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SmartEagleEye:A Cloud-Oriented Webshell Detection System Based on Dynamic Gray-Box and Deep Learning

Tsinghua science and technology 2024年第3期29卷 766-783页

作者： Xin Liu Yingli Zhang Qingchen Yu Jiajun Min Jun Shen Rui Zhou Qingguo Zhou School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China College of Computer Science and Technology Zhejiang UniversityHangzhou 310058China School of Computing and Information Technology University of WollongongWollongong 2500Australia

Compared with traditional environments,the cloud environment exposes online services to additional vulnerabilities and threats of cyber attacks,and the cyber security of cloud platforms is becoming increasingly prominent.A piece of code,known as a Webshell,is usually uploaded to the target servers to achieve multiple *** Webshell attacks has become a hot spot in current ***,the traditional Webshell detectors are not built for the cloud,making it highly difficult to play a defensive role in the cloud ***,a Webshell detection system based on deep learning that is successfully applied in various scenarios,is proposed in this *** system contains two important components:gray-box and neural network *** gray-box analyzer defines a series of rules and algorithms for extracting static and dynamic behaviors from the code to make the decision *** neural network analyzer transforms suspicious code into Operation Code(OPCODE)sequences,turning the detection task into a classification *** experiment results show that SmartEagleEye achieves an encouraging high detection rate and an acceptable false-positive rate,which indicate its capability to provide good protection for the cloud environment.

关键词： Webshell detection cloud web security deep learning

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A Novel Self-Supervised Contrastive Learning Framework for Masked EEG Motor Imagery Modeling

A Novel Self-Supervised Contrastive Learning Framework for M...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Kunkun Zhou, Qianwei Hu, Haigen College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (纸本)9798350368741

Electroencephalography (EEG) is vital for brain-computer interfaces (BCIs) due to its non-invasive approach and high temporal resolution data capabilities, amid challenges such as data scarcity and the need for extensive labeling. Significant inter-individual variability in EEG signals further limits model generalization. Concurrently, the use of self-supervised pre-training, particularly through masked modeling, is gaining traction in time series analysis to mitigate labeling costs. Although this method involves reconstructing masked signal from unmasked series, random masking can disrupt critical temporal variations, complicating effective representation learning. We thus introduce SSL-MEMI, a novel self-supervised contrastive learning framework for masked EEG motor imagery modeling, integrating Domain Adaptive Alignment (DAA) and Multi-View Temporal-spatial Attention module (MTSA) to effectively handle EEG variability. This framework utilizes manifold-based masking to reconstruct original sequences from masked series, thereby enhancing classification accuracy. When tested on the BCI Competition IV and High Gamma datasets, SSL-MEMI outperforms existing methods, achieving top accuracies and demonstrating superior domain adaptation through reduced Global A-distance scores. This study advances EEG classification and indicates broader applications for self-supervised learning in biomedical signal processing. The source code is available at https://***/KunKun-Zhang/***. © 2025 IEEE.

关键词： Brain-computer Interface electroencephalogram(EEG) Masked Modeling Self-Supervised Learning

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A Video Captioning Method by Semantic Topic-Guided Generation

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computers, Materials & Continua 2024年第1期78卷 1071-1093页

作者： Ou Ye Xinli Wei Zhenhua Yu Yan Fu Ying Yang College of Computer Science and Technology Xi’an University of Science and TechnologyXi’an710054China

In the video captioning methods based on an encoder-decoder,limited visual features are extracted by an encoder,and a natural sentence of the video content is generated using a ***,this kind ofmethod is dependent on a single video input source and few visual labels,and there is a problem with semantic alignment between video contents and generated natural sentences,which are not suitable for accurately comprehending and describing the video *** address this issue,this paper proposes a video captioning method by semantic topic-guided ***,a 3D convolutional neural network is utilized to extract the spatiotemporal features of videos during the ***,the semantic topics of video data are extracted using the visual labels retrieved from similar video *** the decoding,a decoder is constructed by combining a novel Enhance-TopK sampling algorithm with a Generative Pre-trained Transformer-2 deep neural network,which decreases the influence of“deviation”in the semantic mapping process between videos and texts by jointly decoding a baseline and semantic topics of video *** this process,the designed Enhance-TopK sampling algorithm can alleviate a long-tail problem by dynamically adjusting the probability distribution of the predicted ***,the experiments are conducted on two publicly used Microsoft Research Video Description andMicrosoft Research-Video to Text *** experimental results demonstrate that the proposed method outperforms several state-of-art ***,the performance indicators Bilingual Evaluation Understudy,Metric for Evaluation of Translation with Explicit Ordering,Recall Oriented Understudy for Gisting Evaluation-longest common subsequence,and Consensus-based Image Description Evaluation of the proposed method are improved by 1.2%,0.1%,0.3%,and 2.4% on the Microsoft Research Video Description dataset,and 0.1%,1.0%,0.1%,and 2.8% on the Microsoft Research-Video to Text dataset

关键词： Video captioning encoder-decoder semantic topic jointly decoding Enhance-TopK sampling

