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A Brain-inspired Approach for Malware Detection using Sub-semantic Hardware Features 23

A Brain-inspired Approach for Malware Detection using Sub-se...

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33rd Great Lakes Symposium on VLSI (GLSVLSI)

作者： Parsa, Maryam Khasawneh, Khaled N. Alouani, Ihsen George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA Queens Univ Belfast Ctr Secure Informat Technol CSIT Belfast Antrim North Ireland

ISBN: (纸本)9798400701252

Despite significant efforts to enhance the resilience of computer systems against malware attacks, the abundance of exploitable vulnerabilities remains a significant challenge. While preventing compromises is difficult, traditional signature-based static analysis techniques are susceptible to bypassing through metamorphic/polymorphic malware or zero-day exploits. Dynamic detection techniques, particularly those utilizing machine learning (ML), have the potential to identify previously unseen signatures by monitoring program behavior. However, classical ML models are power and resource intensive and may not be suitable for devices with limited budgets. This constraint creates a challenging tradeoff between security and resource utilization, which cannot be fully addressed through model compression and pruning. In contrast, neuromorphic architectures offer a promising solution for low-power brain-inspired systems. In this work, we explore the novel use of neuromorphic architectures for malware detection. We accomplish this by encoding sub-semantic micro-architecture level features in the spiking domain and proposing a Spiking Neural Network (SNN) architecture for hardware-aware malware detection. Our results demonstrate promising malware detection performance with an 89% F1-score. Ultimately, this work advocates that neuromorphic architectures, due to their low power consumption, represent a promising candidate for malware detection, especially for energyconstraint processors in IoT and Edge devices.

关键词： Neuromorphic computing Malware Detection Computer Security

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Intelligent Monitoring Algorithm for Transmission Lines Based on Lightning Fusion Technology 3

Intelligent Monitoring Algorithm for Transmission Lines Base...

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3rd International Conference on Data Analytics, computing and Artificial Intelligence, ICDACAI 2024

作者： Liu, Weigang Liu, Xinmin Chen, Dazhi Lv, Fubin Zhou, Hongze Liu, Yan State Grid Xinjiang Electric Power Co. Ltd. Changji Power Supply Company Changji Xinjiang831100 China

ISBN: (纸本)9798331519032

The traditional safety detection method for transmission lines heavily relies on manual inspection, which is not only inefficient but also limited by personnel experience, environmental factors, and inspection cycles. It is difficult to achieve real-time and accurate identification and warning of transmission line anomalies, and has significant limitations and safety hazards. In recent years, lightning fusion technology has emerged as an innovative solution that cleverly combines the high-precision ranging and speed measurement capabilities of advanced radar detection technology with the intuitive image recognition advantages of video surveillance technology. Through complex data processing algorithms, it achieves deep fusion and intelligent analysis of the two data. This article explores an intelligent monitoring algorithm for transmission lines based on lightning vision fusion technology, and proposes a monitoring system that combines deep learning (DL) algorithms to automatically capture and analyze environmental changes around transmission lines, significantly improving the timeliness and accuracy of transmission line monitoring. The experimental results show that this system not only significantly reduces manual intervention and operation costs, but also effectively avoids missed and false alarms, providing a more solid technical guarantee for the safe and stable operation of the power system (EPS). © 2024 IEEE.

关键词： Alarm systems

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A Study for Automating Signature Authentication systems 2

A Study for Automating Signature Authentication Systems

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Singh, Shalini Prasad, Gudari Sai Iniyan, S. Department of Computing Technologies SRM Institute of Science and Technology Tamilnadu Kaatankulathur India

ISBN: (纸本)9798350383867

Signature verification is a critical task in biometric authentication, offering widespread applications in banking, legal, and security domains. This research presents a comprehensive comparison between Convolutional Neural Networks (CNN) and Siamese Neural Networks (SNN) in the context of signature verification. We design and implement a CNN architecture employing RELU and Sigmoid activation functions across different models, incorporating layers such as Conv2D, MaxPooling2D, Flatten, Dense, and Dropout to process and classify signatures as genuine or forged. In parallel, we explore a Siamese Neural Network architecture, leveraging the unique property of learning from pairs of inputs to discern subtle differences in signatures. This network utilises Euclidean distance as a metric to compare feature vectors derived from signature images, encapsulating the essence of signature verification as a problem of similarity measurement. Our experimental setup involves a detailed training regime, where both networks are subjected to a dataset comprising genuine and forged signatures, aiming to learn distinguishing features. The performance of each model is evaluated based on accuracy, loss, and validation metrics across epochs, providing insights into their respective capabilities and limitations. The results indicate nuanced differences in how CNN and SNN approach the signature verification challenge, offering valuable perspectives for future research and application in automated signature authentication systems. Through this comparative study, we aim to contribute to the evolving landscape of biometric verification, providing a robust analysis that aids in selecting appropriate neural network architectures for signature verification tasks. ©2024 IEEE.

