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Delay-CJ:A novel cryptojacking covert attack method based on delayed strategy and its detection

Digital Communications and Networks 2023年第5期9卷 1169-1179页

作者： Guangquan Xu Wenyu Dong Jun Xing Wenqing Lei Jian Liu Lixiao Gong Meiqi Feng Xi Zheng Shaoying Liu Big Data School Qingdao Huanghai UniversityQingdao266427China College of Intelligence and Computing Tianjin UniversityTianjin300350China School of Computing Macquarie UniversityNSW2109Australia Graduate School of Advanced Science and Engineering Hiroshima UniversityJapan

Cryptojacking is a type of resource embezzlement attack,wherein an attacker secretly executes the cryptocurrency mining program in the target host to gain *** has been common since 2017,and in fact,it once became the greatest threat to network *** better prove the attack ability the harm caused by cryptojacking,this paper proposes a new covert browser-based mining attack model named Delay-CJ,this model was deployed in a simulation environment for *** on the general framework of cryptojacking,Delay-CJ adds hybrid evasion detection techniques and applies the delayed execution strategy specifically for video websites in the prototype *** results show that the existing detection methods used for testing may become invalid as result of this *** view of this situation,to achieve a more general and robust detection scheme,we built a cryptojacking detection system named CJDetector,which is based on cryptojacking process ***,it identifies malicious mining by monitoring CPU usage and analyzing the function call *** system not only effectively detects the attack in our example but also has universal *** recognition accuracy of CJDetector reaches 99.33%.Finally,we tested the web pages in Alexa 50K websites to investigate cryptojacking activity in the real *** found that although cryptojacking is indeed on the decline,it remains a part of network security threats that cannot be ignored.

关键词： Cryptojacking Evasion techniques Delayed strategy Cryptocurrency mining Behavior-based detection

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Attention boosted Individualized Regression 38

Attention boosted Individualized Regression

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Guang Cao, Yuan Feng, Long Department of Data Science College of Computing City University of Hong Kong Hong Kong Department of Statistics and Actuarial Science School of Computing and Data Science The University of Hong Kong Hong Kong

Different from classical one-model-fits-all strategy, individualized models allow parameters to vary across samples and are gaining popularity in various fields, particularly in personalized medicine. Motivated by medical imaging analysis, this paper introduces a novel individualized modeling framework for matrix-valued data that does not require additional information on sample similarity for the individualized coefficients. Under our framework, the model individualization stems from an optimal internal relation map within the samples themselves. We refer to the proposed method as Attention boosted Individualized Regression, due to its close connections with the self-attention mechanism. Therefore, our approach provides a new interpretation for attention from the perspective of individualized modeling. Comprehensive numerical experiments and real brain MRI analysis using an ADNI dataset demonstrated the superior performance of our model. © 2024 Neural information processing systems foundation. All rights reserved.

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of Computer science & Technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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Large Graphs with Small Burning Numbers

Large Graphs with Small Burning Numbers

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International Conference on Computational Engineering, ICCE 2023

作者： Rajasingh, Angelin Jemima Parthiban, N. Department of Data Science and Business System School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

In this paper, we have determined the burning number of graphs with universal vertices and graphs that are generated using the graph operation namely ‘join of graphs’. © The Author(s), under exclusive license t... 详细信息

ISBN: (纸本)9783031777639

关键词： Burning number Join of graphs Universal vertex

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Feature Contrast Difference and Enhanced Network for RGB-D Indoor Scene Classification in Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 17610-17621页

作者： Zhou, Wujie Jian, Bitao Liu, Yuanyuan Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou310023 China China University of Geosciences School of Computer Science and Technology Wuhan430074 China Nanyang Technological University College of Computing and Data Science 308232 Singapore

