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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： You Zhai Jian Yang Zixiang Wang Longtao He Liqun Yang Zhoujun Li State Key Lab of Software Development Environment Beihang University National Computer Network Emergency Response Technical Team Coordination Center of China School of Cyber Science and Technology Beihang University

Recently Command and Control (C&C) servers have attracted considerable attention in botnets and domain generation algorithms (DGAs) further enhance the stealth of C&C servers. However, Algorithmically Generated Domains (AGDs) generated by DGAs can be easily detected by previous DGA detection approaches. More specifically, the previous DGAs are hard to satisfy domain name rules, low repetition rate, and anti-detection in practical scenarios simultaneously. Designing an outstanding DGA has become a crucial issue from the botnet owner’s perspective. To mitigate these problems, we propose Cdga, a Controllable DGA via Generative Adversarial networks (GAN), which is a popular backbone model for text generation in the natural language processing (NLP) *** text generation approaches are adopted by Cdga to ensure no repetition in the generated domain names and compliance with the domain rules. In addition to cheating DGA detectors, GANs are exploited to equip Cdga with a powerful anti-detection ability. Furthermore, our proposed method uses the technique of NLP to force the AGDs to meet language rules, where the generated domain names are difficult for recognition by human. By utilizing the time-dependent seed, Cdga can dynamically generate domain names, ensuring that the malware can connect to the C&C server conditioned on a specific time stamp. Experimental results demonstrate that the domain names generated by our method are realistic enough to be resistant to the state-of-the-art DGA detectors.

关键词： Command and control systems Privacy Botnet Force Detectors Generative adversarial networks Natural language processing

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Learning an explicit hyper-parameter prediction function conditioned on tasks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 8818-8891页

作者： Jun Shu Deyu Meng Zongben Xu School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an Shaan'xi Province P. R. China Pazhou Lab (Huangpu) Guangzhou Guangdong Province P. R. China School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an Shaan'xi Province P. R. China Pazhou Lab (Huangpu) Guangzhou Guangdong Province P. R. China and Macau Institute of Systems Engineering Macau University of Science and Technology Taipa Macau P. R. China

Meta learning has attracted much attention recently in machine learning community. Contrary to conventional machine learning aiming to learn inherent prediction rules to predict labels for new query data, meta learning aims to learn the learning methodology for machine learning from observed tasks, so as to generalize to new query tasks by leveraging the meta-learned learning methodology. In this study, we achieve such learning methodology by learning an explicit hyper-parameter prediction function shared by all training tasks, and we call this learning process as Simulating Learning Methodology (SLeM). Specifically, this function is represented as a parameterized function called meta-learner, mapping from a training/test task to its suitable hyper-parameter setting, extracted from a prespecified function set called meta learning machine. Such setting guarantees that the meta-learned learning methodology is able to flexibly fit diverse query tasks, instead of only obtaining fixed hyper-parameters by many current meta learning methods, with less adaptability to query task's variations. Such understanding of meta learning also makes it easily succeed from traditional learning theory for analyzing its generalization bounds with general losses/tasks/models. The theory naturally leads to some feasible controlling strategies for ameliorating the quality of the extracted meta-learner, verified to be able to finely ameliorate its generalization capability in some typical meta learning applications, including few-shot regression, few-shot classification and domain generalization. The source code of our method is released at https://***/xjtushujun/SLeM-Theory.

关键词： meta learning simulating learning methodology statistical learning theory few-shot learning domain generalization structural risk minimization meta-regularization

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Chain-of-Thought Poisoning Attacks against R1-based Retrieval-Augmented Generation Systems

arXiv

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arXiv 2025年

作者： Song, Hongru Liu, Yu-An Zhang, Ruqing Guo, Jiafeng Fan, Yixing CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China

Retrieval-augmented generation (RAG) systems can effectively mitigate the hallucination problem of large language models (LLMs), but they also possess inherent vulnerabilities. Identifying these weaknesses before the large-scale real-world deployment of RAG systems is of great importance, as it lays the foundation for building more secure and robust RAG systems in the future. Existing adversarial attack methods typically exploit knowledge base poisoning to probe the vulnerabilities of RAG systems, which can effectively deceive standard RAG models. However, with the rapid advancement of deep reasoning capabilities in modern LLMs, previous approaches that merely inject incorrect knowledge are inadequate when attacking RAG systems equipped with deep reasoning abilities. Inspired by the deep thinking capabilities of LLMs, this paper extracts reasoning process templates from R1-based RAG systems, uses these templates to wrap erroneous knowledge into adversarial documents, and injects them into the knowledge base to attack RAG systems. The key idea of our approach is that adversarial documents, by simulating the chain-of-thought patterns aligned with the model’s training signals, may be misinterpreted by the model as authentic historical reasoning processes, thus increasing their likelihood of being referenced. Experiments conducted on the MS MARCO passage ranking dataset demonstrate the effectiveness of our proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Domain Knowledge

