The Experts below are selected from a list of 21051 Experts worldwide ranked by ideXlab platform

David Swanson - One of the best experts on this subject based on the ideXlab platform.

  • APRIL: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science
    IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, 2019
    Co-Authors: Deepak Nadig, Brian Bockelman, Byrav Ramamurthy, David Swanson
    Abstract:

    In this paper, we propose an Application-aware intelligent load balancing system for high-throughput, distributed computing, and data-intensive science workflows. We leverage emerging deep learning techniques for time-series modeling to develop an Application-aware predictive analytics system for accurately forecasting GridFTP connection loads. Our solution integrates with a major U.S. CMS Tier-2 site; we use a real dataset representing 670 million GridFTP transfer connections measured over 18 months to drive our predictive analytics solution. First, we perform extensive analysis on this dataset and use the connection loads as an example to study the temporal dependencies between various user-roles and workflow memberships. We use the analysis to motivate the design of a gated recurrent unit (GRU) based deep recurrent neural network (RNN) for modeling long-term temporal dependencies and predicting connection loads. We develop a novel Application-aware, predictive and intelligent load balancer, APRIL, that effectively integrates Application Metadata and load forecast information to maximize server utilization. We conduct extensive experiments to evaluate the performance of our deep RNN predictive analytics system and compare it with other approaches such as ARIMA and multi-layer perceptron (MLP) predictors. The results show that our forecasting model, depending on the user-role, performs between 5.88%-92.6% better than the alternatives. We also demonstrate the effectiveness of APRIL by comparing it with the load balancing capabilities of an existing production Linux Virtual Server (LVS) cluster. Our approach improves server utilization, on an average, between 0.5 to 11 times, when compared with its LVS counterpart.

  • identifying anomalies in gridftp transfers for data intensive science through Application awareness
    International Workshop on Security, 2018
    Co-Authors: Deepak Nadig, Brian Bockelman, Byrav Ramamurthy, David Swanson
    Abstract:

    Network anomaly detection systems can be used to identify anomalous transfers or threats, which, when undetected, can trigger large-scale malicious events. Data-intensive science projects rely on high-throughput computing and high-speed networking resources for data analysis and processing. In this paper, we propose an anomaly detection framework and architecture for identifying anomalies in GridFTP transfers. Application-awareness plays an important role in our proposed architecture and is used to communicate GridFTP Application Metadata to the machine learning and anomaly detection system. We demonstrate the effectiveness of our architecture by evaluating the framework with a real-world, large-scale dataset of GridFTP transfers. Preliminary results show that our framework can be used to develop novel anomaly detection services with diverse feature sets for distributed and data-intensive projects.

Deepak Nadig - One of the best experts on this subject based on the ideXlab platform.

  • APRIL: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science
    IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, 2019
    Co-Authors: Deepak Nadig, Brian Bockelman, Byrav Ramamurthy, David Swanson
    Abstract:

    In this paper, we propose an Application-aware intelligent load balancing system for high-throughput, distributed computing, and data-intensive science workflows. We leverage emerging deep learning techniques for time-series modeling to develop an Application-aware predictive analytics system for accurately forecasting GridFTP connection loads. Our solution integrates with a major U.S. CMS Tier-2 site; we use a real dataset representing 670 million GridFTP transfer connections measured over 18 months to drive our predictive analytics solution. First, we perform extensive analysis on this dataset and use the connection loads as an example to study the temporal dependencies between various user-roles and workflow memberships. We use the analysis to motivate the design of a gated recurrent unit (GRU) based deep recurrent neural network (RNN) for modeling long-term temporal dependencies and predicting connection loads. We develop a novel Application-aware, predictive and intelligent load balancer, APRIL, that effectively integrates Application Metadata and load forecast information to maximize server utilization. We conduct extensive experiments to evaluate the performance of our deep RNN predictive analytics system and compare it with other approaches such as ARIMA and multi-layer perceptron (MLP) predictors. The results show that our forecasting model, depending on the user-role, performs between 5.88%-92.6% better than the alternatives. We also demonstrate the effectiveness of APRIL by comparing it with the load balancing capabilities of an existing production Linux Virtual Server (LVS) cluster. Our approach improves server utilization, on an average, between 0.5 to 11 times, when compared with its LVS counterpart.

