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Unveiling the Superiority of Unsupervised Learning on GPU Cryptojacking Detection: Practice on Magnetic Side Channel-based Mechanism

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4874-4889页

作者： Cao, Hangcheng Xu, Guowen He, Ziyang Shi, Shaoqing Xu, Shengmin Wu, Cong Ning, Jianting Hunan University College of Computer Science and Electronic Engineering China City University of Hong Kong Department of Computer Science Hong Kong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Zhengzhou University School of Cyber Science and Engineering Zhengzhou China Zhengzhou University International College Zhengzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou China Nanyang Technological University College of Computing and Data Science Singapore Wuhan University School of Cyber Science and Engineering Wuhan China City University of Macau Faculty of Data Science China

Ample profits of GPU cryptojacking attract hackers to recklessly invade victims’ devices, for completing specific cryptocurrency mining tasks. Such malicious invasion undoubtedly obstructs normal device usage and wastes computation resources. To resist the threat of GPU cryptojacking, existing works aim to timely detect and clear away it, by distinguishing the dissimilitude between it and legitimate applications. However, these detection mechanisms inappropriately rely on two conflict cornerstones, manifested in leveraging mutable samples of illegitimate cryptojacking to design supervision-based detection models requiring samples with stable patterns. This limitation compromises the practicability of existing detection mechanisms in the face of mutable cryptojacking samples. To fill the gap, we explore the superiority of unsupervised learning in handling this issue and further propose an unsupervised manner-enabled detection mechanism named MagInspector, only using legitimate applications’ magnetic signatures from GPU side channels for model construction. MagInspector innovates in training an unsupervised autoencoder network by an adversarial mode that well learns the stable signature patterns of legitimate applications, while incompatible with mutable cryptojacking ones. In the process of model training, we elaborately extract mutual energy cumulation distribution features to represent legitimate applications to overcome the impact of their inter-type differences. Meanwhile, a locality sensitive hashing-driven outlier removal algorithm is designed to enhance MagInspector’s robustness to the noise samples. Finally, extensive experiments are conducted on GPUs covering four generations of common NVIDIA architectures and two generations of AMD architectures;the results show that applying MagInspector to mutable cryptojacking signature detection achieves a significant average accuracy improvement of 25.5% and 17.8%, respectively. © 2005-2012 IEEE.

关键词： Unsupervised learning

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Temporal fusion transformer-based strategy for efficient multi-cloud content replication

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PeerJ computer science 2025年 11卷 1-22页

作者： Naganandhini, S. Shanthi, D. Computer Science and Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul India

In cloud computing, ensuring the high availability and reliability of data is dominant for efficient content delivery. Content replication across multiple clouds has emerged as a solution to achieve the above. However, managing optimal replication while considering dynamic changes in data popularity and cloud resource availability remains a formidable challenge. In order to address these challenges, this article employs TFT-based Dynamic data Replication Strategy (TD2RS), leveraging the Temporal Fusion Transformer (TFT), a deep learning temporal forecasting model. This proposed system collects historical data on content popularity and resource availability from multiple cloud sources, which are then used as input to TFT. Then TFT is used to capture temporal patterns and forecasts future data demands. An intelligent replication is performed to optimize content replication across multiple cloud environments based on these forecasts. The framework’s performance was validated through extensive experiments using synthetic time-series data simulating with varied cloud resource characteristics. Some of the findings include that the proposed TFT approach improves the availability of data by 20% when compared to traditional replication techniques and also cuts down the latency level by 15%. These outcomes indicate that the TFT-based replication strategy targets to improve content delivery efficiency in the dynamic cloud computing environment, thus providing effective solution to dynamically address the availability, reliability, and performance challenges. © Copyright 2025 S. and D.

关键词： Spatio-temporal data

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Generative AI-aided Vertical Handover Decision in SAGIN for IoT with Integrated Sensing and Communication

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IEEE Internet of Things Journal 2025年第10期12卷 13297-13310页

作者： Tao, Songjing Yuan, Meng Wu, Qiang Wang, Ran Hao, Jie Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China

As an advanced form of IoT technology, Integrated Sensing and Communication (ISAC) deeply integrates communication and perception, enhancing the performance and application range of IoT. At the same time, the Space-Air-Ground Integrated Network (SAGIN) provides a wider and more efficient connection and information processing support for both. However, the highly dynamic and time-varying characteristics of SAGIN lead to more frequent vertical handovers among heterogeneous wireless networks, which seriously affects the continuity and reliability of services. This motivates us to explore an effective vertical handover method in SAGIN to guarantee the quality of network service. The issue is a typical complex and high-dimensional problem with its online and dynamic characteristics, which provides a particularly favorable scenario for the adaptability of the Diffusion Model (DM). Accordingly, we propose a novel vertical handover decision algorithm with the aid of DM. Firstly, we innovate a novel vertical handover analytical model that describes handover jitter, load difference, and handover robustness. Then we formulate it as a multi-objective optimization problem. Next, inspired by Generative AI (GAI), we propose a DM-based GAI-empowered Handover Decision (DGHD) algorithm to capture the time-varying and high-dimensional environments and generate optimal vertical handover decisions. Subsequently, the policy network of Multi-Agent Proximal Policy Optimization (MAPPO) is replaced with the proposed DGHD for addressing environmental uncertainty and enhancing efficiency. Finally, the simulations exhibit that our proposed algorithm outperforms existing algorithms. © 2014 IEEE.

