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Annual Computer Security Applications Conference

作者： Yanduo Fu Ding Wang College of Cyber Science Nankai University Tianjin China Key Laboratory of Data and Intelligent System Security (NKU) Ministry of Education Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (数字)9798331520885

ISBN: (纸本)9798331520892

Password managers (PMs) provide users with convenient and robust functionalities to manage their credentials, highly recommended by security experts and major standard bodies. One of the most popular features is the autofill functionality, with which users need a single click or a few clicks to fill in every field in web forms, facilitating the process of completing web forms. However, such indiscriminate autofill brings severe privacy threats. PMs may inadvertently fill data into wrong fields in web forms, even hidden fields, potentially leading to privacy leaks and credential *** this paper, we conduct an empirical study evaluating the effectiveness of 30 popular PMs in identifying and handling hidden fields. We focus on the privacy threats posed by the autofill functionality, which fills data into hidden fields. We develop a semi-automated autofill testing tool and explore whether PMs autofill sensitive data into hidden fields across 15 concealment techniques and three web forms, including personal information, credit card, and login forms. Experimental results reveal that every PM autofills data into hidden fields in at least one web form, with an overall filled probability of 58.7% in 1032 scenarios. Further analysis reveals that login forms are the most vulnerable, with a 65.7% probability of hidden fields autofill. Hidden fields concealed by clip-path and content-visibility are filled with passwords by all PMs. Besides, built-in-browser PMs exhibit a 4.07 times higher likelihood of filling data into hidden fields than separately-installed PMs. Even more concerning, built-in-browser PMs, except Safari, autofill passwords into hidden fields under any concealment technique. 37.7% of autofill scenarios with insufficient user interaction pose heightened privacy threats, as users are unaware of autofill content. These privacy threats have been confirmed by popular PMs like *** mitigate the threats brought by the autofill functionality, we present two a

关键词： Privacy data privacy Visualization Standards organizations Passwords Credit cards Filling data models Usability Testing

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Generating Event-oriented Attribution for Movies via Two-Stage Prefix-Enhanced Multimodal LLM

arXiv

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arXiv 2024年

作者： Lyu, Yuanjie Xu, Tong Niu, Zihan Peng, Bo Ke, Jing Chen, Enhong The Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science University of Science and Technology of China Hefei230026 China

The prosperity of social media platforms has raised the urgent demand for semantic-rich services, e.g., event and storyline attribution. However, most existing research focuses on clip-level event understanding, primarily through basic captioning tasks, without analyzing the causes of events across an entire movie. This is a significant challenge, as even advanced multimodal large language models (MLLMs) struggle with extensive multimodal information due to limited context length. To address this issue, we propose a Two-Stage Prefix-Enhanced MLLM (TSPE) approach for event attribution, i.e., connecting associated events with their causal semantics, in movie videos. In the local stage, we introduce an interaction-aware prefix that guides the model to focus on the relevant multimodal information within a single clip, briefly summarizing the single event. Correspondingly, in the global stage, we strengthen the connections between associated events using an inferential knowledge graph, and design an event-aware prefix that directs the model to focus on associated events rather than all preceding clips, resulting in accurate event attribution. Comprehensive evaluations of two real-world datasets demonstrate that our framework outperforms state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Knowledge-Enhanced and Structure-Enhanced Representation Learning for Protein-Ligand Binding Affinity Prediction

SSRN

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SSRN 2024年

作者： Li, Mei Cao, Ye Liu, Xiaoguang Ji, Hua College of Computer Science Nankai University Tianjin China College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

Protein-ligand binding affinity (PLA) prediction is an essential stage in drug screening and drug repositioning. Existing methods mainly focus on structurefree prediction of binding affinities and the investigation of structural PLA prediction is not fully explored yet. Spatial structures of protein-ligand complexes are critical in determining binding affinities. A few graph neural network (GNN) based methods model spatial structures of complexes with pairwise atomic distances within a cutoff, which provides insufficient spatial descriptions and limits their capabilities in distinguishing between certain molecules. In this paper, we propose a knowledge-enhanced and structureenhanced representation learning method (KSM) for structural PLA prediction. The proposed KSM has a specially designed structure-based GNN (KSGNN) to learn complete representations for PLA prediction by combining sequence and structure information of complexes. Notably, KSGNN is capable of learning structure-aware representations via incorporating relative spatial information of distances and angles among atoms into the message passing. Additionally, we adopt an attentive pooling layer (APL) to further refine structural patterns in complexes. We compare KSM against 25 state-of-the-art baselines on two benchmarks. Experimental results show that KSM considerably outperforms its competitors, demonstrating its superiority in binding affinity prediction. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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TDID: Transparent and Efficient Decentralized Identity Management with Blockchain