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Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs

arXiv

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arXiv 2025年

作者： Hou, Zhongni Su, Miao Jin, Xiaolong Li, Zixuan Bai, Long Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS China University of Chinese Academy of Sciences China

Temporal Knowledge Graphs (TKGs), which utilize quadruples in the form of (subject, predicate, object, timestamp) to describe temporal facts, have attracted extensive attention. N-tuple TKGs (N-TKGs) further extend traditional TKGs by utilizing n-tuples to incorporate auxiliary elements alongside core elements (i.e., subject, predicate, and object) of facts, so as to represent them in a more fine-grained manner. Reasoning over N-TKGs aims to predict potential future facts based on historical ones. However, existing N-TKG reasoning methods often lack explainability due to their black-box nature. Therefore, we introduce a new Reinforcement Learning-based method, named MT-Path, which leverages the temporal information to traverse historical n-tuples and construct a temporal reasoning path. Specifically, in order to integrate the information encapsulated within n-tuples, i.e., the entity-irrelevant information within the predicate, the information about core elements, and the complete information about the entire n-tuples, MT-Path utilizes a mixture policy-driven action selector, which bases on three low-level policies, namely, the predicate-focused policy, the core-element-focused policy and the whole-fact-focused policy. Further, MT-Path utilizes an auxiliary element-aware GCN to capture the rich semantic dependencies among facts, thereby enabling the agent to gain a deep understanding of each n-tuple. Experimental results demonstrate the effectiveness and the explainability of MT-Path. © 2025, CC BY.

关键词： Reinforcement learning

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Evaluating natural language generation via unbalanced optimal transport 29

Evaluating natural language generation via unbalanced optima...

引用

29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Chen, Yimeng Lan, Yanyan Xiong, Ruibin Pang, Liang Ma, Zhiming Cheng, Xueqi University of Chinese Academy of Sciences China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS China Academy of Mathematics and Systems Science CAS China

ISBN: (纸本)9780999241165

Embedding-based evaluation measures have shown promising improvements on the correlation with human judgments in natural language generation. In these measures, various intrinsic metrics are used in the computation, including generalized precision, recall, F-score and the earth mover's distance. However, the relations between these metrics are unclear, making it difficult to determine which measure to use in real applications. In this paper, we provide an in-depth study on the relations between these metrics. Inspired by the optimal transportation theory, we prove that these metrics correspond to the optimal transport problem with different hard marginal constraints. However, these hard marginal constraints may cause the problem of incomplete and noisy matching in the evaluation process. Therefore we propose a family of new evaluation metrics, namely Lazy Earth Mover's Distances, based on the more general unbalanced optimal transport problem. Experimental results on WMT18 and WMT19 show that our proposed metrics have the ability to produce more consistent evaluation results with human judgements, as compared with existing intrinsic metrics. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Computation theory

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Trajectory Privacy Protection with Pricing Awareness on Ride-on-Demand System

Trajectory Privacy Protection with Pricing Awareness on Ride...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Sihui Jia Saiqin Long Zhirun Zheng Qingyong Deng Ping Wang Shujuan Tian School of Computer Science and School of Cyberspace Science Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan University Xiangtan China School of Information Science and Technology Jinan University Guangzhou China School of Mathematics and Compulational Science Xiangtan University Xiangtan China Guangxi Key Lab of Multi-source Information Mining & Security Guangxi Normal University Guilin China

With the widespread use of the Ride-on Demand (RoD) system, many privacy issues have been exposed, and there is growing concern about whether private information will be leaked. For this problem, our previous work addressed the issue of the user’s initial and final location leakage and provided a strong utility guarantee in the RoD system. Further, the trajectory information is also important in the RoD system, it could contain a lot of private information about the user, such as health or identity, so it’s important to publish a distorted and productive trajectory. For this purpose, in this paper, we provide our trajectory protection method with pricing awareness based on previous work; the method uses supply and demand density function to guide the division of a discrete spatial grid, then uses the Markov chain to generate distorted trajectories on the grid to ensure trajectory continuity, and makes corresponding defenses against several attacks, such as Bayesian. The experiment results on real-world datasets prove the validity and robustness of the method.

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A Deep Exploration of BitLocker Encryption and Security Analysis 20

A Deep Exploration of BitLocker Encryption and Security Anal...

