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Mitigating Social engineering Attacks Through Cost-Effective Security Awareness Training Policy

IEEE Transactions on Network Science and engineering 2025年

作者： Qin, Yang Yang, Xiaofan Yang, Lu-Xing Huang, Kaifan Chongqing University School of Big Data and Software Engineering Chongqing400044 China Deakin University School of Information Technology MelbourneVIC3125 Australia

Human beings are often considered the weakest link in cybersecurity. Social engineering attacks exploit this vulnerability, posing significant threats to the digital assets of organizations. A highly effective strategy to protect users from falling into traps set by attackers is to implement comprehensive security awareness training focused on social engineering. In this context, the organization needs to find a cost-effective policy of allocating the security awareness training cost. We refer to the problem of finding such a policy as the security awareness training (SAT) problem. This paper addresses the SAT problem. First, an opinion dynamics-based security awareness evolution model is introduced. On this basis, the SAT problem is reduced to an optimal control model (the SAT model). Second, by deriving the optimality system for the SAT problem, an algorithm of solving the SAT model is proposed. Next, the feasibility of the proposed algorithm is validated through numerical experiments. Additionally, further exploration of the SAT algorithm are conducted. Finally, for greater versatility, the problem is formulated as a discrete-time problem (the discrete SAT problem), and the discrete SAT algorithm is proposed to solve it. This work takes the first step toward the prevention of social engineering attack through optimal control approach. © 2013 IEEE.

关键词： Control theory

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing*

Group Signature with Time-Bound Keys for Secure E-health Rec...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junze Lu Chunqiang Hu Conghao Ruan Bin Cai Tao Xiang School of Big Data & Software Engineering Chongqing University China College of Computer Science Chongqing University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient.

关键词： Buildings Solids Security Time factors Internet of Things Bioinformatics

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PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECASTING 12

PERIODICITY DECOUPLING FRAMEWORK FOR LONG-TERM SERIES FORECA...

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12th International Conference on Learning Representations, ICLR 2024

作者： Dai, Tao Wu, Beiliang Liu, Peiyuan Li, Naiqi Bao, Jigang Jiang, Yong Xia, Shu-Tao College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China WeBank Institute of Financial Technology Shenzhen University China

Convolutional neural network (CNN)-based and Transformer-based methods have recently made significant strides in time series forecasting, which excel at modeling local temporal variations or capturing long-term dependencies. However, real-world time series usually contain intricate temporal patterns, thus making it challenging for existing methods that mainly focus on temporal variations modeling from the 1D time series directly. Based on the intrinsic periodicity of time series, we propose a novel Periodicity Decoupling Framework (PDF) to capture 2D temporal variations of decoupled series for long-term series forecasting. Our PDF mainly consists of three components: multi-periodic decoupling block (MDB), dual variations modeling block (DVMB), and variations aggregation block (VAB). Unlike the previous methods that model 1D temporal variations, our PDF mainly models 2D temporal variations, decoupled from 1D time series by MDB. After that, DVMB attempts to further capture short-term and long-term variations, followed by VAB to make final predictions. Extensive experimental results across seven real-world long-term time series datasets demonstrate the superiority of our method over other state-of-the-art methods, in terms of both forecasting performance and computational efficiency. Code is available at https://***/Hank0626/PDF. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Time series

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Updating Large Language Models' Memories with Time Constraints

Updating Large Language Models' Memories with Time Constrain...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Wu, Xin Bu, Yuqi Cai, Yi Wang, Tao School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Biostatistics and Health Informatics King's College London United Kingdom

ISBN: (纸本)9798891761681

By incorporating the latest external knowledge, large language models (LLMs) can modify their internal memory. However, in practical applications, LLMs may encounter outdated information, necessitating the filtering of such data and updating of knowledge beyond internal memory. This paper explores whether LLMs can selectively update their memories based on the time constraints between internal memory and external knowledge. We evaluate existing LLMs using three types of data that exhibit different time constraints. Our experimental results reveal the challenges most LLMs face with time-constrained knowledge and highlight the differences in how various LLMs handle such information. Additionally, to address the difficulties LLMs encounter in understanding time constraints, we propose a two-stage decoupling framework that separates the identification and computation of time constraint into a symbolic system. Experimental results demonstrate that the proposed framework yields an improvement of over 60% in ChatGPT's performance, and achieves a 12-24% enhancement in state-of-the-art LLM GPT-4. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Anatomical Prior Guided Spatial Contrastive Learning for Few-Shot Medical Image Segmentation 24

Anatomical Prior Guided Spatial Contrastive Learning for Few...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Wendong Hu, Jinwu Bi, Xiuli Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Post and Telecommunications Chongqing China Pazhou Lab School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Few-shot semantic segmentation has considerable potential for low-data scenarios, especially for medical images that require expert-level dense annotations. Existing few-shot medical image segmentation methods strive to deal with the task by means of prototype learning. However, this scheme relies on support prototypes to guide the segmentation of query images, ignoring the rich anatomical prior knowledge in medical images, which hinders effective feature enhancement for medical images. In this paper, we propose an anatomical prior guided spatial contrastive learning, called APSCL, which exploits anatomical prior knowledge derived from medical images to construct contrastive learning from a spatial perspective for few-shot medical image segmentation. The new framework forces the model to learn the features in line with the embedded anatomical representations. Besides, to fully exploit the guidance information of the support samples, we design a mutual guidance decoder to predict the label of each pixel in the query image. Furthermore, our APSCL can be trained end-to-end in the form of episodic training. Comprehensive experiments on three challenging medical image datasets, i.e., CHAOS-T2, MS-CMRSeg, and Synapse, prove that our method significantly surpasses state-of-the-art few-shot medical segmentation methods, with a mean improvement of 3.61%, 2.30%, and 6.38% on the Dice score, respectively. © 2024 ACM.

