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Using KVM Events to Detect VM Memory-Sharing Lateral Movement Attacks in a Virtualized Environment

Using KVM Events to Detect VM Memory-Sharing Lateral Movemen...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Kun Zhang Junchao Ma Chen Li Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Virtual machine (VM) memory-sharing lateral movement attacks are becoming more advanced, while detection methods against them are still perceived as non-practical. Especially the current detection methods can't detect the VM escape attack. In this paper, we introduce a novel monitoring approach to detect VM memory-sharing lateral movement attacks operations inside a virtualization environment. We utilize the Kernel Virtual Machine (KVM) event sequence data in the kernel and process this data using a machine learning technique to identify any VM memory-sharing lateral movement attacks operations in the guest VM. Experimental results demonstrate that our method successfully separates the VM memory-sharing lateral movement attacks datasets on VMs from the non attacks datasets on VMs, on both trained and nontrained data scenarios. Besides, we also explain the classification results by extracting the set of most important features that separate both classes using their Fisher scores and variance and show that our detecting approach can work to detect VM memory-sharing lateral movement attacks in general. Finally, we evaluate the overhead impact of our VM memory-sharing lateral movement attacks detecting method and show that it has a negligible computation overhead on the host and the guest VM.

关键词： Cloud computing Virtual machine monitors System performance Feature extraction Virtual machines Servers Security Kernel Monitoring Optimization

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Depression Detection with EEG Based on Mutual Information Regularization

Depression Detection with EEG Based on Mutual Information Re...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Haoyu Lin Tianyuan Ma Chen Zhao Jun Qi Tingting Zhang Xiangzeng Kong School of Computer Science and Engineering University of New South Wales Sydney Australia School of Future Technology Fujian Agriculture and Forestry University Fuzhou China Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Depression is a kind of mental illness that is harmful to the development of society. Electroencephalography (EEG) is a promising tool in the area of auxiliary diagnosing diseases. In this paper, we develop a mutual information-based least absolute shrinkage and selection operator (MI-LASSO) model to learn representative features from the power spectral density (PSD) ratio extracted from data. Specifically, MI-LASSO adds an adaptive weight based on mutual information to LASSO, which can discriminate weights of different features. Following the feature selection accomplished by MI-LASSO, the feature set is input into the classifier. We design a stacking ensemble classifier composed of support vector machine (SVM), adaptive boosting (AdaBoost), random forest (RF), and K-nearest neighbor (KNN). Compared to independent classifiers, stacking has a stronger ability to recognize depression. The proposed framework is validated on the open datasets: MODMA and the dataset from Hospital Universiti Sains Malaysia (HUSM). The best classification accuracy on MODMA achieved 99.025%. The best classification accuracy on the second dataset achieved 99.06%. The results indicate that our framework outperforms other EEG-based methods in the identification of depression. We conducted several experiments whose results demonstrate our framework can effectively assist in the diagnosis of depression based on EEG.

关键词： Support vector machines Adaptation models Accuracy Stacking Nearest neighbor methods Depression Feature extraction Brain modeling Electroencephalography Mutual information

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The First international Workshop on Coarse-Grained Reconfigurable Architectures for High-Performance computing (CGRA4HPC)

Proceedings - 2022 IEEE 36th International Parallel and Dist...

引用

Proceedings - 2022 IEEE 36th international parallel and distributed Processing symposium Workshops, IPDPSW 2022 2022年 625-626页

作者： Podobas, Artur Sano, Kentaro Anderson, Jason KTH Royal Institute of Technology Sweden Riken Center for Computational Science Japan University of Toronto Canada

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LCOC: A Low-cost Optimizing Compiler for Large Scale Quantum Programs Towards Realistic Hardware

LCOC: A Low-cost Optimizing Compiler for Large Scale Quantum...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yu Zhu Jinchen Xu Qing Mu Hongru Yang Xiaodong Ding Qiming Du Zheng Shan Laboratory for Advanced Computing and Intelligence Engineering China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Quantum compilers play an essential role in the translation of quantum algorithms on quantum hardware. As the size of quantum programs increases, the problem of extreme compilation overhead becomes apparent. To address this problem, compilation frameworks with high-level intermediate representations such as QCOR have been introduced. However, these frameworks still face significant overhead from qubit mapping and optimizing sequence redundancy, resulting in challenges when compiling circuits to realistic hardware. In our paper, a low-cost optimizing compiler (LCOC) is proposed, which addresses the overhead in qubit mapping and enhanced the optimization capabilities. A qubit mapping optimizer based on the LLVM Polly is introduced in LCOC, which implements an efficient mapping of quantum programs containing specific loop types. While achieving low-cost mapping, it considerably reduces the depth of the circuit. We also implement an optimization sequence generator that leverages the modularity of LLVM and iterative compilation to provide LCOC with a more flexible and efficient optimization sequence. We conduct experiments with 100+ qubit circuits. Experiments show that LCOC is up to 1540× faster compared to Qiskit and 74× faster compared to QCOR in compile time. The average speedup relative to Qiskit and QCOR are 194× and 8.64×, respectively. In addition, it can effectively reduce the circuit depth by 50.32% compared to Qiskit and 83.5% compared to QCOR on average.