关键词： Convolutional neural networks

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A Hierarchical Deep Learning Approach for Predicting Job Queue Times in HPC systems 24

A Hierarchical Deep Learning Approach for Predicting Job Que...

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proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and analysis

作者： Austin Lovell Philip Wisniewski Sarah Rodenbeck Ashish Department of Computer Science Purdue University Indiana USA Rosen Center for Advanced Computing Purdue University Indiana USA

ISBN: (纸本)9798350355543

Accurate wait-time prediction for HPC jobs contributes to a positive user experience but has historically been a challenging task. Previous models lack the accuracy needed for confident predictions, and many were developed before the rise of deep *** this work, we investigate and develop TROUT, a neural network-based model to accurately predict wait times for jobs submitted to the Anvil HPC cluster. Data was taken from the Slurm Workload Manager on the cluster and transformed before performing additional feature engineering from jobs' priorities, partitions, and states. We developed a hierarchical model that classifies job queue times into bins before applying regression, outperforming traditional methods. The model was then integrated into a CLI tool for queue time prediction. This study explores which queue time prediction methods are most applicable for modern HPC systems and shows that deep learning-based prediction models are viable solutions.

关键词： High-performance computing

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2022 6th International Symposium on Instrumentation systems, Circuits and Transducers, INSCIT 2022 - proceedings

2022 6th International Symposium on Instrumentation Systems,...

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6th International Symposium on Instrumentation systems, Circuits and Transducers, INSCIT 2022

ISBN: (纸本)9781665471688

The proceedings contain 16 papers. The topics discussed include: application of wavelet transform for ultrasonic time of flight estimation;a study about solar tracker for the performance of photovoltaic plants;hybrid integrator topology with digital input control and analog signal processing;development of a baseband amplifier for a 130 nm CMOS process using open-source design flow;a novel approach to assessing crop health by electrical impedance spectroscopy;evaluation of a wireless power transfer system with Helmholtz and 3D coils for biomedical capsules;soft growing robot to enable monitoring applications in remote constrained environments;a performance analysis of reconstruction algorithms for an analog-to-information converter architecture;plant health evaluation based on bioimpedance phase measurement;new approach insulator failure differentiation based on image processing;and application of data fusion techniques and IoT tools in an ultrasonic level measurement system.

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Comparative performance analysis of low power SRAM cell architectures 1st

Comparative performance analysis of low power SRAM cell arch...

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1st International Conference on Applications of AI in 5G and IOT, ICAAI5GI 2024

作者： Kumar, Mukesh Jindal, Kanika Shukla, Pavan Kumar Noida Institute of Engineering and Technology Uttar Pradesh Gr. Noida India Noida Institute of Engineering and Technology Uttar Pradesh Gr. Noida India Noida Institute of Engineering and Technology Uttar Pradesh Gr. Noida India

ISBN: (纸本)9781032874036

This paper presents a detailed study on several low power Static Random-Access Memory (SRAM) cell configurations. Architectures of interest include studies on the performance parameters of power consumption, stability, area efficiency, and speed. SRAM cells have been instrumental in the quest for energy-efficient designs tailored to portable and high-performance computing systems. This is because, compared to other forms of memory, they conduct access operations much faster and have control mechanisms that are easy to comprehend. Optimization of the reduction in power consumption of SRAM cells, while other or more parameters for optimal performance are maintained or increased, remains a continuous challenge. This paper presents a review of the latest developments in the area of low-power SRAM cell designs, covering the conventional 6T, 8T, and 10T cells to some recent ones like adiabatic cells, FinFET-based cells, and SOI technology cells. The study uses a set of measurement benchmarks and simulation tools to evaluate and compare the performance of these cells. © 2025 the Author(s).

关键词： Static random access storage

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Demand analysis and research value of cervical curvature measurement

Demand analysis and research value of cervical curvature mea...

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2022 International Conference on Biomedical and Intelligent systems, IC-BIS 2022

作者： Liu, Haifeng Mo, Zijun Huang, Keyi Chen, Jiyun Huang, Yucheng Lin, Gang Lin, Yimin Zhuhai College of Science and Technology Zhuhai519040 China

ISBN: (纸本)9781510660212

With the change in people's lifestyles and the popularity of smartphones, more and more people suffer from cervical spondylosis. The change in cervical curvature is closely related to cervical spondylosis. This paper compares the existing cervical curvature measurement techniques and publishes a questionnaire through the network to explore the application of cervical curvature measurement technology in daily life, people's attention to the health of the cervical vertebra, and the willingness to consume related products. The survey results show that the existing cervical curvature measurement technology still need to be improved, people pay more attention to the health of the cervical vertebra and have a strong willingness to consume products such as cervical curvature measuring instruments. Finally, it is concluded that the research on the measurement technology of cervical curvature is of great significance at both the medical level and the health level of the whole people. © 2022 SPIE. All rights reserved.

