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Towards real-time ML-based DDoS detection via cost-efficient window-based feature extraction

science China(Information sciences) 2023年第5期66卷 114-129页

作者： Haibin LI Yi ZHAO Wenbing YAO Ke XU Qi LI Department of Computer Science and Technology Tsinghua University Beijing National Research Center for Information Science and Technology (BNRist) Institute for Network Sciences and Cyberspace Tsinghua University

Distributed denial of service(DDoS) detection is still an open and challenging problem. In particular, sophisticated attacks, e.g., attacks that disguise attack packets as benign traffic always appear, which can easily evade traditional signature-based methods. Due to the low requirements for computing resources compared to deep learning, many machine learning(ML)-based methods have been realistically deployed to address this issue. However, most existing ML-based DDo S detection methods are highly dependent on the features extracted from each flow, which incur remarkable detection delay and computation overhead. This article investigates the limitations of typical ML-based DDo S detection methods caused by the extraction of flow-level features. Moreover, we develop a cost-efficient window-based method that extracts features from a fixed number of packets periodically, instead of per flow, aiming to reduce the detection delay and computation overhead. The newly proposed window-based method has the advantages of well-controlled overhead and wide support of common routers due to its simplicity and high efficiency by design. Through extensive experiments on real datasets, we evaluate the performance of flow-based and window-based *** experimental results demonstrate that our proposed window-based method can significantly reduce the detection delay and computation overhead while ensuring detection accuracy.

关键词： DDoS attack machine learning feature extraction detection delay cost-efficiency

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VulnerGAN: a backdoor attack through vulnerability amplification against machine learning-based network intrusion detection systems

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science China(Information sciences) 2022年第7期65卷 32-50页

作者： Guangrui LIU Weizhe ZHANG Xinjie LI Kaisheng FAN Shui YU School of Cyberspace Science Harbin Institute of Technology Cyberspace Security Research Center Peng Cheng Laboratory School of Computer Science University of Technology Sydney

Machine learning-based network intrusion detection systems(ML-NIDS) are extensively used for network security against unknown attacks. Existing intrusion detection systems can effectively defend traditional network attacks, however, they face AI based threats. The current known AI attacks cannot balance the escape rate and attack effectiveness. In addition, the time cost of existing AI attacks is very high. In this paper, we propose a backdoor attack called VulnerGAN, which features high concealment,high aggressiveness, and high timeliness. The backdoor can make the specific attack traffic bypass the detection of ML-NIDS without affecting the performance of ML-NIDS in identifying other attack *** uses generative adversarial networks(GAN) to calculate poisoning and adversarial samples based on machine learning model vulnerabilities. It can make traditional network attack traffic escape black-box online ML-NIDS. At the same time, model extraction and fuzzing test are used to enhance the convergence of VulnerGAN. Compared with the state-of-the-art algorithms, the VulnerGAN backdoor attack increases33.28% in concealment, 18.48% in aggressiveness, and 46.32% in timeliness.

关键词： AI security adversarial sample data poisoning network intrusion detection generative adversarial network

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Lip-Audio Modality Fusion for Deep Forgery Video Detection

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computers, Materials & Continua 2025年第2期82卷 3499-3515页

作者： Yong Liu Zhiyu Wang Shouling Ji Daofu Gong Lanxin Cheng Ruosi Cheng College of Cyberspace Security Information Engineering UniversityZhengzhou450001China Research Institute of Intelligent Networks Zhejiang LabHangzhou311121China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China Key Laboratory of Cyberspace Security Ministry of EducationZhengzhou450001China

In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural Networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.

关键词： Deep forgery video detection lip-audio modality fusion mel frequency cepstrum coefficient siamese neural network band-pass filter

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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Algorithms for online fault tolerance server consolidation

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Digital Communications and Networks 2025年第2期11卷 514-523页

作者： Boyu Li Bin Wu Meng Shen Hao Peng Weisheng Li Hong Zhang Jie Gan Zhihong Tian Guangquan Xu College of Intelligence and Computing Tianjin UniversityTianjin 300350China School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Computer Science and Technology Zhejiang Normal UniversityJinhua 321004China School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400054China School of Astronautics Beihang UniversityBeijing100191China School of Computer Science University of SydneySydneyNSW 20528Australia Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou 511370China

