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International Joint Conference on Neural networks (IJCNN)

作者： Yuecheng Wen Xiaohui Han Wenbo Zuo Weihua Liu 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 Quancheng Provincial Laboratory Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In the realm of cybersecurity, efficiently and precisely identifying and mitigating potential threats from malicious encrypted traffic is crucial. As deep learning evolves, methods relying on Convolutional Neural networks (CNNs) to convert traffic payloads into visual representations for analysis have become popular. Although this technique effectively discerns malicious encrypted traffic, it faces inherent limitations, such as difficulties in deployment due to extensive network scales and high computational demands, especially in edge computing environments. To address these challenges, we propose a model for maliciously encrypted traffic detection, named LightMETD. The model consists of two stages. In the first stage, we utilize Random Forest (RF) with a feature selection algorithm to quickly identify easily distinguishable traffic patterns. In the second stage, the filtered traffic data is converted into grayscale images and classified using an innovative lightweight MobileNetV3-S architecture. Our LightMETD model addresses the problem of large volume and high computational requirements of CNN models. To enhance the effectiveness of the LightMETD, we propose an innovative knowledge distillation method to train LightMETD. This method significantly improves the accuracy of malicious encrypted traffic detection by designing a distillation loss function that enables the model to better capture the intricate relationships between samples. We use a USTC-TFC2016 public traffic dataset and a locally collected dataset to demonstrate the effectiveness of LightMETD. LightMETD achieves an impressive 97% average classification accuracy. LightMETD significantly outperforms other baselines in terms of volume and detection speed, underscoring its viability and superiority in addressing real-world challenges posed by malicious encrypted traffic.

关键词： Training Solid modeling Visualization Accuracy Computational modeling Telecommunication traffic Traffic control

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SyntaxBridge: Protocol Description Transformer for Enhanced Formal Analysis of Security Protocols

SyntaxBridge: Protocol Description Transformer for Enhanced ...

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

作者： Liujia Cai Tong Yu Yumeng Li Siqi Lu Hanjie Dong Guangying Cai Guangong Li Yongjuan Wang Henan Key Laboratory of Network Cryptography Technology Information Engineering University Zhengzhou China School of Information and Communication Engineering Hainan University Haikou China School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Currently, formal analysis of security protocols under the symbolic model has become a crucial approach in the field of protocol analysis, owing to its intuitiveness and universality. However, researchers using various symbolic security protocol verifiers need to thoroughly understand the strengths and weaknesses of these verifiers, as well as their specific encoding models. A&B protocol description and CAS+ protocol description are highly favored by researchers due to their intuitiveness, but their limitation lies in the fact that these two descriptions can only be used for their corresponding verifiers. In this study, we constructed a bridge connecting different symbolic security protocol verifiers. Building upon existing compilers A&B to TAMARIN and CAS+ to AVISPA, we extended A&B to CAS+ and CAS+ to A&B using the lexical analyzer Flex and the parser generator tool Bison. Thus, this bridge enables leveraging the respective strengths of each symbolic security protocol verifier in a short period. It also greatly facilitates security protocol researchers in analyzing protocols using intuitive protocol descriptions through various formal analysis tools.

关键词： Bridges Analytical models Privacy Protocols Flexible printed circuits Transformers Generators Encoding Grammar Security

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LIFT+: Lightweight Fine-Tuning for Long-Tail Learning

arXiv

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arXiv 2025年

作者： Shi, Jiang-Xin Wei, Tong Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Artificial Intelligence Nanjing University Nanjing210023 China School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm has emerged as a prominent approach for addressing long-tail learning tasks in the era of foundation models. However, the impact of fine-tuning strategies on long-tail learning performance remains unexplored. In this work, we disclose that existing paradigms exhibit a profound misuse of fine-tuning methods, leaving significant room for improvement in both efficiency and accuracy. Specifically, we reveal that heavy fine-tuning (fine-tuning a large proportion of model parameters) can lead to non-negligible performance deterioration on tail classes, whereas lightweight fine-tuning demonstrates superior effectiveness. Through comprehensive theoretical and empirical validation, we identify this phenomenon as stemming from inconsistent class conditional distributions induced by heavy fine-tuning. Building on this insight, we propose LIFT+, an innovative lightweight fine-tuning framework to optimize consistent class conditions. Furthermore, LIFT+ incorporates semantic-aware initialization, minimalist data augmentation, and test-time ensembling to enhance adaptation and generalization of foundation models. Our framework provides an efficient and accurate pipeline that facilitates fast convergence and model compactness. Extensive experiments demonstrate that LIFT+ significantly reduces both training epochs (from ∼100 to ≤15) and learned parameters (less than 1%), while surpassing state-of-the-art approaches by a considerable margin. The source code is available at https://***/shijxcs/LIFT-plus. Copyright © 2025, The Authors. All rights reserved.