The era of smart connectivity spawned by the Internet of Things (IoT) has made the need to achieve environmental perception and understanding of different scenarios increasingly urgent. Among the many scenarios, indoor scene classification has attracted much attention because of its its relevance to the daily lives of people, ranging from comfort regulation in living spaces to the optimal allocation of resources in offices, and a variety of approaches for this task have emerged. However, increasing accuracy remains a crucial objective due to the complexity and disorder of indoor scenes. Therefore, we propose a feature contrast difference and enhanced network for RGB-D indoor scene classification, FCDENet. First, the RGB and Depth images express different information. Therefore, we built a feature contrast difference module for the first two low-level features to extend the receptive fields of the different features, utilizing differential contrast to complement each other. Second, the high-level feature semantic information is abstract. Therefore, we introduced information cluster blocks, which are used to aggregate feature points with similar attributes into compact clusters after being parsed by an initial frequency transform, enabling instantiated representations of the semantic information. Finally, to further enhance the integrated features, we introduced a wavelet transform block in the cross-layer decoding process. In contrast to conventional decoding methods, we employed a wavelet transform for initial denoising cross-layer features and used multiple pooling structures to supplement local information, gradually weighting to achieve higher prediction accuracy. Extensive experiments on two typical indoor datasets, NYUDv2 and SUN RGB-D, show that our results exhibit excellent performance. In addition, to better demonstrate the reliability of the method, we conducted generalizability experiments on other datasets, and the proposed method provides a robust soluti

关键词： Wavelet decomposition

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Heterogeneous Parallel Key-Insulated Multi-Receiver Signcryption Scheme for IoV

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5123-5137页

作者： Hou, Yingzhe Cao, Yue Xiong, Hu He, Debiao Chi, Chi-Hung Lam, Kwok-Yan Wuhan University School of Cyber Science and Engineering Wuhan430000 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

The rapid growth of electric vehicle and autonomous vehicle populations has led to explosive expansion of IoV data being transmitted in the wireless communication infrastructure. Advances in IoV technologies also resulted in more complex and dynamic communication protocols/patterns, which are hard for the underlying wireless network to satisfy. Besides, security considerations of IoV communications require that key management must be stringently prohibit global failure mode of key management, meaning that, if a single IoV node compromises its private key, it will not lead to total security failure of the entire IoV network. To address these issues, in this paper, we propose a heterogeneous parallel key-insulated multi-receiver signcryption scheme for IoV (HPKI-MRSC). Firstly, the proposed scheme can realize one-to-many heterogeneous transmission, in which RSUs are deployed on certificateless cryptography (CLC) system, while vehicles are allocated in identity-based cryptography (IBC) system. In this manner, we observe that message transmission efficiency is improved greatly. Secondly, the parallel key-insulated mechanism can employ two helper keys to update private key periodically, and then solve key disclosure problem. Finally, when the number of receiver n is greater than or equal to 3, the proposed scheme has a lower signcryption overhead than other comparative schemes, and thus it is more suitable for IoV. © 2005-2012 IEEE.

关键词： Receivers Public key Protocols Encryption Polynomials Vehicle dynamics Electronic mail Training Symbols Real-time systems

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A Xgboost Optimized Ensemble Model for Remaining useful Life Prediction of Aircraft Turbofan Engines 6

A Xgboost Optimized Ensemble Model for Remaining useful Life...

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6th IEEE International Conference on Emerging Smart computing and Informatics, ESCI 2024

作者： Sharma, Vaasudev Dagar, Riyansh Sharanya, S. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India

ISBN: (纸本)9798350306613

Proactive maintenance for the optimized lifecycle of equipment is now effortless via condition monitoring and fault diagnosis. Recognition and avoidance of a state of failure help to achieve solidity and reliability. Artificial Intelligence and Machine Learning (ML) forecast such situations and aid in the prevention of economic and safety emergencies. This work reviews such ML algorithms and ML models that help diagnose faults in Turbofan engines via measurement of the Remaining Useful Life parameter. This proposed an ensemble machine-learning model optimized by Extreme Gradient Boosting. The approach is then evaluated via different metrics and their performance is measured to current innovative methods. The system is useful for all processes that require high reliability quality and efficiency and reduces unplanned downtime, maintenance costs and asset quality. © 2024 IEEE.