关键词： Handover Space-air-ground integrated networks Satellites Heuristic algorithms Optimization Internet of Things Heterogeneous networks Vehicle dynamics Robustness Jitter

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Image smoothing algorithm based on texture intensity adaptation and edge consistency

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Multimedia Systems 2025年第3期31卷 1-17页

作者： Long, Jianwu Luo, Qi Zhang, Chen College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China

Image smoothing techniques that can retain the significant structural information of an image and filter out texture and noise are particularly important. Aiming at the problems that existing methods do not deal with texture variations adequately and fail to efficiently mine and utilize the structural information of image edges, this paper proposes the texture intensity adaptive edge consistency network model (TIAEC-Net). In order to enhance the texture filtering effect, this paper proposes the texture intensity adaptive module, which captures the changes in texture information more accurately by predicting the salient structural information and applying the attention mechanism to filter the features. In the smoothing process, the method of extracting edge information and feature fusion with other input features is used to ensure that significant edge structures are not over-smoothed. Meanwhile, an edge consistency module is proposed to enhance the edge protection capability of the model by analyzing the edge response consistency relationship between the input image and the output image. In addition, a weak structure reinforce module is used to enhance the edge details in the image. After experimental validation, the TIAEC-Net model performs excellently in preserving the significant edge structure of the image and outperforms most of the current methods on publicly available datasets. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Edge detection

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Fake Face Detection Based on Fusion of Spatial Texture and High-Frequency Noise

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Chinese Journal of Electronics 2025年第1期34卷 212-221页

作者： Dengyong Zhang Feifan Qi Jiahao Chen Jiaxin Chen Rongrong Gong Yuehong Tian Lebing Zhang Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Changsha University of Science and Technology School of Computer and Communication Engineering Changsha University of Science and Technology Changsha Social Work College Changkuangao Beijing Technology Co. Ltd. School of Computer and Artificial Intelligence Huaihua University

The rapid development of the Internet has led to the widespread dissemination of manipulated facial images, significantly impacting people's daily lives. With the continuous advancement of Deepfake technology, the generated counterfeit facial images have become increasingly challenging to distinguish. There is an urgent need for a more robust and convincing detection method. Current detection methods mainly operate in the spatial domain and transform the spatial domain into other domains for analysis. With the emergence of transformers, some researchers have also combined traditional convolutional networks with transformers for detection. This paper explores the artifacts left by Deepfakes in various domains and, based on this exploration, proposes a detection method that utilizes the steganalysis rich model to extract high-frequency noise to complement spatial features. We have designed two main modules to fully leverage the interaction between these two aspects based on traditional convolutional neural networks. The first is the multi-scale mixed feature attention module, which introduces artifacts from high-frequency noise into spatial textures, thereby enhancing the model's learning of spatial texture features. The second is the multi-scale channel attention module, which reduces the impact of background noise by weighting the features. Our proposed method was experimentally evaluated on mainstream datasets, and a significant amount of experimental results demonstrate the effectiveness of our approach in detecting Deepfake forged faces, outperforming the majority of existing methods.

关键词： Deepfakes Adaptation models Attention mechanisms Noise Transforms Transformers Feature extraction Internet Convolutional neural networks Faces

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DEN: Depth Enhancement Network for 3D Object Detection with the Fusion of mmwave Radar and Vision in Autonomous Driving

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IEEE Internet of Things Journal 2025年第10期12卷 14420-14430页

作者： Wang, Wenxiang Han, Jianping Jiang, Zhongmin Zhou, Zhiyuan Wu, Yingxiao Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China

In the realm of autonomous driving, precise and robust 3D perception is paramount. Multi-modal fusion for 3D object detection is crucial for improving accuracy, generalization, and robustness in autonomous driving. In this paper, we introduce the Depth Enhancement Network (DEN), an innovative camera-radar fusion framework that generates an accurate depth estimation for 3D object detection. To overcome the lack of spatial information To overcome the limitations caused by the lack of spatial information in an image, DEN estimates image depth using accurate radar points. Furthermore, to extract more comprehensive and fine-grained scene depth information, we present an innovative label optimization strategy that enhances label density and quality. DEN achieves an 18.78% reduction in Mean Absolute Error (MAE) and a 12.8% decrease in Root Mean Square Error (RMSE) for depth estimation. Additionally, it improves 3D object detection accuracy by 0.8% compared to the baseline model. Under low visibility conditions, DEN demonstrates a 6.7% reduction in MAE and a 9.6% reduction in RMSE compared to the baseline. These improvements demonstrated its robustness and enhanced performance under challenging conditions. © 2014 IEEE.