关键词： Translation Quantum algorithm Optimizing compilers Qubit Circuits Redundancy Hardware Generators Iterative methods Optimization

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FedHPM: Online Federated Learning via Heterogeneous Prediction Model

FedHPM: Online Federated Learning via Heterogeneous Predicti...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Zhi Zhang Shiwen Zhang Shuang Chen Wei Liang Hunan University of Science and Technology College of Computer Science and Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology and Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In the era of big data and growing privacy concerns, Federated Learning (FL) has emerged as a promising solution for collaborative model training while preserving user data privacy. However, FL faces challenges such as inconsistent data quality and frequent client dropouts, which hinder its performance. To address these challenges, we propose FedHPM, a novel framework that leverages the predictive capabilities of Long Short-Term Memory (LSTM) networks. FedHPM incorporates an intelligent client selection mechanism that utilizes LSTM-based analysis of historical data to forecast clients’ potential data quality and dropout likelihood. This mechanism enables efficient selection of the top best-performing clients for participation in each round of global model training. By mitigating the impact of dropouts and optimizing the composition of participating clients, FedHPM significantly improves both the convergence speed and final performance of the global model. Through extensive empirical evaluations, we demonstrate the superior performance of FedHPM in enhancing FL stability, accelerating the convergence rate, and maintaining model accuracy. The results highlight the effectiveness of our framework in addressing the challenges associated with data quality and dropouts, thereby advancing the field of federated learning.

关键词： Training Privacy Data privacy Accuracy Federated learning Data integrity Predictive models Data models Long short term memory Convergence

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MultiGrid on FPGA Using Data parallel C++

MultiGrid on FPGA Using Data Parallel C++

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Christopher Siefert Stephen L. Olivier Gwendolyn Voskuilen Jeffrey Young Sandia National Laboratories Albuquerque NM USA Georgia Institute of Technology Atlanta GA USA

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Centered on modern C++ and the SYCL standard for heterogeneous programming, Data parallel C++ (dpc++) and Intel's oneAPI software ecosystem aim to lower the barrier to entry for the use of accelerators like FPGAs in diverse applications. In this work, we consider the usage of FPGAs for scientific computing, in particular with a multigrid solver, MueLu. We report on early experiences implementing kernels of the solver in DPC++ for execution on Stratix 10 FPGAs, and we evaluate several algorithmic design and implementation choices. These choices not only impact performance, but also shed light on the capabilities and limitations of DPC++ and oneAPI.

关键词： distributed processing Scientific computing Ecosystems distributed databases C++ languages Programming Software

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Breast Cancer Classification Using DDPM and Holographic Guidance Diffusion Framework

Breast Cancer Classification Using DDPM and Holographic Guid...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yuxin You Hong-Seng Gan Akinobu Shimizu Muhammad Hanif Ramlee Hafiz Basarudin Viet-Vu Vu School of AI and Advanced Computing Xi’an Jiaotong – Liverpool University Suzhou China Institute of Engineering Tokyo University of Agriculture and Technology Tokyo Japan Department of Biomedical Engineering and Health Sciences Universiti Teknologi Malaysia Johor Bahru Malaysia Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Kajang Malaysia Faculty of Information Technology and Communications CMC University Hanoi Vietnam

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Breast cancer is a serious health threat to women. The early detection of breast cancer is crucial to improving survival rates and reducing the difficulty of treatment. In recent years, denoising diffusion probability models (DDPM) have performed well in image generation, but their application to breast cancer classification remains unknown. Additionally, conditional priors are often the most important source of information for diffusion models to solve problems, and fuzzy lesions or high-noise images pose a significant challenge for the model to extract accurate prior information. To address these issues, we propose a new classification model based on DDPM and combine it with the holographic guided diffusion framework (HGDF) to remove noise from images through specific guidance and make full use of HPC resources to improve the classification performance of breast cancer images. Results from the experiment indicate that the model has excellent accuracy in ultrasound images (BUS dataset) and mammography images (MAMMO dataset), with accuracy of 86.17 % ± 1.74% and 94.32% ± 2.31%, respectively.

