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Detecting APT-Exploited Processes through Semantic Fusion and Interaction Prediction

Computers, Materials & Continua 2024年第2期78卷 1731-1754页

作者： Bin Luo Liangguo Chen Shuhua Ruan Yonggang Luo School of cyber Science and Engineering Sichuan UniversityChengdu610065China Key Laboratory of Data Protection and Intelligent Management(Sichuan University) Ministry of EducationChengdu610065China cyber Science research institute Sichuan UniversityChengdu610065China

Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a ***-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks,and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection,which requires lots of manual efforts to locate attack *** paper proposes an APT-exploited process detection approach called ThreatSniffer,which constructs the benign provenance graph from attack-free audit logs,fits normal system entity interactions and then detects APT-exploited processes by predicting the rationality of entity ***,ThreatSniffer understands system entities in terms of their file paths,interaction sequences,and the number distribution of interaction types and uses the multi-head self-attention mechanism to fuse these ***,based on the insight that APT-exploited processes interact with system entities they should not invoke,ThreatSniffer performs negative sampling on the benign provenance graph to generate non-existent edges,thus characterizing irrational entity interactions without requiring APT attack *** last,it employs a heterogeneous graph neural network as the interaction prediction model to aggregate the contextual information of entity interactions,and locate processes exploited by attackers,thereby achieving fine-grained APT *** results demonstrate that anomaly-based detection enables ThreatSniffer to identify all attack *** to the node-level APT detection method APT-KGL,ThreatSniffer achieves a 6.1%precision improvement because of its comprehensive understanding of entity semantics.

关键词： Advanced persistent threat provenance graph multi-head self-attention graph neural network

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A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

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Computers, Materials & Continua 2024年第6期79卷 3955-3975页

作者： Xinkun Tang Feng Ouyang Ying Xu Ligu Zhu Bo Peng School of Computer and cyber Sciences Communication University of ChinaBeijing100024China Cable Television Technology research institute Academy of Broadcasting ScienceBeijing100866China

High-resolution video transmission requires a substantial amount of *** this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution *** method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data *** validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical *** proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement *** unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom *** implications of this research extend to optimizing low-bitrate video streaming *** selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth *** results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual *** work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited *** innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

关键词： Super resolution region of interest detection video compression

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Label-Aware Chinese Event Detection with Heterogeneous Graph Attention Network

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Journal of Computer Science & Technology 2024年第1期39卷 227-242页

作者：崔诗尧郁博文从鑫柳厅文谭庆丰时金桥 institute of Information Engineering Chinese Academy of SciencesBeijing 100190China School of cyber Security University of Chinese Academy of SciencesBeijing 100049China cyberspace institute of Advanced Technology Guangzhou UniversityGuangzhou 510006China School of cyber Security Beijing University of Posts and TelecommunicationsBeijing 100088China

Event detection(ED)seeks to recognize event triggers and classify them into the predefined event *** ED is formulated as a character-level task owing to the uncertain word *** methods try to incorpo-rate word-level information into characters to enhance their ***,they experience two ***,they fail to incorporate word-level information into each character the word encompasses,causing the insufficient word-charac-ter interaction ***,they struggle to distinguish events of similar types with limited annotated instances,which is called the event confusing *** paper proposes a novel model named Label-Aware Heterogeneous Graph Attention Network(L-HGAT)to address these two ***,we first build a heterogeneous graph of two node types and three edge types to maximally preserve word-character interactions,and then deploy a heterogeneous graph attention network to enhance the semantic propagation between characters and ***,we design a pushing-away game to enlarge the predicting gap between the ground-truth event type and its confusing counterpart for each *** results show that our L-HGAT model consistently achieves superior performance over prior competitive methods.

关键词： Chinese event detection heterogeneous graph attention network(HGAT) label embedding

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A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

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Digital Communications and Networks 2024年第2期10卷 389-403页

作者： Lihua Yin Sixin Lin Zhe Sun Ran Li Yuanyuan He Zhiqiang Hao cyberspace institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China China Industrial Control Systems cyber Emergency Response Team Beijing100040China

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become *** devices not only provide adequate training data,but also cause privacy leakage and energy *** to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent *** this paper,we define the FL as a 3-layer architecture including users,agents and *** order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game *** use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated *** experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in *** the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

关键词： Federated learning Privacy preservation Energy optimization Game theory Distributed communication systems

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Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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Science China(Information Sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG institute of Information Engineering Chinese Academy of Sciences School of cyber Security University of Chinese Academy of Sciences School of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift cyberspace security intrusion detection malicious traffic identification variational autoencoder

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Strengthening Network Security: Deep Learning Models for Intrusion Detectionwith Optimized Feature Subset and Effective Imbalance Handling

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Computers, Materials & Continua 2024年第2期78卷 1995-2022页

作者： Bayi Xu Lei Sun Xiuqing Mao Chengwei Liu Zhiyi Ding School of cyber Science and Engineering Zhengzhou UniversityZhengzhou450000China Three Academy Information Engineering UniversityZhengzhou450001China The 3rd research Department Nanjing Research Institute of Electronic EngineeringNanjing210007China

In recent years,frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace *** paper presents a novel intrusion detection system consisting of a data prepro-cessing stage and a deep learning model for accurately identifying network *** have proposed four deep neural network models,which are constructed using architectures such as Convolutional Neural Networks(CNN),Bi-directional Long Short-Term Memory(BiLSTM),Bidirectional Gate Recurrent Unit(BiGRU),and Attention *** models have been evaluated for their detection performance on the NSL-KDD *** enhance the compatibility between the data and the models,we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset,resulting in an optimized feature ***,we address class imbalance in the dataset using focal ***,we employ the BO-TPE algorithm to optimize the hyperparameters of the four models,maximizing their detection *** test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data *** binary and multiclass experiments,it achieved accuracy rates of 0.999158 and 0.999091,respectively,surpassing other state-of-the-art methods.

