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16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Yin, Sihan Luo, Xudong Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining Security School of Computer Science and Engineering Guilin China

ISBN: (纸本)9781665405539

Now e-commerce applications based on learning are continuously improving our daily life. These learning methods include graph neural networks, deep learning, machine learning, and reinforcement learning. More and more researchers use these methods to solve many problems in ecommerce, such as shopping, fraud detection, and product recommendation. To help researchers gain progress in the field, we survey the state-of-art work on the topic in this paper. Specifically, we focus on learning-based methods in typical ecommerce tasks: customer service, fraud detection and antifraud, cross-border transactions, and commerce forecast. We brief these methods, compare their pros and cons, and raise some problems for future research. © 2021 IEEE.

关键词： Reinforcement learning

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Harnessing Hierarchical label Distribution Variations in Test Agnostic Long-tail Recognition 41

Harnessing Hierarchical Label Distribution Variations in Tes...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Zhiyong Xu, Qianqian Wang, Zitai Li, Sicong Han, Boyu Bao, Shilong Cao, Xiaochun Huang, Qingming School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Tech. CAS China Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China BDKM University of Chinese Academy of Sciences China

This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced. We argue that the variation in these distributions can be broken down hierarchically into global and local levels. The global ones reflect a broad range of diversity, while the local ones typically arise from milder changes, often focused on a particular neighbor. Traditional methods predominantly use a Mixture-of-Expert (MoE) approach, targeting a few fixed test label distributions that exhibit substantial global variations. However, the local variations are left unconsidered. To address this issue, we propose a new MoE strategy, DirMixE, which assigns experts to different Dirichlet meta-distributions of the label distribution, each targeting a specific aspect of local variations. Additionally, the diversity among these Dirichlet meta-distributions inherently captures global variations. This dual-level approach also leads to a more stable objective function, allowing us to sample different test distributions better to quantify the mean and variance of performance outcomes. Theoretically, we show that our proposed objective benefits from enhanced generalization by virtue of the variance-based regularization. Comprehensive experiments across multiple benchmarks confirm the effectiveness of DirMixE. The code is available at https://***/scongl/DirMixE. Copyright 2024 by the author(s)

关键词： Benchmarking

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A Survey of Learning-Based Methods for Cold-Start, Social Recommendation, and Data Sparsity in E-commerce Recommendation Systems 16

A Survey of Learning-Based Methods for Cold-Start, Social Re...

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16th IEEE International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2021

作者： Yin, Sihan Luo, Xudong Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining Security School of Computer Science and Engineering Guilin China

ISBN: (纸本)9781665405539

With the continuous development of the economy and technology, people more and more rely on online shopping, especially during the pandemic of COVID19. On the other hand, sellers display many products, so customers need to make a great effort to find suitable products to meet their needs. To reduce the efforts of customers, researchers have developed many recommendation systems for online products. In this paper, to help further study recommendation systems in e-commerce, we survey the learning-based methods for solving the cold-start problem in a recommendation, social recommendation, and data sparsity. In particular, we compare these methods' pros and cons and point out the directions for further study. © 2021 IEEE.

关键词： Recommender systems

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities 24

On the Effectiveness of Function-Level Vulnerability Detecto...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Li, Zhen Wang, Ning Zou, Deqing Li, Yating Zhang, Ruqian Xu, Shouhuai Zhang, Chao Jin, Hai Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hong Kong Jin YinHu Laboratory Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Colorado Colorado Springs Department of Computer Science Colorado Springs Colorado United States Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798400702174

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers;and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities. © 2024 ACM.

关键词： Open source software

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Data security and Privacy in Bitcoin System:A Survey

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Journal of computer Science & Technology 2020年第4期35卷 843-862页

作者： Lie-Huang Zhu Bao-Kun Zheng Meng Shen Feng Gao Hong-Yu Li Ke-Xin Shi School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Information Management for Law China University of Political Science and LawBeijing 102249China Key Laboratory of Information Network Security Ministry of Public SecurityShanghai 201204China

To date,bitcoin has been the most successful application of blockchain technology and has received considerable attention from both industry and *** is an electronic payment system based on cryptography rather than on *** of whether people are in the same city or country,bitcoin can be sent by any one person to any other person when they reach an *** market value of bitcoin has been rising since its advent in 2009,and its current market value is US160 *** its development,bitcoin itself has exposed many problems and is facing challenges from all the sectors of society;therefore,adversaries may use bitcoin's weakness to make considerable *** survey presents an overview and detailed investigation of data security and privacy in bitcoin *** examine the studies in the literature/Web in two categories:1)analyses of the attacks to the privacy,availability,and consistency of bitcoin data and 2)summaries of the countermeasures for bitcoin data *** on the literature/Web,we list and describe the research methods and results for the two *** compare the performance of these methods and illustrate the relationship between the performance and the ***,we present several important open research directions to identify the follow-up studies in this area.