TDID: Transparent and Efficient Decentralized Identity Manag...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Hao, Jiakun Gao, Jianbo Xiang, Peng Zhang, Jiashuo Chen, Ziming Hu, Hao Chen, Zhong School of Computer Science Peking University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China Nanjing University State Key Lab for Novel Software Technology Jiangsu Nanjing China

ISBN: (纸本)9798350337020

Decentralized identity (DID) is an identity management framework aiming to return the ownership of an identity to its corresponding user. Recent studies propose to store the identifiers of DID issuers and implement identity management systems based on blockchain. However, existing systems cannot avoid identity tampering and verifiable credential abuse of decentralized identities, which makes the identity management opaque. In this paper, we propose TDID, a Transparent and efficient Decentralized IDentity management system with blockchain. The key insight behind TDID is to manage the registration and authentication of DIDs via smart contracts, and design Structured Merkle Patricia Tree (SMPT) as an underlying data structure to store identity data on blockchain. The smart contract based processes can improve transparency of decentralized identity management, while the SMPT data structure can realize efficient storage of DID data. We implement and evaluate TDID on different identity management operations, and the experimental results show that TDID can achieve about 3.1 times for write operation and 6.3 times for read operation while improving the transparency of DID management. © 2023 IEEE.

关键词： Blockchain data Structure Decentralized Identity Smart Contract

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A Security Analysis of Honey Vaults

A Security Analysis of Honey Vaults

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IEEE Symposium on Security and Privacy

作者： Fei Duan Ding Wang Chunfu Jia College of Cyber Science Nankai University Tianjin China Key Laboratory of Data and Intelligent System Security (NKU) Ministry of Education Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Honey encryption (HE) protected password vaults (called honey vaults) are promising tools that allow a user to store multiple passwords (called a password vault) and encrypt them with a master password using HE. In case password vaults are somehow leaked and the attackers launch offline password guessing, honey vaults can yield decoy password vaults for incorrect guesses, forcing an offline guessing attacker to interact with the authentication server to identify whether passwords in decrypted vaults are correct or not. Therefore, honey vaults transform the offline guessing attacker into an online guessing attacker, i.e., honey vault distinguishing *** online guessing, attackers can adopt various attacks to perform multiple guesses against multiple vaults, but the existing theoretical message recovery (MR) security for HE only focuses on the advantage of one-time guess against a single vault, which cannot accurately model realistic attackers and thus can not provide practical advice for users’ vault security. To address this issue, we propose a theoretically-grounded optimal strategy for distinguishing attackers, and manage to derive a much tighter upper bound on the advantage against MR security. Particularly, we provide much tighter upper/lower bounds for advantage against HE-related cryptographic security games, i.e., the security of distribution transforming encoder (DTE), known message attack, and known side information attack. This provides a better understanding of the actual security of honey *** better understand the security of honey vault systems, we instantiate our optimal strategy into three practical attacks and propose an encoding attack. Extensive experiments against two major honey vault systems demonstrate that our four attacks can improve the attack success rate by 1.15-4.35 times compared with their counterparts. For the intersection attack, we propose a feature attack against Cheng et al.’s incremental update mechanism (at USENI

关键词： Privacy Upper bound Authentication Passwords Games Transforms Encoding

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A Method to Explore the Synchronous Changes of High-Traffic Events Based on Dynamic networks 9

A Method to Explore the Synchronous Changes of High-Traffic ...

引用

9th International Conference on Fuzzy Systems and data Mining, FSDM 2023

作者： Huang, Yu-Wen Wang, Li-Na Zang, Chen-Rui College of Sciences Inner Mongolia University of Technology Hohhot010051 China Inner Mongolia Key Lab. of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot010051 China Inner Mongolia Branch China Unicom Hohhot010050 China

ISBN: (纸本)9781643684703

With the rapid development of mobile communication technologies, the mobile network has evolved into a highly heterogeneous network structure. Based on dynamic networks, we mainly investigated a method to explore the synchronous changes of high-traffic events. Event coincidence analysis is used to quantify the concurrency of high-traffic events. A variety of network measures are used to analyze the dynamic spatio-temporal characteristics of high-traffic. The static network is constructed to analyze the synchronous influence area and the temporal and spatial characteristics of high-traffic of base station. Taking hour as the time window, the dynamic network is constructed to study the dynamic spatio-temporal variation rule of high-traffic and the interactive relationship of traffic between base stations. It is found that static network is a small-world network. The spatial connectivity of high-traffic events at the base station is high, and the spatial connectivity is not sensitive to temporal changes. The traffic of different base stations has interactive relation at the same time in different days. © 2023 The authors and IOS Press.