关键词： Intrusion detection CNN BiLSTM BiGRU attention

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SAT-Based Automatic Searching for Differential and Linear Trails: Applying to CRAX

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Chinese Journal of Electronics 2024年第1期33卷 72-79页

作者： Yiyi HAN Caibing WANG Zhongfeng NIU Lei HU State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences School of cyber Security University of Chinese Academy of Sciences

Boolean satisfiability problem(SAT) is now widely applied in differential cryptanalysis and linear cryptanalysis for various cipher *** generated many excellent results for some ciphers,for example,*** this research,we study the differential and linear propagations through the operations of addition,rotation and XOR(ARX),and construct the SAT *** apply the models to CRAX to search differential trails and linear trails *** this sense,our contnbution can be broadly divided into two *** give the bounds for differential and linear cryptanalysis of Alzette both up to 12 steps,by which we present a 3-round differential attack and a 3-round linear attack for *** construct a 4-round key-recovery attack for CRAX with time complexity289times of 4-round encryption and data complexity 225.

关键词： Ciphers Encryption Complexity theory Time complexity

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Provably secure attribute-based authenticated encryption with keyword search from ideal lattices

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Science China(Information Sciences) 2024年第1期67卷 308-309页

作者： Lisha YAO Lin HOU Jian WENG Feixiang ZHAO College of cyber Security Jinan University

A public-key authenticated encryption with keyword search(PAEKS)[1, 2] system overcomes the problem of an inside adversary forging searchable ciphertexts and guessing the keyword information from the given token. This system allows the sender to encrypt and authenticate a ***, traditional PAEKS schemes require the sender to specify the intended receiver in advance, losing much flexibility.

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Security estimation of LWE via BKW algorithms

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cybersecurity 2024年第1期7卷 71-87页

作者： Yu Wei Lei Bi Xianhui Lu Kunpeng Wang SKLOIS Institute of Information EngineeringCASBeijingChina School of cyber Security University of Chinese Academy of SciencesBeijingChina

The Learning With Errors(LWE)problem is widely used in lattice-based cryptography,which is the most promising post-quantum cryptography *** are a variety of LWE-solving methods,which can be classified into four groups:lattice methods,algebraic methods,combinatorial methods,and exhaustive *** Blum–Kalai–Wasserman(BKW)algorithm is an important variety of combinatorial algorithms,which was first presented for solving the Learning Parity With Noise(LPN)problem and then extended to solve *** this paper,we give an overview of BKW algorithms for solving *** introduce the framework and key techniques of BKW algorithms and make comparisons between different BKW algorithms and also with lattice methods by estimating concrete security of specific LWE *** also briefly discuss the current problems and potential future directions of BKW algorithms.

关键词： Post-quantum cryptography Learning with errors problem Lattice-based cryptography Security estimation

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A survey on blockchain-enabled federated learning and its prospects with digital twin

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Digital Communications and Networks 2024年第2期10卷 248-264页

作者： Kangde Liu Zheng Yan Xueqin Liang Raimo Kantola Chuangyue Hu State Key Lab on Integrated Services Network School of Cyber EngineeringXidian UniversityXi'anChina Department of Communications and Networking Aalto UniversityEspooFinland Yangtze Delta Region Blockchain Technology research institute JiaxingChina

Digital Twin(DT)supports real time analysis and provides a reliable simulation platform in the Internet of Things(IoT).The creation and application of DT hinges on amounts of data,which poses pressure on the application of Artificial Intelligence(AI)for DT descriptions and intelligent *** Learning(FL)is a cutting-edge technology that enables geographically dispersed devices to collaboratively train a shared global model locally rather than relying on a data center to perform model ***,DT can benefit by combining with FL,successfully solving the"data island"problem in traditional ***,FL still faces serious challenges,such as enduring single-point failures,suffering from poison attacks,lacking effective incentive *** the successful deployment of DT,we should tackle the issues caused by *** from industry and academia have recognized the potential of introducing Blockchain Technology(BT)into FL to overcome the challenges faced by FL,where BT acting as a distributed and immutable ledger,can store data in a secure,traceable,and trusted ***,to the best of our knowledge,a comprehensive literature review on this topic is still *** this paper,we review existing works about blockchain-enabled FL and visualize their prospects with *** this end,we first propose evaluation requirements with respect to security,faulttolerance,fairness,efficiency,cost-saving,profitability,and support for ***,we classify existing literature according to the functionalities of BT in FL and analyze their advantages and disadvantages based on the proposed evaluation ***,we discuss open problems in the existing literature and the future of DT supported by blockchain-enabled FL,based on which we further propose some directions for future research.

关键词： Digital twin Artificial intelligence Federated learning Blockchain

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