关键词： security privacy bitcoin availability consistency

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Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation

arXiv

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arXiv 2024年

作者： Liu, Yujing Wu, Zongqian Lu, Zhengyu Nie, Ci Wen, Guoqiu Hu, Ping Zhu, Xiaofeng Guangxi Key Lab of Multisource Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data. Specifically, we first employ multiple self-supervised learning methods to train diverse teacher models, and then aggregate their predictions through a teacher weight matrix. Furthermore, we design a new bi-level optimization strategy to dynamically adjust the teacher weight matrix based on the training progress of the student model. Finally, we design a label improvement module to improve the label quality. Extensive experimental results on real datasets show that our method achieves the best results compared to state-of-the-art methods. The source code is listed in Supplementary Materials. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

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Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction

arXiv

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arXiv 2024年

作者： Wu, Yuan Yan, Zhiqiang Wang, Zhengxue Li, Xiang Hui, Le Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China College of Computer Science Nankai University Tianjin300071 China Electronics and Information Northwestern Polytechnical University Xi’an710072 China

The task of vision-based 3D occupancy prediction aims to reconstruct 3D geometry and estimate its semantic classes from 2D color images, where the 2D-to-3D view transformation is an indispensable step. Most previous methods conduct forward projection, such as BEVPooling and VoxelPooling, both of which map the 2D image features into 3D grids. However, the current grid representing features within a certain height range usually introduces many confusing features that belong to other height ranges. To address this challenge, we present Deep Height Decoupling (DHD), a novel framework that incorporates explicit height prior to filter out the confusing features. Specifically, DHD first predicts height maps via explicit supervision. Based on the height distribution statistics, DHD designs Mask Guided Height Sampling (MGHS) to adaptively decouple the height map into multiple binary masks. MGHS projects the 2D image features into multiple subspaces, where each grid contains features within reasonable height ranges. Finally, a Synergistic Feature Aggregation (SFA) module is deployed to enhance the feature representation through channel and spatial affinities, enabling further occupancy refinement. On the popular Occ3D-nuScenes benchmark, our method achieves state-of-the-art performance even with minimal input frames. Source code is released at https://***/yanzq95/DHD. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Adversarial-Inspired Backdoor Defense via Bridging Backdoor and Adversarial Attacks 39

Adversarial-Inspired Backdoor Defense via Bridging Backdoor ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yin, Jia-Li Wang, Weijian Lyhwa Lin, Wei Liu, Ximeng Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350118 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350118 China College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Lion Rock Labs of Cyberspace Security CTIHE Hong Kong

ISBN: (纸本)157735897X

Backdoor attacks and adversarial attacks are two major security threats to deep neural networks (DNNs), with the former one is a training-time data poisoning attack that aims to implant backdoor triggers into models by injecting trigger patterns into training samples, and the latter one is a testing-time attack trying to generate adversarial examples (AEs) from benign images to mislead a well-trained model. While previous works generally treat these two attacks separately, the inherent connection between these two attacks is rarely explored. In this paper, we focus on bridging backdoor and adversarial attacks and observe two intriguing phenomena when applying adversarial attacks on an infected model implanted with backdoors: 1) the sample is harder to be turned into an AE when the trigger is presented;2) the AEs generated from backdoor samples are highly likely to be predicted as its true labels. Inspired by these observations, we proposed a novel backdoor defense method, dubbed Adversarial-Inspired Backdoor Defense (AIBD), to isolate the backdoor samples by leveraging a progressive top-q scheme and break the correlation between backdoor samples and their target labels using adversarial labels. Through extensive experiments on various datasets against six state-of-the-art backdoor attacks, the AIBD-trained models on poisoned data demonstrate superior performance over the existing defense methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Violence Video Detection Based on Multi-modal Fusion and Dual Channel Contrastive Learning

Violence Video Detection Based on Multi-modal Fusion and Dua...

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Human-Centered Cognitive Systems (HCCS)

作者： Huan Li Zhijian Bai Yue Li Tao Qin Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China MOE Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an Chin

There are more and more violence videos propagated on the Internet, it is important to detect them to provide support for online social network management. In the traditional detection methods, the utilization of different modal data is insufficient. In this paper, we propose a violence detection method based on multimodal fusion and dual channel contrastive learning. Firstly, an adaptive feature fusion method based on the attention mechanism is proposed. The problem of original information missing in the fused features is effectively solved by adaptively weighting the original modal features and the attention fusion features. And then the similarity of the same data representations is improved by contrastive learning between different feature fusion methods, in turn, the local problems caused by different fusion methods are solved. Experimental results based on public datasets show that the proposed method can learn the audio and video features and then generate high quality multimodal fusion features, the detection accuracy is improved by 1.3% to 4.8% compared with popular violence detection methods.

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Simulating Cyber Attacks and Designing Malware using Python 10

Simulating Cyber Attacks and Designing Malware using Python

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10th International Conference on Signal Processing and Integrated networks, SPIN 2023

作者： Ankit, Akash Inder, Shubham Sharma, Anuj Johari, Rahul Vidyarthi, Deo Prakash University School of Information Communication and Technology Guru Gobind Singh Indraprastha University SWINGER: Security Wireless IoT Network Group of Engineering and Research Dwarka New Delhi110078 India MelbourneVIC3000 Australia Jawaharlal Nehru University Parallel and Distributed System Lab School of Computer and Systems Sciences New Delhi110067 India

ISBN: (纸本)9781665490993

In this Internet era, we have become heavily dependent on the World Wide Web and it has become a basic amenity for humans. A normal user can be trapped easily in this vicious web if not careful enough. It is a field that requires users to be on their toes every time they try to connect to this enormous interconnection of networks. Reports and Surveys have said that in the coming years, Cybercrime could become the biggest threat. In this proposed work, the authors have deployed applications and designed malware for the purpose of simulating various kinds of cyber-attacks so that they could study their effects & affects and ultimately devise solutions to resolve the vulnerabilities in a system. These applications included networking concepts & protocols and a software based keylogger which was developed with no malicious intent, but to analyse the architectural view or design of a malware and to study the stages of a malware attack like entry, distribution, exploitation, infection and execution. Efforts have been made to demonstrate spooting attacks with an idea of identifying vulnerabilities and security loopholes in a particular system that can be exploited by an attacker if not resolved within time. © 2023 IEEE.

关键词： network security

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