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IDSVAF: Neural Network Intrusion Detection System Vulnerability Assessment Framework

IDSVAF: Neural Network Intrusion Detection System Vulnerabil...

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international conference on Advances in Big Data, Computing and Data Communication systems (icABCD)

作者： Richard Farrell Dept. of Computer Science North Carolina A&T State University Greensboro NC United States of America

ISBN: (数字)9798350387902

ISBN: (纸本)9798350387919

Binary neural network intrusion detection systems are on-network Cybersecurity mechanisms that are not typically thought of as undergoing a vulnerability assessments themselves. A resultant model's validity is usually evaluated based on the neural network architectures ability to sufficiently generalize a dataset, determined via its reported accuracy and or F1-score. This work sought to demonstrate if the previously stated metrics are sufficient in communicating an anomaly detector's specific weaknesses, their baked-in vulnerabilities that can contribute to a Cybersecurity incident by not being able to detect a threat. The employed architecture used a convolutional neural network to generalize the encrypted network flow dataset USTC-TFC2016; it utilizes the error distribution of the confusion matrix for itemization of miss-characterized samples and calculates an aggregated prediction error weaknesses, the aPEW metric. The intrusion detection system vulnerability assessment framework (IDSVAF) is able to report out what samples are not detected in the remaining percentage of an accuracy or F1-Score metric, communicating a trained model's baked-in vulnerabilities to assist risk-based model selection.

关键词： measurement Training Accuracy Computational modeling Ecosystems Intrusion detection computer architecture

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Abstract Summarization Model Based on Semantic Graphs and Entity Pointers

Abstract Summarization Model Based on Semantic Graphs and En...

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Jiawei Zhao Changsheng Zhu School of Computer and Communication Lanzhou University of Technology Lanzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Despite significant advances in abstract summarization models based on pretrained language models, an unresolved issue is that the generated summaries are not always faithful to the input documents, meaning they are prone to factual inaccuracies. To address this problem, a fact-consistency enhancement model based on semantic graphs and entity pointer networks is proposed. This model assists enhances the summarization process by utilizing fine-grained semantic graphs constructed from triple extraction and dependency parsing tasks, reducing the occurrence of internal hallucination errors. Furthermore, the model introduces an entity pointer network to capture factual entities within the input text and further penalizes entity-level discrepancies between the generated summaries and the original texts through an entity consistency loss. Experiments conducted on the CNN/DM dataset show that the joint use of fine-grained semantic graphs and entity pointer networks effectively improves the factual consistency of the model-generated summaries compared to baseline models.

关键词： measurement Computational modeling Semantics Text summarization Signal processing

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Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions

Evaluating the Impact of Local Differential Privacy on Utili...

引用

international joint conference on Neural Networks (IJCNN)

作者： Alycia N. Carey Minh-Hao Van Xintao Wu Electrical Engineering and Computer Science University of Arkansas Fayetteville Arkansas

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

How to properly set the privacy parameter in differential privacy has been an open question in DP research since it was first proposed in 2006. In this work, we demonstrate the ability of influence functions to offer insight into how a specific privacy parameter value will affect a model’s test loss in the randomized response-based local DP setting. Our proposed method allows a data curator to select the privacy parameter best aligned with their allowed privacy-utility trade-off without requiring heavy computation such as extensive model retraining and data privatization. We consider multiple common randomization scenarios, such as performing randomized response over the features, and/or over the labels, as well as the more complex case of applying a class-dependent label noise correction method to offset the noise incurred by randomization. Through empirical evaluations we show that for both binary and multi-class settings, influence functions are able to approximate the true change in test loss that occurs when randomized response is applied over features and/or labels with small mean absolute error, especially in cases where noise correction methods are applied.

关键词： measurement Privacy Differential privacy Computational modeling Privatization Noise Neural networks

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modeling Memory Bandwidth Interference in Cloud Data Centers via Deep Learning

Modeling Memory Bandwidth Interference in Cloud Data Centers...