关键词： Training Accuracy Ultrasonic imaging Computational modeling Noise reduction Noise Diffusion processes Breast cancer Lesions Medical diagnostic imaging

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LB-UNet: Multi-Scale Fusion Lightweight Network with Boundary Enhancement for Water Segmentation

LB-UNet: Multi-Scale Fusion Lightweight Network with Boundar...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Ning Li Libo Wang Gaochao Yang Baohua Yuan Bo Li Academy of Computing and Artificial Intelligence Changzhou University Changzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Water body extraction is a key technology in spatial geographic analysis. It plays an important role in disaster warning and surface navigation. At present, the semantic segmentation model of water body images generally has problems such as large scale changes, inaccurate edge description and noise interference. To address these limitations, we propose a new segmentation model, named Lightweight Boundary Enhancement U-Net (LB-UNet). Firstly, a Boundary Enhancement (BE) module enhanced by Butterworth high-pass filtering is constructed to enhance the edge contour features of the image and retain more boundary information. Secondly, a Multi-scale Fusion Connection Bridge (MFCB) module is designed for skip connection. Through multi-scale fusion, the feature information between the deep and shallow layers is fused with the mask, enriching the feature information of the shallow layer and reducing the loss of image feature information during encoder downsampling. In order to reduce the number of model parameters, residual connections and depth-separable convolution modules are introduced to construct a Lightweight Encoder-Decoder structure (LED). The model is trained on the inland waterway dataset and the remote sensing water body dataset. It achieves the best segmentation performance. On the inland waterway dataset, the IoU on the test set reached 95.69%, an increase of 13.33% compared with U-Net. On the remote sensing water body dataset, the IoU on the test set reached 90.78%, an increase of 10.39% compared with U-Net.

关键词： Image segmentation Navigation Image edge detection Semantic segmentation Noise Interference Light emitting diodes Information filters Rivers Remote sensing

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Efficient and Verifiable Skyline Computation on Blockchain System with Merkle B+ Tree Index

Efficient and Verifiable Skyline Computation on Blockchain S...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Hao Ding Xuefeng Piao Huihui Song Jiasi Li Zhenzhou Ji Meng Liu Jie Liu Wenjie Dong School of Computer Science and Engineering Harbin Institute of Technology Harbin China School of New Energy Harbin Institute of Technology Weihai China Electric Power Research Institute State Grid Shandong Electric Power Company Jinan China Yantai Power Supply Company State Grid Shandong Electric Power Company Yantai China Dongfang Electronics Co. Ltd. Yantai China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the advancement of blockchain technology, its application in data management and query processing has garnered increasing attention. However, as a distributed database system, blockchain currently falls short in supporting diverse data query requirements. Skyline computation, which identifies Pareto optimal solutions in multi-dimensional data, has become an increasingly necessary feature in blockchain environments. Traditional skyline computation methods struggle with inefficiency when handling large-scale and multi-dimensional data, and they lack effective verification mechanisms within a blockchain context. This paper proposes a multi-dimensional data index structure for skyline computation based on a hybrid blockchain architecture of full nodes and light nodes, utilizing Merkle tree and B+ tree. Additionally, an early pruning-based divide-and-conquer strategy is designed based on this index structure. This approach optimizes the skyline query process by computing local skylines to derive the global skyline. Simultaneously, by introducing Bloom filter, we reduce unnecessary I/O operations during result verification, enabling light nodes to perform verification operations more efficiently. Extensive experiments have demonstrated the effectiveness of our proposed scheme, which can improve query speed by an average of 90.08% compared to the BNL algorithm and by an average of 86.12% compared to the SFS algorithm.

关键词： distributed processing Query processing Computer architecture Pareto optimization Filtering algorithms Blockchains Computational efficiency Indexes

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A Novel DA-Based parallel Architecture for Inner-Product of Variable Vectors

A Novel DA-Based Parallel Architecture for Inner-Product of ...

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IEEE international symposium on Circuits and Systems (ISCAS)

作者： Anil Kali Samrat L. Sabat Pramod K. Meher CASEST University of Hyderabad Hyderabad India Department of Computer Science & Engineering C. V. Raman Global University Bhubaneswar India

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Computation of the inner products is frequently used in machine learning (ML) algorithms apart from signal processing and communication applications. distributed arithmetic (DA) has been frequently employed for area-time efficient inner-product implementations. In conventional DA-based architectures, one of the vectors is constant and known a priori. Hence, the traditional DA architectures are not suitable when both vectors are variable. However, computing the inner product of a pair of variable vectors is frequently used for matrix multiplication of various forms and convolutional neural networks. In this paper, we present a novel DA-based architecture for computing the inner product of variable vectors. To derive the proposed architecture, the inner product of any given length is decomposed into a set of short-length inner products, such that the inner product could be computed by successive accumulation of the results of short-length inner products. We have designed a DA-based architecture for the computation of the short-length inner-product of variable vectors and used that in successive clock cycles to compute the whole inner-product by successive accumulation. The post-layout synthesis results using Cadence Innovus with a GPDK 90nm technology library show that the proposed DA-based parallel architecture offers significant advantages in area-delay product and energy consumption over the bit-serial DA architecture.

关键词： Energy consumption Machine learning algorithms Architecture Signal processing algorithms Machine learning Signal processing Vectors

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