关键词： Spine Analytical research Medical imaging Image processing computing systems

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Can You See Me Now? A measurement Study of Zoom, Webex, and Meet 21

Can You See Me Now? A Measurement Study of Zoom, Webex, and ...

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21s acm Internet measurement Conference (IMC)

作者： Chang, Hyunseok Varvello, Matteo Hao, Fang Mukherjee, Sarit Nokia Bell Labs Murray Hill NJ 07974 USA

ISBN: (纸本)9781450391290

Since the outbreak of the COVID-19 pandemic, videoconferencing has become the default mode of communication in our daily lives at homes, workplaces and schools, and it is likely to remain an important part of our lives in the post-pandemic world. Despite its significance, there has not been any systematic study characterizing the user-perceived performance of existing videoconferencing systems other than anecdotal reports. In this paper, we present a detailed measurement study that compares three major videoconferencing systems: Zoom, Webex and Google Meet. Our study is based on 48 hours' worth of more than 700 videoconferencing sessions, which were created with a mix of emulated videoconferencing clients deployed in the cloud, as well as real mobile devices running from a residential network. We find that the existing videoconferencing systems vary in terms of geographic scope, which in turns determines streaming lag experienced by users. We also observe that streaming rate can change under different conditions (e.g., number of users in a session, mobile device status, etc), which affects user-perceived streaming quality. Beyond these findings, our measurement methodology can enable reproducible benchmark analysis for any types of comparative or longitudinal study on available videoconferencing systems.

关键词： COVID-19

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Chartist: Task-driven Eye Movement Control for Chart Reading 25

Chartist: Task-driven Eye Movement Control for Chart Reading

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2025 CHI Conference on Human Factors in computing systems, CHI 2025

作者： Shi, Danqing Wang, Yao Bai, Yunpeng Bulling, Andreas Oulasvirta, Antti Computational Behavior Lab Aalto University Helsinki Finland Institute for Visualisation and Interactive Systems University of Stuttgart Stuttgart Germany Synteraction Lab National University of Singapore Singapore Singapore

ISBN: (纸本)9798400713941

To design data visualizations that are easy to comprehend, we need to understand how people with different interests read them. Computational models of predicting scanpaths on charts could complement empirical studies by offering estimates of user performance inexpensively;however, previous models have been limited to gaze patterns and overlooked the effects of tasks. Here, we contribute Chartist, a computational model that simulates how users move their eyes to extract information from the chart in order to perform analysis tasks, including value retrieval, filtering, and finding extremes. The novel contribution lies in a two-level hierarchical control architecture. At the high level, the model uses LLMs to comprehend the information gained so far and applies this representation to select a goal for the lower-level controllers, which, in turn, move the eyes in accordance with a sampling policy learned via reinforcement learning. The model is capable of predicting human-like task-driven scanpaths across various tasks. It can be applied in fields such as explainable AI, visualization design evaluation, and optimization. While it displays limitations in terms of generalizability and accuracy, it takes modeling in a promising direction, toward understanding human behaviors in interacting with charts. © 2025 Copyright held by the owner/author(s).

关键词： Reinforcement learning

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Vendor-neutral and Production-grade Job Power Management in High Performance computing 24

Vendor-neutral and Production-grade Job Power Management in ...

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proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and analysis

作者： Naman Kulshreshtha Tapasya Patki Jim Garlick Mark Grondona Rong Ge Clemson University Clemson USA Lawrence Livermore National Laboratory

ISBN: (纸本)9798350355543

Power management and energy efficiency are critical research areas for exascale computing and beyond, necessitating reliable telemetry and control for distributed systems. Despite this need, existing approaches present several limitations precluding their adoption in production. These limitations include, but are not limited to, lack of portability due to vendor-specific and closed-source solutions, lack of support for non-MPI applications, and lack of user-level *** present a job-level power management framework based on Flux. We introduce flux-power-monitor and demonstrate its effectiveness on the Lassen (IBM Power AC922) and Tioga (HPE Cray EX235A) systems with a low average overhead of 0.4%. We also present flux-power-manager, where we discuss a proportional sharing policy and introduce a hierarchical FFT-based dynamic power management algorithm (FPP). We demonstrate that FPP reduces energy by 1% compared to proportional sharing, and by 20% compared to the default IBM static power capping policy.

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