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Decoding the MITRE Engenuity ATT&CK Enterprise Evaluation: An analysis of EDR Performance in Real-World Environments 19

Decoding the MITRE Engenuity ATT&CK Enterprise Evaluation: A...

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19th acm Asia Conference on Computer and Communications Security (acm AsiaCCS)

作者： Shen, Xiangmin Li, Zhenyuan Burleigh, Graham Wang, Lingzhi Chen, Yan Northwestern Univ Evanston IL 60208 USA Zhejiang Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9798400704826

Endpoint detection and response (EDR) systems have emerged as a critical component of enterprise security solutions, effectively combating endpoint threats like APT attacks with extended lifecycles. In light of the growing significance of endpoint detection and response (EDR) systems, many cybersecurity providers have developed their own proprietary EDR solutions. It's crucial for users to assess the capabilities of these detection engines to make informed decisions about which products to choose. This is especially urgent given the market's size, which is expected to reach around 3.7 billion dollars by 2023 and is still expanding. MITRE is a leading organization in cyber threat analysis. In 2018, MITRE started to conduct annual APT emulations that cover major EDR vendors worldwide. Indicators include telemetry, detection and blocking capability, etc. Nevertheless, the evaluation results published by MITRE don't contain any further interpretations or suggestions. In this paper, we thoroughly analyzed MITRE evaluation results to gain further insights into real-world EDR systems under test. Specifically, we designed a whole-graph analysis method, which utilizes additional control flow and data flow information to measure the performance of EDR systems. Besides, we analyze MITRE evaluation's results over multiple years from various aspects, including detection coverage, detection confidence, detection modifier, data source, compatibility, etc. Through the above studies, we have compiled a thorough summary of our findings and gained valuable insights from the evaluation results. We believe these summaries and insights can assist researchers, practitioners, and vendors in better understanding the strengths and limitations of mainstream EDR products.

关键词： EDR System Evaluation APT Emulation measurement Study

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Parallel-friendly Spatio-Temporal Graph Learning for Photovoltaic Degradation analysis at Scale 24

Parallel-friendly Spatio-Temporal Graph Learning for Photovo...

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33rd acm International Conference on Information and Knowledge Management (CIKM)

作者： Fan, Yangxin Wieser, Raymond Bruckman, Laura S. French, Roger H. Wu, Yinghui Case Western Reserve Univ Cleveland OH 44106 USA

ISBN: (纸本)9798400704369

Photovoltaic (PV) power stations have become an integral component to the global sustainable energy landscape. Accurately monitoring and estimating the performance of PV systems is critical to their feasibility for power generation and as a financial asset. One of the most challenging problems is to understand and estimate the long-term Performance Loss Rate (PLR) for large fleets of PV inverters. This paper introduces a novel Spatio-Temporal Graph Neural Network empowered, long-term Trend analysis system (ST-GTrend), to estimate PLR of PV systems at fleet-level. ST-GTrend nontrivially integrates spatio-temporal coherence and graph attention to separate PLR as a long-term "aging" trend from multiple fluctuation terms in the PV input data, with a design that can easily scale to large PV sets with effective, multi-level parallel computation. (1) To cope with diverse degradation patterns in time-series, ST-GTrend adopts a paralleled graph autoencoder array to extract aging and fluctuation terms simultaneously, and imposes flatness and smoothness regularizations to disentangle between aging and fluctuation. (2) For large PV systems, ST-GTrend enables a multi-level parallelization paradigm to scale the training and inference computation with a provable performance guarantee. ST-GTrend has been deployed in CRADLE, a scientific high performance computing infrastructure. We evaluated ST-GTrend with three real-world large-scale PV datasets, spanning a time period of 10 years. Our results show that ST-GTrend reduces MAPE and Euclidean distance-based errors on average by 34.74% and 33.66% of SOTA methods, and scales well to large PV sets. We also showcase that the advantages of ST-GTrend generalize for the need of long-term trend analysis in financial and economic data.

