作者： Bingdong Wang Junjie Song Liang Wan Youliang Tian Xin Wang Zhenyong Zhang State Key Laboratory of Public Big Data and the College of Computer Science and Technology Guizhou University Guiyang China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan P. R. China

Advanced communication infrastructure makes the power system smarter but also introduces cybersecurity issues. False data injection attack (FDIA) is a kind of cyberattack that can cause significant damage to the power system. To defend against FDIAs, moving target defense (MTD) was recently proposed by perturbing the branch parameters. However, most of the existing research ignores the impact of MTD on power system stability. Considering the system dynamics, we analyze and investigate the impact of MTD on small-signal stability without sacrificing its effectiveness. We present a sufficient condition for maintaining the stability of the power system with MTD. Finally, we evaluate our findings with the IEEE 68-bus power system.

关键词： Sufficient conditions Sensitivity Costs System dynamics Power system stability Cyber-physical systems Stability analysis

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DDoS Attack Detection Based on Information Entropy Feature Extraction in Software Defined networks

DDoS Attack Detection Based on Information Entropy Feature E...

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International Conference on Networking and Network Applications (NaNA)

作者： Wenjiang Ji Yixin Yang Yaling Zhang Yichuan Wang Mengjie Tian Yuan Qiu School of computer science and engineering Xi'an University of technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology

Distributed Denial of Service(DDoS) attacks target the forwarding-control separation feature of Software-Defined Networking(SDN) to launch attacks, causing network disruptions. Therefore security against DDoS attack detection for SDN controllers is the focus of current research. This paper proposes an Extreme Gradient Boosting (XGBoost) DDoS attack detection algorithm based on a combination of information gain and recursive feature elimination algorithms. We evaluated the performance of the method using the CICDDoS2019 dataset. This method works well in multi-classification model for attack detection with an accuracy of 93.41%.

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Fairness of Large Music Models: From a Culturally Diverse Perspective

Fairness of Large Music Models: From a Culturally Diverse Pe...

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IEEE International Conference on Data science in Cyberspace (DSC)

作者： Qinyuan Wang Bruce Gu He Zhang Yunfeng Li School of Music University of Sydney NSW Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 CNPIEC KEXIN Ltd Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

This paper explores the fairness of large music models in generating culturally diverse musical compositions, emphasizing the need for inclusivity and equity in AI-generated music. By analyzing popular datasets such as the Million Song Dataset and the Lakh MIDI Dataset, we identify a significant underrepresentation of non-Western musical elements. To address this, we adapt existing models to incorporate non-Western scales, rhythms, and instruments. The adapted models demonstrate a substantial improvement in generating culturally diverse music. Using AI-based synthetic listeners, we evaluate the impact of these adaptations on listener perception and engagement. Results indicate that music from the adapted models scores higher in terms of enjoyment, novelty, cultural resonance, and overall engagement compared to the original models. Our findings highlight the importance of cultural diversity in enhancing the user experience and ensuring ethical AI practices. The study also discusses challenges, such as dataset limitations and model adaptation complexity, and suggests directions for future research to further promote fairness and inclusivity in music AI. This work contributes to the development of more culturally aware and representative music generation systems, aiming to enrich the global musical landscape and foster greater appreciation of diverse musical traditions.

关键词： Adaptation models Ethics Instruments Data science Rhythm Data models User experience Resonance Cultural differences Artificial intelligence

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HUNTER: Releasing Persistent Memory Write Performance with A Novel PM-DRAM Collaboration Architecture

HUNTER: Releasing Persistent Memory Write Performance with A...

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Design Automation Conference

作者： Yanqi Pan Yifeng Zhang Wen Xia Xiangyu Zou Cai Deng School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

We present HUNTER, a POSIX-compliant persistent memory (PM) file system that fully releases PM’s write performance. Compared to state-of-the-art ones, HUNTER proposes a novel PM-DRAM collaboration architecture to significantly eliminate/reduce software overheads in the write path. Expensive in-PM metadata are updated asynchronously to hide their performance penalties. Furthermore, in-PM metadata/data are laid out separately for locality awareness, enabling collaboration with asynchronous architecture. HUNTER also adopts several lightweight in-DRAM allocators/indexes to manage PM *** results suggest that HUNTER achieves 2.0–3.4× write bandwidth compared to state-of-the-art PM file systems in write-intensive workloads and shows similar write bandwidth compared to bare PM.