关键词： Mean square error

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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

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Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi directional optimization iot devices swarm deep learning

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A systematic review on lightweight security algorithms for a sustainable IoT infrastructure

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Sarker, Kamal Uddin Department of Computer Science and MIS Oman College of Management and Technology Barka Oman

Operational technology, industrial automation, advanced healthcare systems, and smart city infrastructures are common forms of IoT integrated distributed networks. Numerous IoT components require vast amounts of power for sensing, data extraction, processing, and sharing over the internet. Moreover, IoT devices are in operation with a lack of standards that poses a severe security threat. Intrusion detection systems and cryptographic algorithms are two important components in the security posture of a cyberspace. IoT applications provide real-time services, so security measures are active 24/7/365. Hence, these algorithms are also related to energy consumption. It is a study of security algorithms based on their hardware and software implementations. This review systematically searches for and selects the latest research documents, conducting an analysis of sustainability and security. It depicts the lightweight features of cryptographic algorithms;the relationship of gate density, chip area and power consumption in CMOS technology;and the importance of Machine Learning (MA) applications for IDS and cryptographic solutions in Next Generation IoT (NGIoT). © The Author(s) 2025.

关键词： Intrusion detection

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Classifying Multi-Lingual Reviews Sentiment Analysis in Arabic and English Languages Using the Stochastic Gradient Descent Model

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computers, Materials & Continua 2025年第4期83卷 1275-1290页

作者： Yasser Alharbi Sarwar Shah Khan College of Computer Science and Engineering University of HailHail55436Saudi Arabia Department of Computer Science University of Engineering&TechnologyMardan23200Pakistan

Sentiment analysis plays an important role in distilling and clarifying content from movie reviews,aiding the audience in understanding universal views towards the ***,the abundance of reviews and the risk of encountering spoilers pose challenges for efcient sentiment analysis,particularly in Arabic *** study proposed a Stochastic Gradient Descent(SGD)machine learning(ML)model tailored for sentiment analysis in Arabic and English movie *** allows for fexible model complexity adjustments,which can adapt well to the Involvement of Arabic language *** adaptability ensures that the model can capture the nuances and specifc local patterns of Arabic text,leading to better *** distinct language datasets were utilized,and extensive pre-processing steps were employed to optimize the datasets for *** proposed SGD model,designed to accommodate the nuances of each language,aims to surpass existing models in terms of accuracy and *** SGD model achieves an accuracy of 84.89 on the Arabic dataset and 87.44 on the English dataset,making it the top-performing model in terms of accuracy on both *** indicates that the SGD model consistently demonstrates high accuracy levels across Arabic and English *** study helps deepen the understanding of sentiments across various linguistic *** many studies that focus solely on movie reviews,the Arabic dataset utilized here includes hotel reviews,ofering a broader perspective.

关键词： Sentiment analysis stochastic gradient descent reviews English IMDb dataset Arabic dataset

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SmartZone: Runtime Support for Secure and Efficient On-device Inference on ARM TrustZone

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IEEE Transactions on computers 2025年第6期74卷 2144-2158页

作者： Jian, Zhaolong Liu, Xu Dong, Qiankun Cheng, Longkai Xie, Xueshuo Li, Tao Nankai University College of Computer Science Tianjin China Tianjin Key Laboratory of Network Tianjin China Data Science Technology Tianjin China Haihe Lab of ITAI Tianjin China

On-device inference is a burgeoning paradigm that performs model inference locally on end devices, allowing private data to remain local. ARM TrustZone as a widely supported trusted execution environment has been applied to provide confidentiality protection for on-device inference. However, with the rise of large-scale models like large language models (LLMs), TrustZone-based on-device inference faces challenges in migration difficulties and inefficient execution. The rudimentary TEE OS on TrustZone lacks both the inference runtime needed for building models and the parallel support necessary to accelerate inference. Moreover, the limited secure memory resources on end devices further constrain the model size and degrade performance. In this paper, we propose SmartZone to provide runtime support for secure and efficient on-device inference on TrustZone. SmartZone consists three main components: (1) a trusted inference-oriented operator set, providing the underlying mechanisms adapted to the TrustZone’s execution mode for trusted inference of DNN models and LLMs. (2) the proactive multi-threading parallel support, which increases the number of CPU cores in the secure state via cross-world thread collaboration to achieve parallelism, and (3) the on-demand secure memory management method, which statically allocates the appropriate secure memory size based on pre-execution resource analysis. We implement a prototype of SmartZone on the Raspberry Pi 3B+ board and evaluate it on four well-known DNN models and llama2 LLM. Extensive experimental results show that SmartZone provides end-to-end protection for on-device inference while maintaining excellent performance. Compared to the origin trusted inference, SmartZone accelerates the inference speed by up to 4.26× and reduces energy consumption by 65.81%. © 1968-2012 IEEE.

关键词： Memory management

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