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20th IEEE International Conference on Communication technology, ICCT 2020

作者： Tan, Cheng Zhang, Lijun Bao, Liang Science and Technology on Communication Security Laboratory Chengdu China Security Ministry of Public Security Key Lab of Information Network Shanghai China

ISBN: (纸本)9781728181417

Due to the popularity of Windows system, BitLocker is widely used as a built-in disk encryption tool. As a commercial application, the design of BitLocker has to consider a capability of disaster recovery, which helps a user to recover data stored on encrypted disk when a regular access is not available. In this case, it will inevitably lead to some security risks when using BitLocker. We have a deep exploration of BitLocker encryption mechanism in this paper. We present the decryption method of encrypted VMK in case of system partition encryption and non-system partition encryption, respectively. VMK is the core key in BitLocker, with which the encrypted partition or the entire disk can be further decrypted. As for security analysis on BitLocker, we firstly make a difficulty analysis of brute force cracking on BitLocker keys, and then we analyze a possible threat caused by key theft. Based on this, we propose a few countermeasures about BitLocker usage. Additionally, we give some suggestions about security enhancement of BitLocker encryption. © 2020 IEEE.

关键词： Cryptography

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Rate-Splitting Multiple Access Enabled Green Probabilistic Semantic Communication over Wireless networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and networking 2025年

作者： Xu, Ruopeng Yang, Zhaohui Mao, Yijie Huang, Chongwen Yang, Qianqian Xu, Lexi Xu, Wei Zhang, Zhaoyang Zhejiang University College of Information Science and Electronic Engineering China Hangzhou310027 China ShanghaiTech University School of Information Science and Technology Shanghai201210 China Zhejiang Prov. Key Lab. of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China China United Network Communications Corporation Research Institute Beijing100048 China Southeast University National Mobile Communications Research Laboratory Nanjing211189 China

In this paper, we propose a multi-user green probabilistic semantic communication (PSC) system for semantic communication (SemCom) facilitated by probabilistic knowledge graphs (PKGs), which are knowledge graphs (KGs) integrated with probability to represent semantic information. In the considered model, semantic information of different users is represented by PKGs. On this basis, we design a semantic compression model for multi-user downlink task-oriented SemCom, utilizing the semantic compression ratio (SCR) as a parameter to connect the computation and communication processes of information transmission. Utilizing rate-splitting multiple access (RSMA) technology, the transmitted messages are splitted into shared and private ones. Considering the limited wireless resources and constraint in semantic communication, an optimization problem with the goal of minimizing system energy consumption comprehensively considering the computation and communication process is formulated. In order to address the optimization problem, we propose an alternating optimization algorithm that tackles sub-problems of power allocation and beamforming design with successive convex approximation (SCA) method, semantic compression ratio optimization with variable substitution method, and computation capacity allocation with obtaining closed-form optimal solution. Simulation results verify the effectiveness of the proposed algorithm. © 2017 IEEE.

关键词： Knowledge graph

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FAST: a dual-tier few-shot learning paradigm for whole slide image classification 24

FAST: a dual-tier few-shot learning paradigm for whole slide...

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

作者： Kexue Fu Xiaoyuan Luo Linhao Qu Shuo Wang Ying Xiong Ilias Maglogiannis Longxiang Gao Manning Wang 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 and Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Fudan University University of Piraeus

ISBN: (纸本)9798331314385

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST.

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A Prior Embedding-Driven Architecture for Long Distance Blind Iris Recognition

arXiv

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arXiv 2024年

作者： Xiong, Qi Zhang, Xinman Shen, Jun International College Hunan University of Arts and Sciences Changde415000 China School of Automation Science and Engineering Faculty of Electronic and Information Engineering MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia

Blind iris images, which result from unknown degradation during the process of iris recognition at long distances, often lead to decreased iris recognition rates. Currently, little existing literature offers a solution to this problem. In response, we propose a prior embedding-driven architecture for long distance blind iris recognition. We first proposed a blind iris image restoration network called Iris-PPRGAN. To effectively restore the texture of the blind iris, Iris-PPRGAN includes a Generative Adversarial network (GAN) used as a Prior Decoder, and a DNN used as the encoder. To extract iris features more efficiently, we then proposed a robust iris classifier by modifying the bottleneck module of InsightFace, which called Insight-Iris. A low-quality blind iris image is first restored by Iris-PPRGAN, then the restored iris image undergoes recognition via Insight-Iris. Experimental results on the public CASIA-Irisdistance dataset demonstrate that our proposed method significantly superior results to state-of-the-art blind iris restoration methods both quantitatively and qualitatively, Specifically, the recognition rate for long-distance blind iris images reaches 90% after processing with our methods, representing an improvement of approximately ten percentage points compared to images without restoration. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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