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Evolutionary Trigger Detection and Lightweight Model Repair Based Backdoor Defense

arXiv

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arXiv 2024年

作者： Zhou, Qi Ye, Zipeng Tang, Yubo Luo, Wenjian Shi, Yuhui Jia, Yan Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China School of Computer Science and Engineering Southern University of Science and Technology Guangdong Shenzhen518055 China

Deep Neural networks (DNNs) have been widely used in many areas. However, DNN model is fragile to backdoor attacks. A backdoor in the DNN model can be activated by a poisoned input with trigger and leads to wrong prediction, which causes serious security issues in applications. It is challenging for current defenses to eliminate the backdoor effectively with limited computing resources, especially when the sizes and numbers of the triggers are variable as in the physical world. We propose an efficient backdoor defense based on evolutionary trigger detection and lightweight model repair. In the first phase of our method, CAM-focus Evolutionary Trigger Filter (CETF) is proposed for trigger detection. CETF is an effective sample-preprocessing based method with the evolutionary algorithm, and our experimental results show that CETF not only distinguishes the images with triggers accurately from the clean images, but also can be widely used in practice for its simplicity and stability in different backdoor attack situations. In the second phase of our method, we leverage several lightweight unlearning methods with the trigger detected by CETF for model repair, which also constructively demonstrate the underlying correlation of the backdoor with Batch Normalization layers. Source code will be published after accepted. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Joint Extraction of Entities and Relationships from Cyber Threat Intelligence based on Task-specific Fourier Network

Joint Extraction of Entities and Relationships from Cyber Th...

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International Joint Conference on Neural networks (IJCNN)

作者： Haiqing Lv Xiaohui Han Hui Cui Peipei Wang Wenbo Zuo Yang Zhou 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 Quan Cheng Laboratory Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The increasing complexity of cyber threats and the emergence of new attack technologies have brought huge challenges to attack incident analysis and source tracing. Using cyber threat intelligence to build a Cyber security Knowledge Graph (CKG) provides a new technical solution for attack attribution. Constructing a CKG requires numerous entity and relationship triples extracted from unstructured cyber threat intelligence texts. However, existing entity and relationship joint extraction methods in cyber threat intelligence face two problems. Firstly, they share the same word embeddings for both subtasks, ignoring the fine-grained semantic differences between the subtasks. Secondly, they rarely consider the interaction between the features of the two subtasks, which is vital for capturing the semantic dependencies between the tasks. To address these issues, we propose a joint entity and relationship extraction model specifically designed for network security concepts. We utilize two lightweight Fourier networks with independent weights to build a feature extraction module for encoding fine-grained features for entity recognition and relationship extraction tasks. Furthermore, we use a subtask feature interaction strategy assisted by a gated attention mechanism to enhance feature interaction between entity recognition and relationship extraction tasks. Use fine-grained entity recognition task information to guide relationship extraction to capture semantic dependencies between tasks. Experimental results on a cyber threat intelligence dataset demonstrate that our model outperforms existing baselines.

关键词： Attention mechanisms Face recognition Semantics Neural networks Knowledge graphs Logic gates Network security

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Enhance Sketch Recognition’s Explainability via Semantic Component-Level Parsing

arXiv

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arXiv 2023年

作者： Zhu, Guangming Wang, Siyuan Wu, Tianci Zhang, Liang School of Computer Science and Technology Xidian University China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province China Xi’an Key Laboratory of Intelligent Software Engineering China

Free-hand sketches are appealing for humans as a universal tool to depict the visual world. Humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus that which types of semantic components constitute each sketch category. For example, an airplane should at least have a fuselage and wings. Based on this analysis, a semantic component-level memory module is constructed and embedded in the proposed structured sketch recognition network in this paper. The memory keys representing semantic components of each sketch category can be self-learned and enhance the recognition network’s explainability. Our proposed networks can deal with different situations of sketch recognition, i.e., with or without semantic components labels of strokes. Experiments on the SPG and SketchIME datasets demonstrate the memory module’s flexibility and the recognition network’s explainability. The code and data are available at https://***/GuangmingZhu/SketchESC. © 2023, CC BY.