  • identifying anomalies in gridftp transfers for data intensive science through Application awareness
    International Workshop on Security, 2018
    Co-Authors: Deepak Nadig, Brian Bockelman, Byrav Ramamurthy, David Swanson
    Abstract:

    Network anomaly detection systems can be used to identify anomalous transfers or threats, which, when undetected, can trigger large-scale malicious events. Data-intensive science projects rely on high-throughput computing and high-speed networking resources for data analysis and processing. In this paper, we propose an anomaly detection framework and architecture for identifying anomalies in GridFTP transfers. Application-awareness plays an important role in our proposed architecture and is used to communicate GridFTP Application Metadata to the machine learning and anomaly detection system. We demonstrate the effectiveness of our architecture by evaluating the framework with a real-world, large-scale dataset of GridFTP transfers. Preliminary results show that our framework can be used to develop novel anomaly detection services with diverse feature sets for distributed and data-intensive projects.

Brian Bockelman - One of the best experts on this subject based on the ideXlab platform.

  • APRIL: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science
    IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, 2019
    Co-Authors: Deepak Nadig, Brian Bockelman, Byrav Ramamurthy, David Swanson
    Abstract:

    In this paper, we propose an Application-aware intelligent load balancing system for high-throughput, distributed computing, and data-intensive science workflows. We leverage emerging deep learning techniques for time-series modeling to develop an Application-aware predictive analytics system for accurately forecasting GridFTP connection loads. Our solution integrates with a major U.S. CMS Tier-2 site; we use a real dataset representing 670 million GridFTP transfer connections measured over 18 months to drive our predictive analytics solution. First, we perform extensive analysis on this dataset and use the connection loads as an example to study the temporal dependencies between various user-roles and workflow memberships. We use the analysis to motivate the design of a gated recurrent unit (GRU) based deep recurrent neural network (RNN) for modeling long-term temporal dependencies and predicting connection loads. We develop a novel Application-aware, predictive and intelligent load balancer, APRIL, that effectively integrates Application Metadata and load forecast information to maximize server utilization. We conduct extensive experiments to evaluate the performance of our deep RNN predictive analytics system and compare it with other approaches such as ARIMA and multi-layer perceptron (MLP) predictors. The results show that our forecasting model, depending on the user-role, performs between 5.88%-92.6% better than the alternatives. We also demonstrate the effectiveness of APRIL by comparing it with the load balancing capabilities of an existing production Linux Virtual Server (LVS) cluster. Our approach improves server utilization, on an average, between 0.5 to 11 times, when compared with its LVS counterpart.

  • identifying anomalies in gridftp transfers for data intensive science through Application awareness
    International Workshop on Security, 2018
    Co-Authors: Deepak Nadig, Brian Bockelman, Byrav Ramamurthy, David Swanson
    Abstract:

    Network anomaly detection systems can be used to identify anomalous transfers or threats, which, when undetected, can trigger large-scale malicious events. Data-intensive science projects rely on high-throughput computing and high-speed networking resources for data analysis and processing. In this paper, we propose an anomaly detection framework and architecture for identifying anomalies in GridFTP transfers. Application-awareness plays an important role in our proposed architecture and is used to communicate GridFTP Application Metadata to the machine learning and anomaly detection system. We demonstrate the effectiveness of our architecture by evaluating the framework with a real-world, large-scale dataset of GridFTP transfers. Preliminary results show that our framework can be used to develop novel anomaly detection services with diverse feature sets for distributed and data-intensive projects.

Byrav Ramamurthy - One of the best experts on this subject based on the ideXlab platform.

  • APRIL: An Application-Aware, Predictive and Intelligent Load Balancing Solution for Data-Intensive Science
    IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, 2019
    Co-Authors: Deepak Nadig, Brian Bockelman, Byrav Ramamurthy, David Swanson
    Abstract:

    In this paper, we propose an Application-aware intelligent load balancing system for high-throughput, distributed computing, and data-intensive science workflows. We leverage emerging deep learning techniques for time-series modeling to develop an Application-aware predictive analytics system for accurately forecasting GridFTP connection loads. Our solution integrates with a major U.S. CMS Tier-2 site; we use a real dataset representing 670 million GridFTP transfer connections measured over 18 months to drive our predictive analytics solution. First, we perform extensive analysis on this dataset and use the connection loads as an example to study the temporal dependencies between various user-roles and workflow memberships. We use the analysis to motivate the design of a gated recurrent unit (GRU) based deep recurrent neural network (RNN) for modeling long-term temporal dependencies and predicting connection loads. We develop a novel Application-aware, predictive and intelligent load balancer, APRIL, that effectively integrates Application Metadata and load forecast information to maximize server utilization. We conduct extensive experiments to evaluate the performance of our deep RNN predictive analytics system and compare it with other approaches such as ARIMA and multi-layer perceptron (MLP) predictors. The results show that our forecasting model, depending on the user-role, performs between 5.88%-92.6% better than the alternatives. We also demonstrate the effectiveness of APRIL by comparing it with the load balancing capabilities of an existing production Linux Virtual Server (LVS) cluster. Our approach improves server utilization, on an average, between 0.5 to 11 times, when compared with its LVS counterpart.

  • identifying anomalies in gridftp transfers for data intensive science through Application awareness
    International Workshop on Security, 2018
    Co-Authors: Deepak Nadig, Brian Bockelman, Byrav Ramamurthy, David Swanson
    Abstract:

    Network anomaly detection systems can be used to identify anomalous transfers or threats, which, when undetected, can trigger large-scale malicious events. Data-intensive science projects rely on high-throughput computing and high-speed networking resources for data analysis and processing. In this paper, we propose an anomaly detection framework and architecture for identifying anomalies in GridFTP transfers. Application-awareness plays an important role in our proposed architecture and is used to communicate GridFTP Application Metadata to the machine learning and anomaly detection system. We demonstrate the effectiveness of our architecture by evaluating the framework with a real-world, large-scale dataset of GridFTP transfers. Preliminary results show that our framework can be used to develop novel anomaly detection services with diverse feature sets for distributed and data-intensive projects.

Sen Sevil - One of the best experts on this subject based on the ideXlab platform.

  • Attention: there is an inconsistency between android permissions and Application Metadata!
    'Springer Science and Business Media LLC', 2020
    Co-Authors: Alecakir Huseyin, Can Burcu, Sen Sevil
    Abstract:

    This is an accepted manuscript of an article published by Springer in International Journal of Information Security on 07/01/2021, available online: https://doi.org/10.1007/s10207-020-00536-1 The accepted version of the publication may differ from the final published version.Since mobile Applications make our lives easier, there is a large number of mobile Applications customized for our needs in the Application markets. While the Application markets provide us a platform for downloading Applications, it is also used by malware developers in order to distribute their malicious Applications. In Android, permissions are used to prevent users from installing Applications that might violate the users’ privacy by raising their awareness. From the privacy and security point of view, if the functionality of Applications is given in sufficient detail in their descriptions, then the requirement of requested permissions could be well-understood. This is defined as description-to-permission fidelity in the literature. In this study, we propose two novel models that address the inconsistencies between the Application descriptions and the requested permissions. The proposed models are based on the current state-of-art neural architectures called attention mechanisms. Here, we aim to find the permission statement words or sentences in app descriptions by using the attention mechanism along with recurrent neural networks. The lack of such permission statements in Application descriptions creates a suspicion. Hence, the proposed approach could assist in static analysis techniques in order to find suspicious apps and to prioritize apps for more resource intensive analysis techniques. The experimental results show that the proposed approach achieves high accuracy.Published onlin

  • Discovering inconsistencies between requested permissions and Application Metadata by using deep learning
    ISCTurkey, 2020
    Co-Authors: Alecakir Huseyin, Kabukcu Muhammet, Can Buglalilar Burcu, Sen Sevil
    Abstract:

    This is an accepted manuscript of an article due to be published in the proceedings of ISCTurkey 2020. The accepted version of the publication may differ from the final published version.Android gives us opportunity to extract meaningful information from Metadata. From the security point of view, the missing important information in Metadata of an Application could be a sign of suspicious Application, which could be directed for extensive analysis. Especially the usage of dangerous permissions is expected to be explained in app descriptions. The permission-to-description fidelity problem in the literature aims to discover such inconsistencies between the usage of permissions and descriptions. This study proposes a new method based on natural language processing and recurrent neural networks. The effect of user reviews on finding such inconsistencies is also investigated in addition to Application descriptions. The experimental results show that high precision is obtained by the proposed solution, and the proposed method could be used for triage of Android Applications