We study a novel replication mechanism to ensure service continuity against multiple simultaneous server *** this mechanism,each item represents a computing task and is replicated intoξ+1 servers for some integerξ≥1,with workloads specified by the amount of required *** one or more servers fail,the affected workloads can be redirected to other servers that host replicas associated with the same item,such that the service is not interrupted by the failure of up toξ*** requires that any feasible assignment algorithm must reserve some capacity in each server to accommodate the workload redirected from potential failed servers without overloading,and determining the optimal method for reserving capacity becomes a key *** existing algorithms that assume that no two servers share replicas of more than one item,we first formulate capacity reservation for a general arbitrary *** to the combinatorial nature of this problem,finding the optimal solution is *** this end,we propose a Generalized and Simple Calculating Reserved Capacity(GSCRC)algorithm,with a time complexity only related to the number of items packed in the *** conjunction with GSCRC,we propose a robust replica packing algorithm with capacity optimization(RobustPack),which aims to minimize the number of servers hosting replicas and tolerate multiple server *** theoretical analysis and experimental evaluations,we show that the RobustPack algorithm can achieve better performance.

关键词： Cloud computing Server consolidation Replica Fault tolerance

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Block-gram:Mining knowledgeable features for efficiently smart contract vulnerability detection

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Digital Communications and Networks 2025年第1期11卷 1-12页

作者： Xueshuo Xie Haolong Wang Zhaolong Jian Yaozheng Fang Zichun Wang Tao Li Tianjin Key Laboratory of Network and Data Security Technology TianjinChina College of Computer Science Nankai UniversityTianjinChina Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesChina

Smart contracts are widely used on the blockchain to implement complex transactions,such as decentralized applications on *** vulnerability detection of large-scale smart contracts is critical,as attacks on smart contracts often cause huge economic *** it is difficult to repair and update smart contracts,it is necessary to find the vulnerabilities before they are ***,code analysis,which requires traversal paths,and learning methods,which require many features to be trained,are too time-consuming to detect large-scale on-chain ***-based methods will obtain detection models from a feature space compared to code analysis methods such as symbol *** the existing features lack the interpretability of the detection results and training model,even worse,the large-scale feature space also affects the efficiency of *** paper focuses on improving the detection efficiency by reducing the dimension of the features,combined with expert *** this paper,a feature extraction model Block-gram is proposed to form low-dimensional knowledge-based features from ***,the metadata is separated and the runtime code is converted into a sequence of opcodes,which are divided into segments based on some instructions(jumps,etc.).Then,scalable Block-gram features,including 4-dimensional block features and 8-dimensional attribute features,are mined for the learning-based model ***,feature contributions are calculated from SHAP values to measure the relationship between our features and the results of the detection *** addition,six types of vulnerability labels are made on a dataset containing 33,885 contracts,and these knowledge-based features are evaluated using seven state-of-the-art learning algorithms,which show that the average detection latency speeds up 25×to 650×,compared with the features extracted by N-gram,and also can enhance the interpretability of the detection model.

关键词： Smart contract Bytecode&opcode Knowledgeable features Vulnerability detection Feature contribution

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COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection

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science China(Information sciences) 2025年第1期68卷 189-203页

作者： Xiaoqin ZHANG Zhenni YU Li ZHAO Deng-Ping FAN Guobao XIAO Zhejiang Province Key Laboratory of Intelligent Informatics for Safety and Emergency Wenzhou University Nankai International Advanced Research Institute (SHENZHEN FUTIAN) College of Computer Science Nankai University School of Computer Science and Technology Tongji University

We rethink the segment anything model(SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection(COD). SAM has zero-shot generalization ability beyond other models and can provide an ideal framework for COD. Our network aims to enhance the single prompt strategy in SAM to a multiprompt strategy. To achieve this, we propose an edge gradient extraction module, which generates a mask containing gradient information regarding the boundaries of camouflaged objects. This gradient mask is then used as a novel boundary prompt, enhancing the segmentation process. Thereafter, we design a box-boundary mutual guidance module, which fosters more precise and comprehensive feature extraction via mutual guidance between a boundary prompt and a box prompt. This collaboration enhances the model's ability to accurately detect camouflaged objects. Moreover, we employ the discrete wavelet transform to extract high-frequency features from image embeddings. The high-frequency features serve as a supplementary component to the multiprompt ***, our COMPrompter guides the network to achieve enhanced segmentation results, thereby advancing the development of SAM in terms of COD. Experimental results across COD benchmarks demonstrate that COMPrompter achieves a cutting-edge performance, surpassing the current leading model by an average positive metric of 2.2% in COD10K. In the specific application of COD, the experimental results in polyp segmentation show that our model is superior to top-tier methods as well. The code will be made available at https://***/guobaoxiao/COMPrompter.