关键词： CMAPSS Ensemble Model Machine Learning Predictive Maintenance Remaining useful life (RUL) Turbofan Engine

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FPS Killer - A Moving Target Detection Algorithm in FPS Game Based on Yolov3 Frame and Gradient Descent Algorithm 2

FPS Killer - A Moving Target Detection Algorithm in FPS Game...

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2nd International Conference on Advancements in Smart, Secure and Intelligent computing, ASSIC 2024

作者： Huang, Jiayu Liu, Aofan School of Computing Jiangsu Vocational College of Commerce Jiangsu China School of Computing & Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (纸本)9798350370188

With the continuous development of the economic level of the society, the game industry is getting more and more extensive development among young people. As one of the mainstreams in the game industry, FPS games are warmly welcomed by the majority of players. In order to ensure the fairness of the game, anti-cheat technologies have emerged, such as 'BattlEye' and 'ValveAnti-Cheat'. This article applies the current popular computer vision technology to FPS games and proposes a cheating technology that cannot be detected by traditional anti-cheating methods. However, this project will only be used for academic research and discussion, and it is forbidden to use it for commercial sales and esports. © 2024 IEEE.

关键词： Object detection

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Interactive E-Commerce: Addressing Cart Abandonment through Gamified User Engagement

Interactive E-Commerce: Addressing Cart Abandonment through ...

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2024 International Conference on Communication, computing and Internet of Things, IC3IoT 2024

作者： Sohanda, Mayank Kumar Gupta, Mihir Kalpana, A.V. Srm Institute Of Science And Technology School Of Computing Department Of Data Science And Business Systems Kattankulathur India

ISBN: (纸本)9798350352689

This paper addresses the pervasive issue of cart abandonment in online shopping, a phenomenon that significantly impacts e-commerce revenue streams. Through an investigation into the underlying causes, we identify the lack of social engagement as a critical factor contributing to this behavior. Our primary objective is to bridge the gap between item selection and purchase completion by enhancing the user experience and boosting conversion rates. To tackle this challenge, we propose a novel approach by integrating gamification strategies into the online shopping experience. Our methods include implementing real-time user activity indicators, such as visual representations of active shoppers and interactive leaderboards, to foster a sense of community and competition among users. Additionally, we introduce social shopping features, such as cart sharing and real-time feedback systems, to promote user collaboration and interaction. Preliminary data analysis reveals a significant increase in conversion rates following the implementation of these gamification strategies. Users are more inclined to complete purchases within a community-centric and competitive environment. Moreover, the introduction of social elements has enriched the overall shopping experience, as evidenced by user feedback and heightened engagement on the platform. In conclusion, our study demonstrates the effectiveness of integrating gamification elements into online shopping platforms to combat cart abandonment. By creating a more engaging and communal shopping environment, users are not only motivated to complete purchases, but also to enjoy a more fulfilling shopping experience. Our findings highlight the potential of gamification as a powerful tool for enhancing e-commerce platforms and offer valuable insights for future advancements in online retail strategies. © 2024 IEEE.

关键词： Sales

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GAM-GCN: Decomposing Graph Convolutional Networks via Generalized Additive Models 15

GAM-GCN: Decomposing Graph Convolutional Networks via Genera...

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15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Liu, Chen Yu, Lequan Han, Kai Yin, Guosheng The University of Hong Kong School of Computing and Data Science Hong Kong Hong Kong

ISBN: (纸本)9798331508821

The vast adoption of graph neural networks (GNNs) in broad applications calls for versatile interpretability tools so that a better understanding of the GNNs' intrinsic structures can be gained. We propose an interpretable GNN framework to decompose the prediction into the additive combination of node features' main effects and the contributions of edges. The key component of our framework is the generalized additive model with the graph convolutional network (GAM-GCN) that allows for global node feature interpretations. The inherent interpretability of GAM and the expressive power of GCN are preserved and naturally connected. Further, the effects of neighboring edges are measured by edge perturbation and surrogate linear modeling, and the most important subgraph can be selected. We evaluate the proposed approach using extensive experiments and show that it is a useful tool for interpreting GNNs in the attempt to unlock the black box. © 2024 IEEE.

关键词： Graph neural networks

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