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international conference on computers, Communications, and systems (ICCCS)

作者： Jieqian Yao Jialun Li Xuan Mo Weigang Wu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

While large scale data centers have become more and more popular, performance interference among different workloads brings challenges to the guarantee of QoS. Although quite a number of studies have been made on handling such interference caused by resource sharing, there still lacks work on modeling the memory bandwidth interference which could obviously affect the workload performance. Due to the lack of isolation techniques on memory bandwidth and the dynamics of memory access patterns, workload interference on memory bandwidth is more complicated than the interference on other shared resources. In this paper, we propose a novel Memory Bandwidth Interference Model (MBIM) to estimate the interference of memory bandwidth in co-located cloud workloads. To capture the interference precisely, our MBIM harnesses the architectural structure of the workload deployment and utilizes micro-architectural metrics. Our MBIM consists of four parts: (1) Container static feature selection. (2) Dynamic load information integration. (3) Hierarchical information generation using multi-layer perceptron. (4) Memory bandwidth interference estimation model based on deep learning. Extensive experiments demonstrate that our MBIM can improve average accuracy by 14.9% in MSE, 10.7% in MAPE, and 6.7% in SMAPE of the estimated memory bandwidth interference. To the best of our knowledge, this is the first work of modeling memory bandwidth interference in cloud data centers.

关键词： measurement Deep learning Data centers Interference Bandwidth Quality of service Dynamic scheduling

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304415

The proceedings contain 77 papers. The special focus in this conference is on Parallel Processing and Applied Mathematics. The topics include: Neural Nets with a Newton Conjugate Gradient Method on Multiple GPUs;Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications;Cost and Performance Analysis of MPI-Based SaaS on the Private Cloud Infrastructure;building a Fine-Grained Analytical Performance Model for Complex Scientific Simulations;evaluation of Machine Learning Techniques for Predicting Run Times of Scientific Workflow Jobs;Smart Clustering of HPC Applications Using Similar Job Detection Methods;distributed Work Stealing in a Task-Based Dataflow Runtime;task Scheduler for Heterogeneous Data Centres Based on Deep Reinforcement Learning;Shisha: Online Scheduling of CNN Pipelines on Heterogeneous Architectures;General Framework for Deriving Reproducible Krylov Subspace Algorithms: BiCGStab Case;proactive Task Offloading for Load Balancing in Iterative Applications;language Agnostic Approach for Unification of Implementation Variants for Different Computing Devices;high Performance Dataframes from Parallel Processing Patterns;global Access to Legacy Data-Sets in Multi-cloud Applications with Onedata;MD-Bench: A Generic Proxy-App Toolbox for State-of-the-Art Molecular Dynamics Algorithms;Breaking Down the Parallel Performance of GROMACS, a High-Performance Molecular Dynamics Software;GPU-Based Molecular Dynamics of Turbulent Liquid Flows with OpenMM;a Novel Parallel Approach for modeling the Dynamics of Aerodynamically Interacting Particles in Turbulent Flows;reliable Energy measurement on Heterogeneous systems–on–Chip Based Environments;distributed Objective Function Evaluation for Optimization of Radiation Therapy Treatment Plans;a Generalized Parallel Prefix Sums Algorithm for Arbitrary Size Arrays;GPU4SNN: GPU-Based Acceleration for Spiking Neural Network Simulations;Ant System Inspired Heuristic Optimization of UAVs Depl

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Optimal Location of PMUs to Enhance State Estimation of Power systems

Optimal Location of PMUs to Enhance State Estimation of Powe...

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IEEE Jordan international joint conference on Electrical Engineering and Information Technology (JEEIT)

作者： Atallah Ahmad Al Qaralleh Khaled Alawasa Abdullah I. Al-Odienat Faculty of Engineering Mutah University Karak Jordan Department of Eletrical and Computer Engineering Sultan Qaboos University Muscat Oman Department of Renewable Energy Engineering Amman Arab University Amman Jordan

The Wide Area Monitoring systems (WAMs) has brought several advantages for the power system monitoring, operation, control. Recently, the introduction of Phasor measurement Units (PMUs) to power grids played a significant role in transforming them into smart grids. However, PMUs are costly and installing PMU at each bus is feasible. Therefore, the formulation of optimal placement of PMUs (OPP) in power systems is very important to minimize the total number, while preserving the observability of the system. Newton Raphson load flow method is applied to obtain load flow results of the system. Weighted least squares WLS with PMUs approaches for the state estimation of IEEE 14 bus test case is presented. The results show more accurate state estimation process.

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Digital Engineering Framework for Promoting Health and Human Performance with Immersive Technology

Digital Engineering Framework for Promoting Health and Human...