关键词： Contrastive Learning

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ShellFusion: An Answer Generator for Shell Programming Tasks via Knowledge Fusion

ShellFusion: An Answer Generator for Shell Programming Tasks...

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International Conference on software engineering - Companion, ICSE, Companion

作者： Zhongqi Chen Neng Zhang Pengyue Si Qinde Chen Chao Liu Zibin Zheng School of Software Engineering Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

Shell programming is widely used to accomplish various tasks in Unix and Linux platforms. However, the large number of shell commands available, e.g., 50,000+ commands are documented in the Ubuntu Manual Pages (MPs), makes it a big challenge for programmers to find appropriate commands for a task. Although there are some tutorials (e.g., TLDR) with examples manually created to address the challenge, the tutorials only cover a limited number of frequently used commands for shell beginners and provide limited support for users to search commands by a task. In this paper, we introduce a novel web-based tool, ShellFusion, which can automatically generate comprehensive answers (including relevant commands, scripts, and explanations) for shell programming tasks by fusing multisource knowledge mined from Q&A posts, Ubuntu MPs, and TLDR tutorials. Our evaluation on 434 shell programming tasks shows that ShellFusion significantly outperforms the state-of-the-art approaches by at least 179.6% in terms of MRR@K and MAP@K. A user study conducted with 20 shell programmers further shows that ShellFusion can help users address programming tasks more efficiently and accurately. ShellFusion Tool: http://***/ Demo Video: https://***/P0YJzpKBmnA

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Dynamic Hand Gesture Classification Based on Attention Mechanism and Transformer 3

Dynamic Hand Gesture Classification Based on Attention Mecha...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Ren, Zhen He, Haonan Huang, Zhengyi School of Big Data & Software Engineering Chongqing University Chongqing401331 China School of Mechanical & Automotive Engineering South China University of Technology Guangdong Guangzhou510641 China School of Electromechanical Engineering Guangdong University of Technology Guangdong Guangzhou510006 China

ISBN: (纸本)9781510667563

In the era of rapid development of deep learning, the popular transformer in recent years has played an outstanding role in many fields, but most of it focuses on the development of language models, and is still in the development stage in computer vision. Gesture recognition is a relatively popular module in computer vision, but the mainstream method still uses CNN for gesture recognition, and there are few articles combining gesture recognition with attention mechanism and transformer. In this paper, we propose a dynamic gesture recognition model based on attention mechanism and Transformer. In order to extract the valid information in each frame, we add the attention mechanism to the feature extraction network, followed by passing it into the transformer to predict the hand gesture through the self-attention mechanism. The model has been tested on the IsoGD dataset, achiving good experimental results. Not only do we confirm that the attention mechanism can improve the recognition accuracy through ablation study, but also prove that transformer is feasible to process and identify the temporal information among frames in dynamic gestures and perform recognition. © 2023 SPIE.

关键词： Gesture recognition

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Improving ChatGPT Prompt for Code Generation

arXiv

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arXiv 2023年

作者： Liu, Chao Bao, Xuanlin Zhang, Hongyu Zhang, Neng Hu, Haibo Zhang, Xiaohong Yan, Meng School of Big Data and Software engineering Chongqing University China School of Software Engineering Sun Yat-sen University China

Automated code generation can be a powerful technique for software development, significantly reducing developers’ efforts and time required to create new code by generating it automatically based on requirements. Recently, OpenAI’s language model ChatGPT has emerged as a powerful tool for generating human-like responses to a wide range of textual inputs (i.e., prompts), including those related to code generation. However, the effectiveness of ChatGPT for code generation is not well understood, and the generation performance could be heavily influenced by the choice of prompt. To answer these questions, we conducted experiments using the CodeXGlue dataset to evaluate ChatGPT’s capabilities for two code generation tasks, including text-to-code and code-to-code generation. We designed prompts by leveraging the chain-of-thought strategy with multi-step optimizations. Our results showed that by carefully designing prompts to guide ChatGPT, the generation performance can be improved substantially. We also analyzed the factors that influenced the prompt design and provided insights that could guide future research. Copyright © 2023, The Authors. All rights reserved.

关键词： software design

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A Collaborative Jamming Algorithm Based on Multi-UAV Scheduling 23

A Collaborative Jamming Algorithm Based on Multi-UAV Schedul...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Jiang, Yixin Zhou, Lingyun Tang, Yijia Tu, Ya Liu, Chunhong Shi, Qingjiang Science and Technology on Communication Information Security Control Laboratory Jiaxing314033 China School of Software Engineering Tongji University Shanghai201804 China Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9798350325959

In this paper, we consider the problem of multi-unmanned aerial vehicles' (UAVs), scheduling for cooperative jamming, where UAVs equipped with directional antennas perform collaborative jamming tasks against several targets of interest. To ensure effective jamming towards the targets, we formulate it as an non-convex optimization problem, aiming to minimize the communication performance of the targets by jointly optimizing UAVs' deployment and directional antenna orientations. Due to the unique structure of the problem, we derive an equivalent transformation by introducing a set of auxiliary matrices. Subsequently, we propose an efficient iterative algorithm based on the alternating direction method of multi-pliers (ADMM), which decomposes the problem into multiple tractable subproblems solved in closed-form or by gradient projection method. Extensive simulations validate the efficacy of the proposed algorithm. © 2023 IEEE.

关键词： ADMM improved Gradient Descend jamming system UAV deployment

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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