关键词： Graph Neural Networks Spatio-Temporal Data Trend analysis

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Assessing the GPU Offload Threshold of GEMM and GEMV Kernels on Modern Heterogeneous HPC systems

Assessing the GPU Offload Threshold of GEMM and GEMV Kernels...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Wilkinson, Finn Cockrean, Alex Lin, Wei-Chen Mcintosh-Smith, Simon Deakin, Tom University of Bristol High-Performance Computing Group Bristol United Kingdom

ISBN: (纸本)9798350355543

With an ever-growing compute advantage over CPUs, GPUs are often used in workloads with ample BLAS computation to improve performance. However, several factors including data-to-compute ratio, amount of data re-use, and data structure shape can all impact performance. Hence, using a GPU is not a guarantee of better BLAS performance. In this work, we introduce the GPU BLAS Offload Benchmark (GPU-BLOB), a novel and portable benchmark that measures CPU and GPU compute performance of different BLAS kernels and problem configurations. From the GPU offload threshold (a BLAS kernel's minimum dimensions for a certain configuration where using a GPU is guaranteed to yield improved performance), we evaluate the per-node performance of three, in-production, HPC systems. We show that the offload threshold for GEMM is highly dependant on problem shape and number of consecutive BLAS calls, and that, contrary to conventional wisdom, GEMV can benefit from GPU acceleration, especially on SoC-based systems. © 2024 IEEE.

关键词： AMD MI250X BLAS Heterogeneous High-Performance computing Intel Ponte Vecchio Nvidia Grace-Hopper Performance

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Mitigating synchronization bottlenecks in high-performance actor-model-based software

Mitigating synchronization bottlenecks in high-performance a...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Klenk, Kyle Moayeri, Mohammad Mahdi Guo, Junwei Clark, Martyn P. Spiteri, Raymond J. University of Saskatchewan Department of Computer Science Saskatoon Canada University of Calgary Department of Civil Engineering Calgary Canada

ISBN: (纸本)9798350355543

Bulk synchronous programming (in distributed-memory systems) and the fork-join pattern (in shared-memory systems) are often used for problems where independent processes must periodically synchronize. Frequent synchronization can greatly undermine the performance of software designed to solve such problems. We use the actor model of concurrent computing to balance the load of hundreds of thousands of short-lived tasks and mitigate synchronization bottlenecks by buffering communication via actor batching. The actor model is becoming increasingly popular in scientific and high-performance computing because it can handle heterogeneous tasks and computing environments with enhanced programming flexibility and ease relative to conventional paradigms like MPI. For a hydrologic simulation of continental North America with over 500,000 elements, the proposed buffering approach is approximately 4 times faster than no buffering, outperforms MPI on single and multiple nodes, and remains competitive with OpenMP on a single node and MPI+OpenMP on multiple nodes. © 2024 IEEE.

关键词： actor model of concurrent computing bulk synchronous programming fork-join pattern high-throughput computing scientific and high-performance computing

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BeamArmor5G: Demonstrating MIMO Anti-Jamming and Localization with srsRAN 5G Stack 30

BeamArmor5G: Demonstrating MIMO Anti-Jamming and Localizatio...

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30th International Conference on Mobile computing and Networking, acm MobiCom 2024

作者： Jonnavithula, Sesha Sai Rakesh Jain, Ish Kumar Bharadia, Dinesh University of California San Diego La Jolla CA United States Rensselaer Polytechnic Institute TroyNY United States

ISBN: (纸本)9798400704895

The rapid expansion of wireless technologies demands effective management of complex RAN for MIMO in 5G systems. Current architectures and RAN Intelligent Controllers (RICs) lack critical real-time data processing and physical layer functionalities, hindering MIMO advancements. We introduce BeamArmor5G, an open-source MIMO App development framework that addresses these gaps with comprehensive PHY layer capabilities, enabling detailed wireless channel measurement streaming from RAN to the controller. BeamArmor5G is implemented with srsRAN to empower open-source community researchers to advance beamforming, localization, and interference management, significantly enhancing network performance and enabling new 5G applications. © 2024 Copyright held by the owner/author(s).