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PTGFI: A Prompt-Based Two-Stage Generative Framework for Function Name Inference

PTGFI: A Prompt-Based Two-Stage Generative Framework for Fun...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Menglu Wang Xiaohui Han Peipei Wang Wenbo Zuo 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)9781665410205

ISBN: (纸本)9781665410212

In the field of cybersecurity, analyzing malicious software or programs is crucial for preventing network attacks. Malicious code often exists in a stripped binary form to thwart analysis, presenting challenges for analysts. This study investigates inferring function names from stripped binary to aid security researchers in analyzing malicious code. We propose PTGFI, a Prompt-based Two-stage Generative framework for Function name Inference. The PTGFI framework transforms the task of inferring function names into a two-stage semantic generation problem. By capturing function descriptions of assembly functions and introducing prompt learning, effective inference of function names is achieved. In experiments, PTGFI outperforms the state-of-the-art model by 2.96 % in precision. Moreover, ablation studies demonstrate the effectiveness of advanced components within the PTGFI framework. We further validate the utility and reliability of function names generated by the PTGFI framework through case studies.

关键词： Analytical models Codes Accuracy Semantics Transforms Malware Reliability computer crime Assembly Cybernetics

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SyntaxBridge: Protocol Description Transformer for Enhanced Formal Analysis of Security Protocols 23

SyntaxBridge: Protocol Description Transformer for Enhanced ...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Cai, Liujia Yu, Tong Li, Yumeng Lu, Siqi Dong, Hanjie Cai, Guangying Li, Guangong Wang, Yongjuan Information Engineering University Henan Key Laboratory of Network Cryptography Technology Zhengzhou China Hainan University School of Information and Communication Engineering Haikou China Zhengzhou University School of Computer Science and Artificial Intelligence Zhengzhou China

关键词： HTTP

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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System 25

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhang, Jinsong Mao, Xiaoxuan Li, Lu Wu, Hanqian Joint Graduate School Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration of Ministry of Education Nanjing China

ISBN: (纸本)9798350330014

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our Thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability. © 2023 IEEE.

关键词： Decision making

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Coronavirus Pandemic Analysis Through Tripartite Graph Clustering in Online Social networks

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Big Data Mining and Analytics 2021年第4期4卷 242-251页

作者： Xueting Liao Danyang Zheng Xiaojun Cao Department of Computer Science Georgia State UniversityAtlantaGA 30302USA Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information EngineeringSoochow UniversitySuzhou 215006China

The COVID-19 pandemic has hit the world *** reaction to the pandemic related issues has been pouring into social platforms,such as *** public officials and governments use Twitter to make policy *** keep close track of the related information and express their concerns about the policies on *** is beneficial yet challenging to derive important information or knowledge out of such Twitter *** this paper,we propose a Tripartite Graph Clustering for Pandemic Data Analysis(TGC-PDA)framework that builds on the proposed models and analysis:(1)tripartite graph representation,(2)non-negative matrix factorization with regularization,and(3)sentiment *** collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth *** framework can detect the communities of Twitter users and analyze the topics that are discussed in the *** extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions,which would provide policy makers useful information and statistics for decision making.

关键词： COVID-19 clustering online social network Twitter

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Video Summarization Method Integrating Universal Demand Elements

SSRN

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SSRN 2024年

作者： Tong, Jianglei Gui, Xiaolin Teng, Xiaoyu Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'An710049 China Shaanxi Province Key Laboratory of Computer Network Xi'An710049 China School of Information Xi'an University of Finance and Economics Xi'An710100 China

Given the lack of linkage between demand analysis and video elements in current video summary algorithms, it is difficult to cover user needs with summary results. In this paper, a video summary method integrating universal demand elements is proposed. This method initially parses coarse-grained user demands into four universal demand elements: characters, locations, representative objects, and events. Subsequently, it employs a feature extraction module designed using ConvLSTM to explore the spatiotemporal relationships of these demand elements within keyframes. Furthermore, the method combines a spatial pyramid and multi-head attention mechanism to design a main-storyline detection module. Additionally, the paper introduces and designs a storyline relevance evaluation metric to assess the similarity between machine-generated and actual summaries in terms of universal demand elements. We evaluated our algorithm on two benchmark datasets and the results demonstrate its feasibility and effectiveness, along with superior transferability. © 2024, The Authors. All rights reserved.

关键词： Video recording

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A Real-time Cutting Model Based on Finite Element and Order Reduction

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computer Systems Science & Engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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