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Intelligent Orchestrating of IoT Microservices Based on Reinforcement Learning

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Chinese Journal of Electronics 2022年第5期31卷 930-937页

作者： WU Yuqin SHEN Congqi CHEN Shuhan WU Chunming LI Shunbin WEI Ruan College of Computer Science and Technology Zhejiang University College of Information and Electrical Engineering Ningde Normal University Zhejiang Lab College of Control Science and Engineering Zhejiang University

With the recent increase in the number of Internet of things(IoT) services, an intelligent scheduling strategy is needed to manage these services. In this paper, the problem of automatic choreography of microservices in IoT is explored. A type of reinforcement learning(RL) algorithm called TD3 is used to generate the optimal choreography policy under the framework of a softwaredefined network. The optimal policy is gradually reached during the learning procedure to achieve the goal, despite the dynamic characteristics of the network environment. The simulation results show that compared with other methods, the TD3 algorithm converges faster after a certain number of iterations, and it performs better than other non-RL algorithms by obtaining the highest reward. The TD3 algorithm can effciently adjust the traffic transmission path and provide qualified IoT services.

关键词： software-defined network telecommunication scheduling intelligent orchestrating nonRL algorithms reinforcement learning software defined networking optimal choreography policy reinforcement learning algorithm Internet of things services network environment IoT microservices TD3 algorithm traffic transmission path Internet of Things qualified IoT services intelligent scheduling strategy dynamic characteristics

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TwinDiffusion: Enhancing Coherence and Efficiency in Panoramic Image Generation with Diffusion Models 27

TwinDiffusion: Enhancing Coherence and Efficiency in Panoram...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Zhou, Teng Tang, Yongchuan College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9781643685489

Diffusion models have emerged as effective tools for generating diverse and high-quality content. However, their capability in high-resolution image generation, particularly for panoramic images, still faces challenges such as visible seams and incoherent transitions. In this paper, we propose TwinDiffusion, an optimized framework designed to address these challenges through two key innovations: the Crop Fusion for quality enhancement and the Cross Sampling for efficiency optimization. We introduce a training-free optimizing stage to refine the similarity of adjacent image areas, as well as an interleaving sampling strategy to yield dynamic patches during the cropping process. A comprehensive evaluation is conducted to compare TwinDiffusion with the prior works, considering factors including coherence, fidelity, compatibility, and efficiency. The results demonstrate the superior performance of our approach in generating seamless and coherent panoramas, setting a new standard in quality and efficiency for panoramic image generation. © 2024 The Authors.

关键词： Image enhancement

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StyleFactory: Towards Better Style Alignment in Image Creation through Style-Strength-Based Control and Evaluation 24

StyleFactory: Towards Better Style Alignment in Image Creati...

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37th Annual ACM Symposium on User Interface Software and technology, UIST 2024

作者： Zhou, Mingxu Zhang, Dengming You, Weitao Yu, Ziqi Wu, Yifei Pan, Chenghao Liu, Huiting Lao, Tianyu Chen, Pei College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China

ISBN: (纸本)9798400706288

Generative AI models have been widely used for image creation. However, generating images that are well-aligned with users' personal styles on aesthetic features (e.g., color and texture) can be challenging due to the poor style expression and interpretation between humans and models. Through a formative study, we observed that participants showed a clear subjective perception of the desired style and variations in its strength, which directly inspired us to develop style-strength-based control and evaluation. Building on this, we present StyleFactory, an interactive system that helps users achieve style alignment. Our interface enables users to rank images based on their strengths in the desired style and visualizes the strength distribution of other images in that style from the model's perspective. In this way, users can evaluate the understanding gap between themselves and the model, and define well-aligned personal styles for image creation through targeted iterations. Our technical evaluation and user study demonstrate that StyleFactory accurately generates images in specific styles, effectively facilitates style alignment in image creation workflow, stimulates creativity, and enhances the user experience in human-AI interactions. © 2024 ACM.

关键词： Human-Model Style Alignment

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A Study of the Effect of Image Semantic Distance on Design Outcomes in the Sketching Stage

A Study of the Effect of Image Semantic Distance on Design O...

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Liang, Yiming Bai, Yang Lou, Qianya Ying, Haotian Cai, Lei Zhang, Xusheng Sui, Haiyan College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9781643685069

In this paper, the mechanism of image stimulation is systematically sorted out, and the role of image stimulation in the conceptual design process as well as the advantages of choosing images as stimulus inspiration are theoretically argued, providing theoretical support for image stimulation to stimulate creativity. Secondly, the influencing factors of image stimuli with different semantic distances in the design sketch stage and their effects on the design results were explored. The study verifies that image stimuli with different semantic distances affect designers' behavioral performance during the design sketching process, which in turn affects the design results. Finally, a controlled experiment based on the Word2Vec word embedding method in the field of Natural Language Processing (NLP) was conducted to investigate the quantitative and qualitative effects of the image stimuli on the participants' design outcomes during the sketching phase. It was found that the design results produced by semantically distant image stimuli outperformed those produced by semantically closer image stimuli in terms of quantity and originality, which verifies that image stimuli with different semantic distances affect design creativity. © 2024 The Authors.