关键词： 5G mobile communication systems

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SimEdge: Towards Accelerated Real-Time Augmented Reality Simulations Using Adaptive Smart Edge computing 23

SimEdge: Towards Accelerated Real-Time Augmented Reality Sim...

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26th acm International Conference on Modelling, analysis, and Simulation of Wireless and Mobile systems (MSWiM)

作者： Kaessinger, Johannes Troetsch, Heiko Duerr, Frank Edinger, Janick Univ Stuttgart IPVS Stuttgart Germany Univ Mannheim Mannheim Germany Univ Hamburg Hamburg Germany

ISBN: (纸本)9798400703669

Real-time simulation in augmented reality environments is an important area of research. The simulation model used in this paper simulates the musculoskeletal system, which has many potential applications in physiotherapy, medical education, and rehabilitation. To enable such real-time simulations in augmented reality, high-performance computing clusters are typically required, which can be costly and infeasible in terms of high network delay. This paper proposes SimEdge, a domain-specific edge computing system that enables real-time pervasive simulation in augmented reality environments on heterogeneous providers. This is done by smart offloading decisions based on a continuously updated list of resource providers. SimEdge builds on previous work using surrogate models and leverages scheduling improvements and context-aware data and task placement. Significant improvements in frame rate and input lag are achieved, as well as a reduction in energy consumption. The experimental results show that the proposed approach leads to an 8.3-fold increase in frame rate and a 65.14% reduction in input lag, as well as a 35.07% reduction in energy consumption compared to the local baseline system. These results demonstrate the effectiveness of the approach in improving efficiency and responsiveness.

关键词： edge computing scheduling neural networks pervasive computing mobile simulation

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Validation of an Instrument to Measure Self-Efficacy in Information Security 2024

Validation of an Instrument to Measure Self-Efficacy in Info...

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1st acm Virtual Global computing Education Conference V. 1, SIGCSE Virtual 2024

作者： Tise, Joseph C. McGill, Monica M. Institute for Advancing Computing Education WinchesterVA United States Institute for Advancing Computing Education PeoriaIL United States

ISBN: (纸本)9798400705984

Problem. Extant measures of students' cybersecurity self-efficacy lack sufficient evidence of validity based on internal structure. Such evidence of validity is needed to enhance confidence in conclusions drawn from use of self-efficacy measures in the cybersecurity domain. Research Question. To address this identified problem, we sought to answer our research question: What is the underlying factor structure of a new self-efficacy for Information Security measure? Method. We leveraged exploratory factor analysis (EFA) to determine the number of factors underlying a new measure of student self-efficacy to conduct information security. This measure was created to align with the five elements of the information security section of the K-12 cybersecurity Education framework. Participants were 190 undergraduate students recruited from computer science courses across the U.S. Findings. Results from the EFA indicated that a four-factor solution best fit the data while maximizing interpretability of the factors. The internal reliability of the measure was quite strong (α = .99). Implications. The psychometric quality of this measure was demonstrated, and thus evidence of validity based on internal structure has been established. Future work will conduct a confirmatory factor analysis (CFA) and assess measurement invariance across subgroups of interest (e.g., over- vs. under-represented race/ethnicity groups, gender). © 2024 acm.