关键词： Base stations

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Hypergraph-enhanced Dual Semi-supervised Graph Classification 41

Hypergraph-enhanced Dual Semi-supervised Graph Classificatio...

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41st International Conference on Machine Learning, ICML 2024

作者： Ju, Wei Mao, Zhengyang Yi, Siyu Qin, Yifang Gu, Yiyang Xiao, Zhiping Wang, Yifan Luo, Xiao Zhang, Ming School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University China School of Statistics and Data Science Nankai University China Department of Computer Science University of California Los Angeles United States School of Information Technology & Management University of International Business and Economics China

In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unlabeled graphs. Despite the promising capability of graph neural networks (GNNs), they typically require a large number of costly labeled graphs, while a wealth of unlabeled graphs fail to be effectively utilized. Moreover, GNNs are inherently limited to encoding local neighborhood information using message-passing mechanisms, thus lacking the ability to model higher-order dependencies among nodes. To tackle these challenges, we propose a Hypergraph-Enhanced DuAL framework named HEAL for semi-supervised graph classification, which captures graph semantics from the perspective of the hypergraph and the line graph, respectively. Specifically, to better explore the higher-order relationships among nodes, we design a hypergraph structure learning to adaptively learn complex node dependencies beyond pairwise relations. Meanwhile, based on the learned hypergraph, we introduce a line graph to capture the interaction between hyperedges, thereby better mining the underlying semantic structures. Finally, we develop a relational consistency learning to facilitate knowledge transfer between the two branches and provide better mutual guidance. Extensive experiments on real-world graph datasets verify the effectiveness of the proposed method against existing state-of-the-art methods. Copyright 2024 by the author(s)

关键词： Knowledge graph

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A Lightweight Anonymous key Agreement Scheme in V2G networks 2

A Lightweight Anonymous Key Agreement Scheme in V2G Networks

引用

2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022

作者： Chendong, Huang Feng, Wang Zhongming, Huang Fujian University of Technology School of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China

ISBN: (数字)9781665454766

ISBN: (纸本)9781665454766

Electric vehicle access to the grid (V2G) technology enables bidirectional interaction of energy and information between electric vehicles and the grid. The energy transactions between electric vehicles and the power grid is completed by the aggregators. Energy transactions are generally completed through open wireless communication networks, which are vulnerable to network attacks such as eavesdropping, impersonation attack, etc. data transmission in the V2G networks requires to verify the identity of each communication entity to achieve confidential communication. The realization of confidential communication can enhance the security of energy transactions. key agreement is a key technique to solve this problem. Therefore, we propose a lightweight anonymous key agreement scheme for V2G networks. For efficiency, we hire a data center to assist electric vehicles and aggregators in our key agreement scheme. Security analysis shows that our scheme can provide anonymity, resist eavesdropping, impersonation and replay attacks, etc. Performance analysis shows that our scheme has lower overall computational cost and communication overhead. © 2022 IEEE.

关键词： network security

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Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit networks

arXiv

引用

arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

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Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer

arXiv

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arXiv 2024年

作者： Zhu, Yongxin Su, Dan He, Liqiang Xu, Linli Yu, Dong School of Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent AI Lab China

While recent advancements in speech language models have achieved significant progress, they face remarkable challenges in modeling the long acoustic sequences of neural audio codecs. In this paper, we introduce Generative Pre-trained Speech Transformer (GPST), a hierarchical transformer designed for efficient speech language modeling. GPST quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a hierarchical transformer architecture, allowing for a unified one-stage generation process and enhancing Hi-Res audio generation capabilities. By training on large corpora of speeches in an end-to-end unsupervised manner, GPST can generate syntactically consistent speech with diverse speaker identities. Given a brief 3-second prompt, GPST can produce natural and coherent personalized speech, demonstrating in-context learning abilities. Moreover, our approach can be easily extended to spoken cross-lingual speech generation by incorporating multi-lingual semantic tokens and universal acoustic tokens. Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. The code is available at https://***/youngsheen/GPST. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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