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Annual IEEE systems conference

作者： Gayathri Boopathy Emi Aoki Flore Norcéide Charles Thompson Erika Lewis Kavitha Chandra Department of Electrical and Computer Engineering Center for Advanced Computation and Telecommunications Department of Physical Therapy & Kinesiology UMass Movement Research Center University of Massachusetts Lowell Lowell USA

ISBN: (数字)9798331508180

ISBN: (纸本)9798331508197

The need for point-of-care health solutions and digital health interventions continues to increase, driven by an aging population and those displaced from the systemic impacts of environmental, infrastructural, and economic factors. These demographic and social shifts are placing unprecedented strain on the healthcare system. In response to these challenges, digital health solutions are emerging, although data indicates low confidence among both consumers and healthcare professionals due to a lack of regulatory oversight compared to traditional medical systems. This research explores the use of augmented reality (AR) technology in physical therapy and rehabilitation as a potential digital health intervention. The focus is on improving upper limb and hand mobility. This study employs a digital engineering design process in collaboration with physical therapists and clinical researchers. The goal is to utilize data from the AR sensors to capture quantitative information about a patient's therapeutic progress. The Digital Engineering framework presented can be applied for improving the validation and verification of technology-based digital health interventions, with the potential to enhance access to rehabilitation resources. Use-case and requirement diagrams are developed through close collaboration with physical therapy clinicians, to assess user engagement and therapeutic outcomes. Activity diagrams capture successive stages of improvement in the proposed system. The performance of the AR application in tracking joint angles and measuring mobility during prescribed gestures is evaluated against published normative values, highlighting the potential of this technology to improve upon manual measurement methods.

关键词： Hands Point of care Medical treatment Collaboration Manuals Electronic healthcare Sensors modeling Augmented reality Strain

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

14th International Conference on Parallel Processing and App...

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14th international conference on Parallel Processing and Applied Mathematics, PPAM 2022

ISBN: (纸本)9783031304446

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A Learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand

A Learning-based Controller for Multi-Contact Grasps on Unkn...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Dominik Winkelbauer Rudolph Triebel Berthold Bäuml DLR Institute of Robotics & Mechatronics Germany Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbitrary grasp types, including power grasps with multi-contacts, while operating self-contained on before unseen objects. No detailed contact information is needed, but only a rough 3D model, e.g., reconstructed from a single depth image. First, the external wrench being applied to the object is estimated by using the measured torques at the joints. Then, the torques necessary to counteract the estimated wrench while keeping the object at its initial pose are predicted. The torques are commanded via desired joint angles to an underlying joint-level impedance controller. To reach real-time performance, we propose a learning-based approach that is based on a wrench estimator- and a torque predictor neural network. Both networks are trained in a supervised fashion using data generated via the analytical formulation of the controller. In an extensive simulation-based evaluation, we show that our controller is able to keep 83.1% of the tested grasps stable when applying external wrenches with up to 10 N. At the same time, we outperform the two tested baselines by being more efficient and inducing less involuntary object movement. Finally, we show that the controller also works on the real DLR-Hand II, reaching a cycle time of 6 ms. Website: ***/grasping

关键词： Solid modeling Torque Three-dimensional displays Power measurement Neural networks Robot sensing systems Real-time systems Torque measurement Impedance Intelligent robots

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Communication-Constrained Multi-Robot Exploration with Intermittent Rendezvous

Communication-Constrained Multi-Robot Exploration with Inter...

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IEEE/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Alysson Ribeiro Da Silva Luiz Chaimowicz Thales C. Silva M. Ani Hsieh VeR-Lab Universidade Federal de Minas Gerais Brazil GRASP Laboratory University of Pennsylvania USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Communication constraints can significantly impact robots’ ability to share information, coordinate their movements, and synchronize their actions, thus limiting coordination in Multi-Robot Exploration (MRE) applications. In this work, we address these challenges by modeling the MRE application as a DEC-POMDP and designing a joint policy that follows a rendezvous plan. This policy allows robots to explore unknown environments while intermittently sharing maps opportunistically or at rendezvous locations without being constrained by joint path optimizations. To generate the rendezvous plan, robots represent the MRE task as an instance of the Job Shop Scheduling Problem (JSSP) and minimize JSSP metrics. They aim to reduce waiting times and increase connectivity, which correlates to the DEC-POMDP rewards and time to complete the task. Our simulation results suggest that our method is more efficient than using relays or maintaining intermittent communication with a base station, being a suitable approach for Multi-Robot Exploration. We developed a proof-of-concept using the Robot Operating System (ROS) that is available at: https://***/multirobotplayground/Noetic-Multi-Robot-Sandbox.

关键词： measurement Base stations Uncertainty Robot kinematics Simulation Relay networks Trajectory Synchronization Robots Optimization

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