关键词： segment anything model camouflaged object detection boundary prompt

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Localization in modified polar representation: hybrid measurements and closed-form solution

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Journal of Systems Engineering and Electronics 2024年第3期35卷 575-588页

作者： CONG Xunchao SUN Yimao YANG Yanbing ZHANG Lei CHEN Liangyin The 10th Research Institute of China Electronics Technology Group Corporation Chengdu 610036China College of Computer Science and Institute for Industrial Internet Research Sichuan UniversityChengdu 610065China

Classical localization methods use Cartesian or Polar coordinates, which require a priori range information to determine whether to estimate position or to only find bearings. The modified polar representation (MPR) unifies near-field and farfield models, alleviating the thresholding effect. Current localization methods in MPR based on the angle of arrival (AOA) and time difference of arrival (TDOA) measurements resort to semidefinite relaxation (SDR) and Gauss-Newton iteration, which are computationally complex and face the possible diverge problem. This paper formulates a pseudo linear equation between the measurements and the unknown MPR position,which leads to a closed-form solution for the hybrid TDOA-AOA localization problem, namely hybrid constrained optimization(HCO). HCO attains Cramér-Rao bound (CRB)-level accuracy for mild Gaussian noise. Compared with the existing closed-form solutions for the hybrid TDOA-AOA case, HCO provides comparable performance to the hybrid generalized trust region subproblem (HGTRS) solution and is better than the hybrid successive unconstrained minimization (HSUM) solution in large noise region. Its computational complexity is lower than that of HGTRS. Simulations validate the performance of HCO achieves the CRB that the maximum likelihood estimator (MLE) attains if the noise is small, but the MLE deviates from CRB earlier.

关键词： localization modified polar representation time difference of arrival(TDOA) angle of arrival(AOA) closed-form solution

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A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval

A Framework of Knowledge Graph-Enhanced Large Language Model...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Yading Song, Dandan Zhou, Changzhi Tian, Yuhang Wang, Hao Yang, Ziyi Zhang, Shuhao School of Computer Science and Technology Beijing Institute of Technology China School of Cyberspace Science and Technology Beijing Institute of Technology China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Knowledge graphs (KGs) can provide explainable reasoning for large language models (LLMs), alleviating their hallucination problem. Knowledge graph question answering (KGQA) is a typical benchmark to evaluate the methods enhancing LLMs with KG. Previous methods on KG-enhanced LLM for KGQA either enhance LLMs with KG retrieval in a single round or perform multi-hop KG reasoning in multiple rounds with LLMs. Both of them conduct retrieving and reasoning based solely on the whole original question, without any processing to the question. To tackle this limitation, we propose a framework of KG-enhanced LLM based on question decomposition and atomic retrieval, called KELDaR. We introduce question decomposition tree as the framework for LLM reasoning. This approach extracts the implicit information of reasoning steps within complex questions, serving as a guide to facilitate atomic retrieval on KG targeting the atomic-level simple questions at leaves of the tree. Additionally, we design strategies for atomic retrieval, which extract and retrieve question-relevant KG subgraphs to assist the few-shot LLM in answering atomic-level questions. Experiments on KGQA datasets demonstrate that our framework outperforms existing reasoning-based baselines. And in a low-cost setting without additional training or fine-tuning, our framework achieves competitive or superior results compared to most existing training-based baselines. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Pyramid-VAE-GAN:Transferring hierarchical latent variables for image inpainting

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Computational Visual Media 2023年第4期9卷 827-841页

作者： Huiyuan Tian Li Zhang Shijian Li Min Yao Gang Pan College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Advanced Technology Research Institute Zhejiang UniversityHangzhou 310027China

Significant progress has been made in image inpainting methods in recent ***,they are incapable of producing inpainting results with reasonable structures,rich detail,and sharpness at the same *** this paper,we propose the Pyramid-VAE-GAN network for image inpainting to address this *** network is built on a variational autoencoder(VAE)backbone that encodes high-level latent variables to represent complicated high-dimensional prior distributions of *** prior assists in reconstructing reasonable structures when *** also adopt a pyramid structure in our model to maintain rich detail in low-level latent *** avoid the usual incompatibility of requiring both reasonable structures and rich detail,we propose a novel cross-layer latent variable transfer *** transfers information about long-range structures contained in high-level latent variables to low-level latent variables representing more detailed *** further use adversarial training to select the most reasonable results and to improve the sharpness of the *** experimental results on multiple datasets demonstrate the superiority of our *** code is available at https://***/thy960112/Pyramid-VAE-GAN.

关键词： image inpainting variational autoencoder(VAE) latent variable transfer(LTN) pyramid structure generative model

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