关键词： Conceptual design

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A Study of Dynamic Sculpture Prototyping Tools

A Study of Dynamic Sculpture Prototyping Tools

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2023 International Symposium on World Ecological Design, ISWED 2023

作者： Liu, Yu Cai, Lei Feng, Xinqi Bai, Yang Lou, Qianya Zhang, Xusheng Sui, Haiyan College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9781643685069

s. With the development of computer-aided design technology, more and more designers and artists have created installation art full of dynamic beauty to enrich the urban landscape, such as dynamic sculpture. This kind of dynamic sculpture needs to spend a lot of time and energy of the producer to produce digital models and product animation. This results in the artists not being able to utilize their creativity well. In order to propose an effective auxiliary design tool to reduce the artist's workload, this study first conducts a literature review on related theories and proposes a design method based on constrained morphology through curves. Based on this method, a complete design process was designed and a browser-based design tool, KINETICIST, was developed. Finally, user experiments were conducted in this study. The result proves that KINETICIST has qualified user experience performance under the dimensions of effectiveness, ease of interaction, and ease of learning, and has practical value. © 2024 The Authors.

关键词： computer aided design

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Information Re-Organization Improves Reasoning in Large Language Models 38

Information Re-Organization Improves Reasoning in Large Lang...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Cheng, Xiaoxia Tan, Zeqi Xue, Wei Lu, Weiming College of Computer Science and Technology Zhejiang University China

Improving the reasoning capabilities of large language models (LLMs) has attracted considerable interest. Recent approaches primarily focus on improving the reasoning process to yield a more precise final answer. However, in scenarios involving contextually aware reasoning, these methods neglect the importance of first identifying logical relationships from the context before proceeding with the reasoning. This oversight could lead to a superficial understanding and interaction with the context, potentially undermining the quality and reliability of the reasoning outcomes. In this paper, we propose an information re-organization (InfoRE) method before proceeding with the reasoning to enhance the reasoning ability of LLMs. Our re-organization method involves initially extracting logical relationships from the contextual content, such as documents or paragraphs, and subsequently pruning redundant content to minimize noise. Then, we utilize the re-organized information in the reasoning process. This enables LLMs to deeply understand the contextual content by clearly perceiving these logical relationships, while also ensuring high-quality responses by eliminating potential noise. To demonstrate the effectiveness of our approach in improving the reasoning ability, we conduct experiments using Llama2-70B, GPT-3.5, and GPT-4 on various contextually aware multi-hop reasoning tasks. Using only a zero-shot setting, our method achieves an average absolute improvement of 4% across all tasks, highlighting its potential to improve the reasoning performance of LLMs. Our source code is available at https://***/hustcxx/InfoRE. © 2024 Neural information processing systems foundation. All rights reserved.

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Magnetic-dielectric synergistic enhancement effect of anti-perovskite medium-entropy alloy nitride foams designed by lattice expansion engineering

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材料科学技术（英文版） 2025年第2期205卷 42-52页

作者： Wangchang Li Zengbao Ma Wanjia Li Lun Fan Yue Kang Ting Zou Xiao Han Yao Ying Wenbo Xiang Zhiwei Li Jing Yu Jingwu Zheng Liang Qiao Juan Li Min Wu Shenglei Che College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014China Research Center of Magnetic and Electronic Materials Zhejiang University of TechnologyHangzhou 310014China College of Materials Science and Engineering Zhejiang University of TechnologyHangzhou 310014China Systems Engineering Institute Beijing 100010China School of Physical Science and Technology Lanzhou UniversityLanzhou 730000China

A single-phase anti-perovskite medium-entropy alloy nitride foams(MEANFs),as innovative materials for electromagnetic wave(EMW)absorption,have been successfully synthesized through the lattice ex-pansion induced by nitrogen *** achievement notably overcomes the inherent constraints of conventional metal-based absorbers,including low resonance frequency,high conductivity,and elevated density,for the synergistic advantages provided by multimetallic alloys and *** analy-sis with comprehensive theoretical calculations provides in-depth insights into the formation mechanism,electronic structure,and magnetic moment of ***,deliberate component design along with the foam structure proves to be an effective strategy for enhancing impedance matching and *** results show that the MEANFs exhibit a minimum reflection loss(RLmin)value of-60.32 dB and a maximum effective absorption bandwidth(EABmax)of 5.28 GHz at 1.69 *** augmentation of energy dissipation in EMW is predominantly attributed to factors such as porous structure,interfacial polarization,defect-induced polarization,and magnetic *** study demonstrates a facile and efficient approach for synthesizing single-phase medium-entropy alloys,emphasizing their potential as materials for electromagnetic wave absorption due to their adjustable magnetic-dielectric properties.

关键词： Medium-entropy alloy nitride foams Anti-perovskite structure Lattice expansion Engineering Magnetic-dielectric synergistic Electromagnetic wave absorption

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