关键词： Semantics

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Deny Whatever You Want: Dual-Deniable Public-key Encryption 28th

Deny Whatever You Want: Dual-Deniable Public-Key Encryption

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28th IACR International Conference on Practice and Theory of Public key Cryptography, PKC 2025

作者： An, Zhiyuan Zhang, Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou510006 China

ISBN: (纸本)9783031918285

We introduce an enhanced requirement of deniable public key encryption that we call dual-deniability. It asks that a sender who is coerced should be able to produce fake randomness, which can explain the target ciphertext as the encryption of any alternative message under any valid key she/he desires to deny. Compared with the original notion of deniability (Canetti et al. in CRYPTO ’97, hereafter named message-deniability), this term further provides a shield for the anonymity of the receiver against coercion attacks. We first give a formal definition of dual-deniability, along with its weak-mode variant. For conceptual understanding, we then show dual-deniability implies semantic security and anonymity against CPA, separates full robustness, and even contradicts key-less or mixed robustness, while is (constructively) implied by key-deniability and full robustness with a minor assumption for bits encryption. As for the availability of dual-deniability, our main scheme is a generic approach from ciphertext-simulatable PKE, where we devise a subtle multi-encryption schema to hide the true message within random masking ciphertexts under plan-ahead setting. Further, we leverage the weak model to present a more efficient scheme having negligible detection probability and constant ciphertext size. Besides, we revisit the notable scheme (Sahai and Waters in STOC ’14) and show it is inherently dual-deniable. Finally, we extend the Boneh-Katz transform to capture CCA security, deriving dual-deniable and CCA-secure PKE from any selectively dual-deniable IBE under multi-TA setting. Overall our work mounts the feasibility of anonymous messaging against coercion attacks. © International Association for Cryptologic Research 2025.

关键词： Anonymity

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Internet of Digital Twin: Framework, Applications and Enabling Technologies

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IEEE Communications Surveys and Tutorials 2025年

作者： Liu, Ruhan Luan, Tom H. Qu, Youyang Xiang, Yong Gao, Longxiang Zhao, Dongmei Deakin University School of Info Technology MelbourneVIC3125 Australia Xi'an Jiaotong University School of Cyber Science and Engineering Shaanxi Xi'an710049 China Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China McMaster University Department of Electrical and Computer Engineering HamiltonON Canada

Intelligent physical systems, such as smart vehicles and robotic arms, are increasingly integrated into both industrial and everyday applications. However, the systems typically face hardware limitations that constrain their computational capacities. Digital twin systems offer a solution by creating real-time digital replicas of physical systems that enhance computational efficiency, overcoming physical limitations. Moreover, multiple digital twins that hold complementary knowledge can conveniently collaborate to share information and computational resources, further improving the performance of physical systems by forming an Internet of Digital Twin (IoDT). This paper presents a comprehensive investigation of the digital twin network, tracing the evolution of digital twins and providing a classification of the key technologies, functional frameworks, and application domains of IoDT. This paper delves into the IoDT communication framework by studying the fundamental communication modes of IoDT, exploring its integration with advanced technologies such as edge computing, blockchain, 5G/6G networks, and machine learning to facilitate data transmission, interaction, and omni-directional sensing. By offering a broad perspective, the paper aims to deepen stakeholders' understanding of current research and potential future developments, encouraging further exploration of IoDT technologies and their evolution. © 1998-2012 IEEE.

关键词： 5G mobile communication systems

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Loc4Plan: Locating Before Planning for Outdoor Vision and Language Navigation

arXiv

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arXiv 2024年

作者： Tian, Huilin Meng, Jingke Zheng, Wei-Shi Li, Yuan-Ming Yan, Junkai Zhang, Yunong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou China

Vision and Language Navigation (VLN) is a challenging task that requires agents to understand instructions and navigate to the destination in a visual environment. One of the key challenges in outdoor VLN is keeping track of which part of the instruction was completed. To alleviate this problem, previous works mainly focus on grounding the natural language to the visual input, but neglecting the crucial role of the agent’s spatial position information in the grounding process. In this work, we first explore the substantial effect of spatial position locating on the grounding of outdoor VLN, drawing inspiration from human navigation. In real-world navigation scenarios, before planning a path to the destination, humans typically need to figure out their current location. This observation underscores the pivotal role of spatial localization in the navigation process. In this work, we introduce a novel framework, Locating before Planning (Loc4Plan), designed to incorporate spatial perception for action planning in outdoor VLN tasks. The main idea behind Loc4Plan is to perform the spatial localization before planning a decision action based on corresponding guidance, which comprises a block-aware spatial locating (BAL) module and a spatial-aware action planning (SAP) module. Specifically, to help the agent perceive its spatial location in the environment, we propose to learn a position predictor that measures how far the agent is from the next intersection for reflecting its position, which is achieved by the BAL module. After the locating process, we propose the SAP module to incorporate spatial information to ground the corresponding guidance and enhance the precision of action planning. Extensive Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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