关键词： Cybersecurity

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Serverless computing for Dynamic HPC Workflows

Serverless Computing for Dynamic HPC Workflows

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and analysis, SC Workshops 2024

作者： Thurimella, Vijay Raith, Philipp Hong Enriquez, Rolando P. Andrei Da Silva, Anderson Rattihalli, Gourav Gavrilovska, Ada Milojicic, Dejan Georgia Institute of Technology School of Computer Science Atlanta United States Hewlett Packard Labs Hewlett Packard Enterprise MilpitasCA United States

ISBN: (纸本)9798350355543

Containers have become an important component for scientific workflows, enhancing reproducibility, portability, and isolation when coupled with workflow management systems. However, integrating containers with these systems can be complex, potentially hindering wider adoption. Serverless platforms offer a solution by providing a layer of abstraction over container orchestrators, simplifying management while introducing event-driven capabilities. This paper presents a novel integration of serverless with workflow management systems to optimize scientific workflow execution. Our approach leverages serverless functions to dynamically provision containers for workflow tasks, resulting in up to 30% faster execution. We found that performance can be further improved by reusing containers between multiple different tasks that were provisioned by the serverless platform. These findings demonstrate the utility of combining specialized container orchestration with established workflow management to streamline scientific computing, improve resource utilization, and accelerate time-to-results. Serverless' event-driven architecture enables efficient resource scaling, aligning with the dynamic nature of scientific workloads. © 2024 IEEE.

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Blazing through Hard Drive Telemetry Data Streams with Parallel Processing 18

Blazing through Hard Drive Telemetry Data Streams with Paral...

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18th International Conference on Distributed and Event-Based systems (DEBS)

作者： Sarbu, Julia Iustina Onica, Emanuel Amariei, Ciprian Alexandru Ioan Cuza Univ Iasi Romania Testable Res Inc Delaware OH USA

ISBN: (纸本)9798400704437

The 2024 DEBS Grand Challenge addresses the topic of hard drive failure predictive maintenance, through analysis of data streams that contain SMART readings, reported by drives located in different groups of storage servers. This paper details the technical implementation of a solution that focuses primarily on parallelizing the data stream processing to obtain vertical scalability. When processing two queries concerning the addressed topic, and setting a threshold of a maximum 16 ms latency for responding, our solution obtained a throughput of about 57% out of the maximum possible when no processing is made on the data stream. We also describe an initial work-in-progress implementation of a distributed extension that relies on Apache Kafka, meant to further scale the throughput of the parallel solution and to address possible failure conditions of retrieving the input stream.

关键词： data stream processing parallel computing Apache Kafka

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AC-Cache: A Memory-Efficient Caching System for Small Objects via Exploiting Access Correlations 25

AC-Cache: A Memory-Efficient Caching System for Small Object...

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30th Symposium on Principles and Practice of Parallel Programming

作者： Nan, Fulin Wu, Ronglong Shen, Zhirong Yang, Jiahui Cheng, Li Chen, Zheng Zhang, Yiming Shu, Jiwu Xiamen Univ Xiamen Peoples R China Tencent Inc Shenzhen Peoples R China

ISBN: (纸本)9798400714436

In-memory key-value (KV) caching bridges the performance gap between high-performance networks and disk devices. However, prior in-memory KV caching systems either consider large objects or introduce additional memory overhead. In this paper, we conduct a systematic analysis over 56 production traces, and make three observations: (i) small objects dominate the traces and data accesses are highly skewed;(ii) the hotness of objects keeps stable across days;and (iii) the multi-get operation that retrieves multiple objects from the same node incurs much shorter tail latency than purely using the single-get operation. These observations motivate the design of AC-Cache, an access-correlation-aware in-memory caching system for small objects. AC-Cache comprises three design primitives: (i) we formulate the distribution of KV objects as an integer linear programming problem to balance data accesses and memory consumption;(ii) we capture the access correlation in a memory-efficient means and generate fine-grained correlation groups;and (iii) we formulate the distribution of the correlation groups as a maximum flow problem to balance data accesses, and leverage a heuristic algorithm to dispatch other KV objects to balance memory consumption. Extensive experiments with billions of objects on Alibaba Cloud show that AC-Cache can reduce the tail latency by 5.1-80.2% and increase the access throughput by 42.8-534.8%.

关键词： Caching Storage Load balance Correlation analysis

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